Amazon Bedrock Integrations

Prefactor is a cutting-edge platform designed for real-time assessment, monitoring, and reliability of production AI agents. It evaluates each execution instantly based on metrics such as quality, drift, cost, and data risk, seamlessly integrating these assessments into actionable responses to ensure that any failing agent is detected in real time rather than merely reflected on a post-execution dashboard. Teams are equipped to monitor every model invocation, tool usage, and decision-making process through structured traces and spans, allowing them to conduct evaluations using LLM-as-judge, technical assessments, qualitative analyses, and custom metrics at every phase of the process. Additionally, context can be incorporated from various sources, including GitHub, Linear, Jira, databases, and internal APIs, serving as ground truth for evaluations. When a run exceeds predefined limits, Prefactor is capable of blocking or throttling it, pausing sensitive actions, or routing the decision to a person for approval, modification, or rejection prior to execution, with meticulous logging of each choice made. The command-line interface allows for the discovery of agents without the need for platform migration, while the TypeScript and Python SDKs ensure seamless integration with LangChain, Claude, Vercel AI, OpenClaw, and LiveKit, enhancing the overall functionality and adaptability of the platform. This comprehensive approach not only optimizes agent performance but also fosters collaboration among teams by providing clear visibility and control over the AI processes.

Cloptima is an innovative platform that integrates AI and cloud FinOps, offering governance for LLM expenditures, insights into multicloud costs, optimization for Kubernetes, analysis of queries, and controls on engineering costs within a unified framework. Through its AI gateway, teams can securely utilize their own credentials from OpenAI, Anthropic, Gemini, Vertex AI, and Amazon Bedrock, applying a range of protections like encrypted controls, virtual keys, model policies, token limits, budgets, guardrails, and attribution before any requests are sent to the providers. The platform's spend analytics provide a comprehensive breakdown of usage categorized by provider, model, team, application, environment, user, agent session, tool, workflow, and other dimensions, while the agent controls monitor retries, loops, tool interactions, and the potential for runaway costs. Additionally, exact and semantic response caching can help minimize redundant usage, whereas intelligent routing capabilities allow for the redirection of eligible traffic to more cost-effective or faster models, with the option for canary rollout and rollback if there are regressions in quality, latency, or error rates. This holistic approach ensures that organizations can effectively manage their AI-related expenditures while maximizing efficiency and performance across their operations.

AICosts.ai serves as a comprehensive platform for managing AI-related expenses, consolidating billing and usage information from over 50 different providers into a single dashboard. Users can easily upload invoices and data exports in various formats such as PDF, CSV, or JSON, or they can utilize the developer API to send usage events, with the platform efficiently parsing this information into a standardized format without needing any proxy setups or alterations to production requests. It accommodates a wide array of services including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Vertex AI, Cohere, Groq, Hugging Face, Pinecone, RunwayML, Make, Zapier, and n8n. Daily insights break down expenditures by platform, model, and billed units, which encompass tokens, operations, characters, and other specific metrics from providers, enabling users to compare different services and understand the origins of their charges. Additionally, users can set budgets that may either encompass the entire AI landscape or focus on specific platforms or features, while also receiving email notifications whenever their rolling 30-day expenses surpass predetermined thresholds, ensuring they stay informed and within their financial limits. This level of detail and control empowers teams to manage their AI costs more effectively.

FlowRunner stands out as a visual workflow automation platform that integrates native AI agent orchestration, allowing agents to execute workflows independently while pausing for necessary human input. What sets FlowRunner apart is its unique approach to oversight; while most platforms treat approval as a workflow step, FlowRunner empowers agents to reach out to humans as callable tools during execution. It effectively gathers the necessary context and options, then communicates with a designated individual via email, Slack, WhatsApp, or phone, resuming the workflow once a decision is made. Notably, a run can remain on hold for up to a year, ensuring that time-sensitive decisions, like those requiring three days, do not have a deadline. In addition to this, features like audit trails, role-based access control (RBAC), and service level agreement (SLA) tracking are available starting from the mid-tier package, rather than being confined to custom enterprise agreements, and a business associate agreement (BAA) can also be acquired. Every subscription level allows for unlimited users, unlimited workflows, and full access to the integration catalog, with no limitations based on user count or connectors. Billing is structured around the number of workflow runs rather than the individual action steps taken, ensuring a transparent pricing model. Agents are also able to utilize their own AI provider keys, with no additional charges for inference. Furthermore, users can choose between cloud-hosted and self-hosted options, providing flexibility to fit various operational needs.

Switch is a human and AI agent collaboration platform from Flint AI that brings autonomous agents into the communication tools teams already use. It provides shared rooms where employees and multiple AI agents can coordinate work, exchange information, make decisions, and maintain a common history of activity. Instead of keeping knowledge isolated within individual agents, Switch maintains relevant context in the room so it remains available when work is handed between people and agents. Agents can research topics, create designs, analyze technical requirements, write code, coordinate other agents, and report completed work within the same collaborative environment. Switch integrates with messaging platforms including Slack, Microsoft Teams, Discord, and Mattermost. It supports agent and AI technologies including Claude Code, Claude, OpenAI, LangChain, LangGraph, Amazon Bedrock, Vertex AI, ADK, and custom agents. Organizations can coordinate agents built with different frameworks or providers through a shared workspace rather than standardizing on a single agent platform. This approach allows existing human communication workflows to become coordination environments for agentic work without requiring teams to adopt an entirely new collaboration interface. Switch is designed for organizations building human-agent teams that need persistent context, agent interoperability, collaborative task execution, and visibility into work performed by AI agents.

Cloudgeni serves as an intelligent AIOps platform tailored for cloud infrastructure, adept at analyzing incidents, compliance, drift, and financial operations issues, subsequently rectifying them through validated Infrastructure-as-Code pull requests. It consolidates cloud states, Infrastructure-as-Code, and operational signals into a unified context layer, enabling agents to grasp dependencies, pinpoint root causes, implement changes aligned with the organization's standards, validate these changes prior to deployment, and confirm the resolution of issues post-implementation. The platform's agents are equipped to handle various tasks, including compliance remediation, managing configuration drift, resource imports, DevOps activities, pull request evaluations, Infrastructure-as-Code pipelines, cost management, Site Reliability Engineering workflows, AI infrastructure governance, and bespoke automation solutions. Additionally, Cloudgeni seamlessly integrates with a multitude of platforms and tools such as AWS, Azure, GCP, OCI, Kubernetes, OpenShift, Terraform, OpenTofu, Terragrunt, Bicep, Pulumi, Helm, GitHub, GitLab, Azure DevOps, security instruments, observability solutions, and internal knowledge repositories, fostering a comprehensive ecosystem for cloud management. This versatility ensures that organizations can maintain optimal performance and security across their cloud environments while streamlining their operational processes.

Rune is an efficient, keyboard-centric integrated development environment designed for advanced users, seamlessly integrating code, terminals, command-line tools, language intelligence, debugging capabilities, and AI agents within a flexible, multi-workspace framework. Adhering to the Unix philosophy, it ensures that robust development resources are readily accessible, avoiding the need for a mouse-based workflow. The IDE features three distinct editing modes: a standard editor aimed at users familiar with VS Code, Cursor, or Sublime; a modal editing option for Vim and Neovim enthusiasts; and an Emacs-style interface, with consistent keybindings across the platform. Workspaces are designed to amalgamate files, terminals, tasks, debugging tools, search functionalities, and language intelligence, capable of operating both locally and on remote servers. Rune Agent operates within the same workspace as the code, enabling it to read and modify files, search through the codebase, execute commands, utilize various tools, and engage in comprehensive dialogues. Moreover, it offers support for a range of AI models including OpenAI, Codex, Anthropic, Claude, Gemini, Amazon Bedrock, local models, and custom OpenAI-compatible endpoints, enhancing its versatility for developers. This combination of features makes Rune a powerful ally for programmers looking to streamline their development processes.

Holo4 is H Company's series of generalist computer-use and agentic AI models built to perform multi-step work across software interfaces. It is available as Holo4 27B, a dense 27-billion-parameter model, and Holo4 35B-A3B, a Mixture-of-Experts model containing 35 billion total parameters with 3 billion active. Holo4 can interact with applications by clicking and typing through graphical interfaces, writing and executing code, or calling MCP and API tools. The same model can operate across desktops, websites, Android devices, code sandboxes, and business APIs without requiring developers to select a separate specialized model for each environment. H Company trained Holo4 using 127 billion supervised fine-tuning tokens, with approximately three-quarters consisting of successful agentic trajectories spanning desktop, web, MCP/API, and mobile tasks. Reinforcement learning then trained separate experts for desktop and web interaction and for terminal, MCP, and API work before merging them into a single model. Holo4 27B scored 85.2% on OSWorld, 61.7% on OSWorld 2.0, 45.4% on AutomationBench, and 85.1% on AndroidWorld in the evaluations reported by H Company. The models support a 256K context window, with the 27B model positioned for greater accuracy on long multi-step tasks and the 35B-A3B version positioned as a faster and less expensive alternative. Holo4 is available through a hosted API and downloadable model weights, enabling developers and enterprises to build agents that perform workflows spanning multiple applications and interaction methods.

Teneo.AI is a market-leading agentic AI platform built to fully automate enterprise customer service. It delivers intelligent AI agents that manage voice and digital interactions with up to 99% resolution accuracy. Designed for speed, Teneo enables organizations to launch pilots in weeks and reach production rapidly. The platform supports omnichannel engagement across voice, chat, apps, and email from a single environment. Voice AI capabilities allow enterprises to handle millions of interactions every month without service disruption. Teneo integrates easily with existing contact center and enterprise systems through open APIs. Built-in analytics provide actionable insights to continuously optimize performance. Enterprise-grade security and governance ensure compliance with internal and regulatory requirements. Organizations achieve significant cost savings while improving first-contact resolution and CSAT. Teneo helps enterprises progress from automation to fully agentless customer service operations.

Stable Diffusion is a generative image model family from Stability AI designed to help users create high-quality images across many styles and use cases. The models can generate photography, 3D visuals, paintings, line art, illustrations, product concepts, branded assets, and other creative outputs from text prompts. Stable Diffusion is built for strong prompt following, giving users more control over the final image and making it useful for detailed creative direction. The model family includes options optimized for professional image quality, faster generation, and customization on consumer hardware. Users can deploy Stable Diffusion through a self-hosted license, integrate it through the Stability AI API, access it through cloud partners, or use it in web-based creative tools. Stability AI also offers image editing APIs and tools for editing uploaded or generated images. These tools support object erasing, inpainting, outpainting, upscaling, sketch-based generation, structural control, and style control. Stable Diffusion can support workflows such as brand style creation, product photography, concept art, marketing visuals, app experiences, creative tools, and enterprise image generation. By combining flexible deployment, image generation, editing, and customization, Stable Diffusion gives teams a powerful foundation for building and scaling AI-powered visual creation.

We focus on creating and executing solutions that leverage collective intelligence and augmented technology. Stability AI is dedicated to developing open AI tools that enable us to unlock our full potential. Our team consists of passionate builders who are genuinely concerned about the real-world impact of our work. Significant progress often arises from collaboration across various teams, where we embrace the challenge of questioning established norms and fostering creativity. Our core motivation lies in producing groundbreaking ideas and transforming them into practical solutions. We prioritize innovation over tradition, believing that our diverse perspectives strengthen our approach. By valuing these differences, we aim to find common ground and harness the power of varied viewpoints to drive our mission forward. Ultimately, our commitment to collaboration and creativity cultivates an environment where transformative ideas can thrive.

Openlayer is the enterprise AI governance platform for discovering, testing, monitoring, securing, and governing AI systems from development through production. Openlayer brings AI evaluation, observability, guardrails, compliance, gateway enforcement, and cost controls into one platform.

Introducing the next iteration of our open-source large language model, this version features model weights along with initial code for the pretrained and fine-tuned Llama language models, which span from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been developed using an impressive 2 trillion tokens and offer double the context length compared to their predecessor, Llama 1. Furthermore, the fine-tuned models have been enhanced through the analysis of over 1 million human annotations. Llama 2 demonstrates superior performance against various other open-source language models across multiple external benchmarks, excelling in areas such as reasoning, coding capabilities, proficiency, and knowledge assessments. For its training, Llama 2 utilized publicly accessible online data sources, while the fine-tuned variant, Llama-2-chat, incorporates publicly available instruction datasets along with the aforementioned extensive human annotations. Our initiative enjoys strong support from a diverse array of global stakeholders who are enthusiastic about our open approach to AI, including companies that have provided valuable early feedback and are eager to collaborate using Llama 2. The excitement surrounding Llama 2 signifies a pivotal shift in how AI can be developed and utilized collectively.

LMOps is a stack that allows you to launch production-ready applications for monitoring, model management and more. Portkey is a replacement for OpenAI or any other provider APIs. Portkey allows you to manage engines, parameters and versions. Switch, upgrade, and test models with confidence. View aggregate metrics for your app and users to optimize usage and API costs Protect your user data from malicious attacks and accidental exposure. Receive proactive alerts if things go wrong. Test your models in real-world conditions and deploy the best performers. We have been building apps on top of LLM's APIs for over 2 1/2 years. While building a PoC only took a weekend, bringing it to production and managing it was a hassle! We built Portkey to help you successfully deploy large language models APIs into your applications. We're happy to help you, regardless of whether or not you try Portkey!

Pixtral Large is an expansive multimodal model featuring 124 billion parameters, crafted by Mistral AI and enhancing their previous Mistral Large 2 framework. This model combines a 123-billion-parameter multimodal decoder with a 1-billion-parameter vision encoder, allowing it to excel in the interpretation of various content types, including documents, charts, and natural images, all while retaining superior text comprehension abilities. With the capability to manage a context window of 128,000 tokens, Pixtral Large can efficiently analyze at least 30 high-resolution images at once. It has achieved remarkable results on benchmarks like MathVista, DocVQA, and VQAv2, outpacing competitors such as GPT-4o and Gemini-1.5 Pro. Available for research and educational purposes under the Mistral Research License, it also has a Mistral Commercial License for business applications. This versatility makes Pixtral Large a valuable tool for both academic research and commercial innovations.

Transitioning from development to production, as well as from traditional data engineering to artificial intelligence, requires securing the various environments, pipelines, tools, and open-source components integral to your data and AI supply chain. It is essential to continuously identify, prevent, and rectify security and compliance vulnerabilities in AI before they reach production. In addition, monitoring AI applications in real-time allows for the detection and mitigation of adversarial AI attacks while enforcing specific application guardrails. Noma integrates smoothly across your data and AI supply chain and applications, providing a detailed map of all data pipelines, notebooks, MLOps tools, open-source AI elements, and both first- and third-party models along with datasets, thereby automatically generating a thorough AI/ML bill of materials (BOM). Additionally, Noma constantly identifies and offers actionable solutions for security issues, including misconfigurations, AI-related vulnerabilities, and non-compliant training data usage throughout your data and AI supply chain. This proactive approach enables organizations to enhance their AI security posture effectively, ensuring that potential threats are addressed before they can impact production. Ultimately, adopting such measures not only fortifies security but also boosts overall confidence in AI systems.

SaaSConstruct is an all-in-one template that accelerates the creation of SaaS applications on the AWS cloud platform. It features a complete, pre-configured codebase that integrates cloud infrastructure alongside both frontend and backend elements, facilitating the quick deployment of SaaS websites with essential functionalities like user authentication, payment processing, and customizable AI chatbots. Built upon AWS services, the infrastructure allows for easy scalability and integration with other AWS tools as required. By providing these ready-to-use components, SaaSConstruct enables developers to efficiently test and launch their projects, allowing them to concentrate on core features rather than getting bogged down by initial setup tasks. This template includes code for the majority of standard SaaS processes such as payments and authentication, significantly reducing your AWS expenses during the development phase. Additionally, it supports the integration of AI models like those from AWS Bedrock. With SaaSConstruct, you can save countless hours of development time, ensuring you have everything necessary to launch your SaaS product today while maintaining a focus on innovation.

Celonis offers an AI Development platform that empowers businesses to create and implement sophisticated AI copilots, assistants, and agents, all based on a digital replica of their operational processes. These Process Copilots function as generative AI chatbots tailored for particular applications, delivering rapid insights into processes through an easy-to-use chat interface. They are capable of addressing urgent inquiries regarding metrics and workflows, producing visual data representations such as charts and tables, and pinpointing areas that require enhancement in key performance indicators. This innovative method allows organizations to efficiently expand their process insights, which in turn improves decision-making and boosts operational efficiency. Additionally, the annotation builder feature enables users to organize data to extract valuable insights from various sources, including emails and service tickets, utilizing generative AI techniques. For instance, it can categorize customer service orders and evaluate the risks associated with moving materials between plants, thereby facilitating AI assistants that offer actionable recommendations for user consideration. Ultimately, this platform not only streamlines operations but also fosters a culture of data-driven decision-making within enterprises.

Kong AI Gateway serves as a sophisticated semantic AI gateway that manages and secures traffic from Large Language Models (LLMs), facilitating the rapid integration of Generative AI (GenAI) through innovative semantic AI plugins. This platform empowers users to seamlessly integrate, secure, and monitor widely-used LLMs while enhancing AI interactions with features like semantic caching and robust security protocols. Additionally, it introduces advanced prompt engineering techniques to ensure compliance and governance are maintained. Developers benefit from the simplicity of adapting their existing AI applications with just a single line of code, which significantly streamlines the migration process. Furthermore, Kong AI Gateway provides no-code AI integrations, enabling users to transform and enrich API responses effortlessly through declarative configurations. By establishing advanced prompt security measures, it determines acceptable behaviors and facilitates the creation of optimized prompts using AI templates that are compatible with OpenAI's interface. This powerful combination of features positions Kong AI Gateway as an essential tool for organizations looking to harness the full potential of AI technology.

Created by LM-SYS, RouteLLM is a publicly available toolkit that enables users to direct tasks among various large language models to enhance resource management and efficiency. It features strategy-driven routing, which assists developers in optimizing speed, precision, and expenses by dynamically choosing the most suitable model for each specific input. This innovative approach not only streamlines workflows but also enhances the overall performance of language model applications.

Orq.ai stands out as the leading platform tailored for software teams to effectively manage agentic AI systems on a large scale. It allows you to refine prompts, implement various use cases, and track performance meticulously, ensuring no blind spots and eliminating the need for vibe checks. Users can test different prompts and LLM settings prior to launching them into production. Furthermore, it provides the capability to assess agentic AI systems within offline environments. The platform enables the deployment of GenAI features to designated user groups, all while maintaining robust guardrails, prioritizing data privacy, and utilizing advanced RAG pipelines. It also offers the ability to visualize all agent-triggered events, facilitating rapid debugging. Users gain detailed oversight of costs, latency, and overall performance. Additionally, you can connect with your preferred AI models or even integrate your own. Orq.ai accelerates workflow efficiency with readily available components specifically designed for agentic AI systems. It centralizes the management of essential phases in the LLM application lifecycle within a single platform. With options for self-hosted or hybrid deployment, it ensures compliance with SOC 2 and GDPR standards, thereby providing enterprise-level security. This comprehensive approach not only streamlines operations but also empowers teams to innovate and adapt swiftly in a dynamic technological landscape.

CAMB.AI's MARS6 represents a revolutionary advancement in text-to-speech (TTS) technology, making it the first speech model available on the Amazon Web Services (AWS) Bedrock platform. This integration empowers developers to weave sophisticated TTS functionalities into their generative AI projects, paving the way for the development of more dynamic voice assistants, captivating audiobooks, interactive media, and a variety of audio-driven experiences. With its cutting-edge algorithms, MARS6 delivers natural and expressive speech synthesis, establishing a new benchmark for TTS conversion quality. Developers can conveniently access MARS6 via the Amazon Bedrock platform, which promotes effortless integration into their applications, thereby enhancing user engagement and accessibility. The addition of MARS6 to AWS Bedrock's extensive array of foundational models highlights CAMB.AI's dedication to pushing the boundaries of machine learning and artificial intelligence. By providing developers with essential tools to craft immersive audio experiences, CAMB.AI is not only facilitating innovation but also ensuring that these advancements are built on AWS's trusted and scalable infrastructure. This synergy between advanced TTS technology and cloud capabilities is poised to transform how users interact with audio content across diverse platforms.

Vertesia serves as a comprehensive, low-code platform for generative AI that empowers enterprise teams to swiftly design, implement, and manage GenAI applications and agents on a large scale. Tailored for both business users and IT professionals, it facilitates a seamless development process, enabling a transition from initial prototype to final production without the need for lengthy timelines or cumbersome infrastructure. The platform accommodates a variety of generative AI models from top inference providers, granting users flexibility and reducing the risk of vendor lock-in. Additionally, Vertesia's agentic retrieval-augmented generation (RAG) pipeline boosts the precision and efficiency of generative AI by automating the content preparation process, which encompasses advanced document processing and semantic chunking techniques. With robust enterprise-level security measures, adherence to SOC2 compliance, and compatibility with major cloud services like AWS, GCP, and Azure, Vertesia guarantees safe and scalable deployment solutions. By simplifying the complexities of AI application development, Vertesia significantly accelerates the path to innovation for organizations looking to harness the power of generative AI.

WebOrion Protector Plus is an advanced firewall powered by GPU technology, specifically designed to safeguard generative AI applications with essential mission-critical protection. It delivers real-time defenses against emerging threats, including prompt injection attacks, sensitive data leaks, and content hallucinations. Among its notable features are defenses against prompt injection, protection of intellectual property and personally identifiable information (PII) from unauthorized access, and content moderation to ensure that responses from large language models (LLMs) are both accurate and relevant. Additionally, it implements user input rate limiting to reduce the risk of security vulnerabilities and excessive resource consumption. Central to its robust capabilities is ShieldPrompt, an intricate defense mechanism that incorporates context evaluation through LLM analysis of user prompts, employs canary checks by integrating deceptive prompts to identify possible data breaches, and prevents jailbreak attempts by utilizing Byte Pair Encoding (BPE) tokenization combined with adaptive dropout techniques. This comprehensive approach not only fortifies security but also enhances the overall reliability and integrity of generative AI systems.

Amazon Bedrock AgentCore allows for the secure deployment and management of advanced AI agents at scale, featuring infrastructure specifically designed for dynamic agent workloads, robust tools for agent enhancement, and vital controls for real-world applications. It is compatible with any framework and foundation model, whether within or outside of Amazon Bedrock, thus eliminating the burdensome need for specialized infrastructure. AgentCore ensures complete session isolation and offers industry-leading support for prolonged workloads lasting up to eight hours, with seamless integration into existing identity providers for smooth authentication and permission management. Additionally, a gateway is utilized to convert APIs into tools that are ready for agents with minimal coding required, while built-in memory preserves context throughout interactions. Furthermore, agents benefit from a secure browser environment that facilitates complex web-based tasks and a sandboxed code interpreter, which is ideal for functions such as creating visualizations, enhancing their overall capability. This combination of features significantly streamlines the development process, making it easier for organizations to leverage AI technology effectively.

The AGAT Secure AI Platform is an AI solution that prioritizes security, offering robust generative AI features tailored for enterprise needs while maintaining strict data protection and governance standards. It can be deployed in various settings, including on-premises environments that are completely isolated, or through cloud services, facilitating scenarios where data exposure is minimized and enabling comprehensive control for enterprises. This platform includes two key components: an AI Suite and an AI Firewall. The AI Suite provides a private AI ecosystem featuring various modules, such as a knowledge assistant that retrieves answers from internal data, a data-analysis agent that performs natural language analytics on spreadsheets and databases, a smart search tool for meaning-based content discovery, an AI code assistant for code completion, generation, and error detection, as well as AI agents capable of planning and executing tasks through file modifications and internet searches. Meanwhile, the AI Firewall functions as a real-time proxy for public AI services, implementing risk-based policies and additional security measures, thus ensuring that organizations can leverage AI safely while adhering to their governance frameworks. Overall, AGAT Secure AI Platform stands out for its commitment to security without sacrificing the innovative capabilities of generative AI.

FinOps LLM serves as an advanced platform for AI cost management and observability, specifically designed for engineering teams utilizing production GenAI. It enables transparency in token expenditures across a variety of providers such as OpenAI, Anthropic, Amazon Bedrock, Google Gemini, Azure, and Groq, while also aligning internal usage data with invoices from these providers. Users can filter token-level expenses based on provider, model, feature, team, customer, environment, and other custom metrics, ensuring that each dollar spent has a designated owner. Additionally, the platform includes attribution and chargeback functionalities that correlate usage with product interfaces and customer demographics, facilitating showback processes and allowing for data exports to systems like NetSuite, QuickBooks, CSV, or through APIs. Furthermore, real-time anomaly detection features track spending, latency, and quality, comparing them against dynamic feature baselines, and issue alerts via Slack, PagerDuty, email, or webhooks whenever notable changes occur. To further enhance cost control, optional budget enforcement and auto-throttling measures can prevent excessive spending due to runaway agents, excessive retries, or unexpected model shifts. This comprehensive approach ensures that engineering teams can manage their AI resources effectively while maintaining financial oversight.

telemetry.dev serves as an observability platform that is fully integrated with OpenTelemetry, specifically designed for AI agents and applications involving large language models. It enables developers to monitor various elements such as model calls, tool steps, retrievals, logs, and metrics, while also allowing them to analyze issues relating to failures, latency, token consumption, and projected expenses. Additionally, users can draw comparisons across different models, providers, and environments to gain deeper insights into performance. The platform features native support for TypeScript and Python SDKs, along with integrations for various providers and frameworks, as well as standard OTLP/HTTP ingestion methods. To enhance data privacy and security, it incorporates environment-specific capture controls, SDK masking, and server-side redaction, ensuring that teams can effectively manage the sensitive content associated with prompts, responses, and telemetry data. This comprehensive functionality enables organizations to optimize their AI applications and ensure robust observability across their operations.

Galileo is an AI observability and eval engineering platform designed to help teams evaluate, monitor, guardrail, and improve AI agents and applications. The platform connects the offline testing process with production governance, turning evals into guardrails that can control agent actions, tool access, escalation paths, and safety behavior. Galileo helps teams capture ground truth from synthetic data, development data, live production data, and subject matter expert annotations. Its evaluation capabilities include RAG evals, agent evals, safety evals, security evals, and custom evals that can be tuned to specific environments. Galileo’s Luna models distill optimized LLM-as-judge evaluators into compact models that run at lower cost and latency for production-scale monitoring. The insights engine analyzes traces, prompts, functions, context, datasets, models, and agent behavior to identify failure modes and recommend fixes. Teams can use Galileo to detect hallucinations, tool-selection failures, drift, bias, unsafe outputs, and other reliability issues before they harm production experiences. Deployment options include SaaS, virtual private cloud, and on-premises environments. By combining observability, eval engineering, production guardrails, ground-truth datasets, Luna models, insights, and enterprise deployment options, Galileo helps organizations build more reliable AI systems.

Leverage the capabilities of Qlik® Staige™ to transform AI into a tangible reality by establishing a reliable data infrastructure, incorporating automation, generating actionable predictions, and creating a significant impact across your organization. AI transcends mere experiments and initiatives; it represents a comprehensive ecosystem filled with files, scripts, and outcomes. Regardless of where you allocate your resources, we have collaborated with premier sources to provide integrations that enhance efficiency, facilitate management, and ensure quality assurance. Streamline the process of delivering real-time data to AWS data warehouses or data lakes, making it readily available through a well-governed catalog. Our latest partnership with Amazon Bedrock allows for seamless connections to essential large language models (LLMs) such as A21 Labs, Amazon Titan, Anthropic, Cohere, and Meta. This smooth integration with Amazon Bedrock not only simplifies access for AWS customers but also empowers them to harness large language models alongside analytics, resulting in insightful, AI-driven conclusions. By utilizing these advancements, organizations can fully unlock their data's potential in innovative ways.

The sole auto-pilot tool designed specifically for your distinctive software development process is Anycode AI. This innovative solution transforms your entire legacy codebase into contemporary technology stacks at a speed up to eight times quicker than traditional methods. With Anycode AI, you can enhance your coding efficiency by ten times, leveraging AI for quick and compliant coding and testing. Experience rapid modernization with Anycode AI, which allows you to manage legacy code effortlessly while adapting to updates for streamlined applications. Transition from outdated systems with ease, as our platform optimizes old logic to ensure a seamless shift to cutting-edge technologies. Embrace the future of coding and testing with Anycode AI, and watch your productivity soar.

Unlock the advantages of generative AI for your business while minimizing associated risks. Ensure safe organizational use of AI through enhanced visibility and effective remediation, all while utilizing your current security framework. Maintain awareness of your AI landscape by obtaining a full inventory of all generative AI applications within your organization. Effectively manage AI-related risks by identifying which applications have the capacity to store and learn from your data, as well as understanding the connections between various data types and language models. With Aim, you can track AI adoption trends over time and gain crucial insights that are vital for business operations. Aim equips organizations to harness public generative AI technology securely, revealing hidden shadow AI tools and their potential risks while implementing real-time data protection strategies. By securing your internal language model deployments, Aim enhances the productivity of AI copilots, addressing misconfigurations, identifying threats, and strengthening trust boundaries for a safer AI environment. This approach fosters a culture of innovation while ensuring that your organization remains protected in an evolving digital landscape.

Embark on an accelerated journey toward adopting GenAI technologies within your organization. With just a few clicks, you can set up GenAI assistants that utilize your company’s data. Implement GenAI assistants equipped with security measures, fostering trust, compliance, and effective cost management. Explore the ways your teams are harnessing AI-driven assistants to gain valuable insights from data. Identify new opportunities to enhance the value derived from these technologies. Empower your GenAI applications through leading Large Language Models (LLMs). Establish seamless connections with premier GenAI model providers like Google, Amazon, Mistral, and Azure. Utilize secure GenAI features on your marketing communications site to effectively respond to inquiries from the press, analysts, and customers. Swiftly create and deploy GenAI assistants on web platforms, ensuring they deliver quick, accurate, and policy-compliant responses based on your public content. Additionally, harness secure GenAI capabilities to provide prompt and accurate answers to legal policy inquiries posed by your staff, enhancing overall efficiency and clarity. By integrating these solutions, you can significantly improve the support provided to both employees and clients alike.

Mirascope is an innovative open-source library designed on Pydantic 2.0, aimed at providing a clean and highly extensible experience for prompt management and the development of applications utilizing LLMs. This robust library is both powerful and user-friendly, streamlining interactions with LLMs through a cohesive interface that is compatible with a range of providers such as OpenAI, Anthropic, Mistral, Gemini, Groq, Cohere, LiteLLM, Azure AI, Gemini Enterprise Agent Platform, and Bedrock. Whether your focus is on generating text, extracting structured data, or building sophisticated AI-driven agent systems, Mirascope equips you with essential tools to enhance your development workflow and create impactful, resilient applications. Additionally, Mirascope features response models that enable you to effectively structure and validate output from LLMs, ensuring that the responses meet specific formatting requirements or include necessary fields. This capability not only enhances the reliability of the output but also contributes to the overall quality and precision of the application you are developing.

Amazon Nova represents an advanced generation of foundation models (FMs) that offer cutting-edge intelligence and exceptional price-performance ratios, and it is exclusively accessible through Amazon Bedrock. The lineup includes three distinct models: Amazon Nova Micro, Amazon Nova Lite, and Amazon Nova Pro, each designed to process inputs in text, image, or video form and produce text-based outputs. These models cater to various operational needs, providing diverse options in terms of capability, accuracy, speed, and cost efficiency. Specifically, Amazon Nova Micro is tailored for text-only applications, ensuring the quickest response times at minimal expense. In contrast, Amazon Nova Lite serves as a budget-friendly multimodal solution that excels at swiftly handling image, video, and text inputs. On the other hand, Amazon Nova Pro boasts superior capabilities, offering an optimal blend of accuracy, speed, and cost-effectiveness suitable for an array of tasks, including video summarization, Q&A, and mathematical computations. With its exceptional performance and affordability, Amazon Nova Pro stands out as an attractive choice for nearly any application.

Amazon Nova Canvas is an advanced image generation tool that produces high-quality images based on textual descriptions or images supplied as prompts. In addition to its impressive generation capabilities, Amazon Nova Canvas includes user-friendly features for image editing through text commands, options for modifying color palettes and layouts, and integrated safety measures to ensure responsible AI usage. This combination of functionalities makes it a versatile choice for both professional and creative users.

Amazon Nova Reel represents a cutting-edge advancement in video generation technology, enabling users to effortlessly produce high-quality videos from text and images. This innovative model utilizes natural language prompts to manipulate various elements such as visual style and pacing, incorporating features like camera motion adjustments. Additionally, it includes integrated controls designed to promote the safe and ethical application of artificial intelligence in video creation, ensuring users can harness its full potential responsibly.

PartyRock is an innovative platform that allows individuals to create AI-driven applications within a dynamic environment supported by Amazon Bedrock. This engaging space offers a quick and enjoyable introduction to generative AI. Introduced by Amazon Web Services (AWS) in November 2023, PartyRock caters to users of all skill levels, enabling them to design applications powered by generative AI without requiring any programming knowledge. Users can simply articulate their app ideas to develop a wide range of applications, from basic text generators to advanced productivity tools that leverage various AI features. Since its launch, the platform has seen the creation of over 500,000 applications by users around the globe. Functioning as a playground, PartyRock utilizes Amazon Bedrock, AWS's comprehensive service that grants access to essential AI models. Additionally, the platform features a web-based interface that removes the necessity for an AWS account, allowing users to log in using their existing social media credentials. Moreover, users have the opportunity to browse through hundreds of thousands of published applications, organized by their respective functionalities, further enhancing their creative possibilities. This makes PartyRock an exciting and accessible option for anyone interested in exploring the potential of generative AI.

Langflow serves as a low-code AI development platform that enables the creation of applications utilizing agentic capabilities and retrieval-augmented generation. With its intuitive visual interface, developers can easily assemble intricate AI workflows using drag-and-drop components, which streamlines the process of experimentation and prototyping. Being Python-based and independent of any specific model, API, or database, it allows for effortless integration with a wide array of tools and technology stacks. Langflow is versatile enough to support the creation of intelligent chatbots, document processing systems, and multi-agent frameworks. It comes equipped with features such as dynamic input variables, fine-tuning options, and the flexibility to design custom components tailored to specific needs. Moreover, Langflow connects seamlessly with various services, including Cohere, Bing, Anthropic, HuggingFace, OpenAI, and Pinecone, among others. Developers have the option to work with pre-existing components or write their own code, thus enhancing the adaptability of AI application development. The platform additionally includes a free cloud service, making it convenient for users to quickly deploy and test their projects, fostering innovation and rapid iteration in AI solutions. As a result, Langflow stands out as a comprehensive tool for anyone looking to leverage AI technology efficiently.

Sema4.ai enables business users to create and manage enterprise AI agents on a large scale, allowing them to perceive, act, and learn in ways that were once thought impossible. These advanced enterprise AI agents are innovative applications capable of handling intricate tasks with remarkable precision and efficiency. While they are powered by large language models, their abilities extend far beyond mere text processing. Designed to be user-friendly, agents can be trained using simple English, which allows even those without technical expertise to develop and oversee them. Additionally, these agents can interpret both documents and images, enhancing their functionality. Operating continuously, they autonomously seek out and execute tasks around the clock. A notable application is in invoice reconciliation, a vital financial procedure that guarantees prompt and accurate vendor payments. Our enterprise AI agents optimize this process by automating the complete workflow, which not only reduces costs but also reinforces financial oversight. By automating these tasks, a finance agent liberates time for finance professionals, enabling them to focus on more strategic management activities. This transformative approach not only enhances operational efficiency but also elevates the overall effectiveness of financial teams within organizations.

Amazon SageMaker Unified Studio provides a seamless and integrated environment for data teams to manage AI and machine learning projects from start to finish. It combines the power of AWS’s analytics tools—like Amazon Athena, Redshift, and Glue—with machine learning workflows, enabling users to build, train, and deploy models more effectively. The platform supports collaborative project work, secure data sharing, and access to Amazon’s AI services for generative AI app development. With built-in tools for model training, inference, and evaluation, SageMaker Unified Studio accelerates the AI development lifecycle.

Aurascape is a cutting-edge security platform tailored for the AI era, empowering businesses to innovate securely amidst the rapid advancements of artificial intelligence. It offers an all-encompassing view of interactions between AI applications, effectively protecting against potential data breaches and threats driven by AI technologies. Among its standout features are the ability to oversee AI activity across a wide range of applications, safeguarding sensitive information to meet compliance standards, defending against zero-day vulnerabilities, enabling the secure implementation of AI copilots, establishing guardrails for coding assistants, and streamlining AI security workflows through automation. The core mission of Aurascape is to foster a confident adoption of AI tools within organizations while ensuring strong security protocols are in place. As AI applications evolve, their interactions become increasingly dynamic, real-time, and autonomous, necessitating robust protective measures. By preempting emerging threats, safeguarding data with exceptional accuracy, and enhancing team productivity, Aurascape also monitors unauthorized app usage, identifies risky authentication practices, and curtails unsafe data sharing. This comprehensive security approach not only mitigates risks but also empowers organizations to fully leverage the potential of AI technologies.

Amazon Nova Micro is an advanced text-only AI model optimized for rapid language processing at a very low cost. With capabilities in reasoning, translation, and code completion, it offers over 200 tokens per second in response generation, making it suitable for fast-paced, real-time applications. Nova Micro supports fine-tuning with text inputs, and its efficiency in understanding and generating text makes it a cost-effective solution for AI-driven applications requiring high performance and quick outputs.

Amazon Nova Lite is a versatile AI model that supports multimodal inputs, including text, image, and video, and provides lightning-fast processing. It offers a great balance of speed, accuracy, and affordability, making it ideal for applications that need high throughput, such as customer engagement and content creation. With support for fine-tuning and real-time responsiveness, Nova Lite delivers high-quality outputs with minimal latency, empowering businesses to innovate at scale.

Amazon Nova Pro is a high-performance multimodal AI model that combines top-tier accuracy with fast processing and cost efficiency. It is perfect for use cases like video summarization, complex Q&A, code development, and executing multi-step AI workflows. Nova Pro supports text, image, and video inputs, allowing businesses to enhance customer interactions, content creation, and data analysis with AI. Its ability to perform well on industry benchmarks makes it suitable for enterprises aiming to streamline operations and drive automation.

Amazon Nova Sonic is an advanced speech-to-speech model that offers real-time, lifelike voice interactions while maintaining exceptional price efficiency. By integrating speech comprehension and generation into one cohesive model, it allows developers to craft engaging and fluid conversational AI solutions with minimal delay. This system fine-tunes its replies by analyzing the prosody of the input speech, including elements like rhythm and tone, which leads to more authentic conversations. Additionally, Nova Sonic features function calling and agentic workflows that facilitate interactions with external services and APIs, utilizing knowledge grounding with enterprise data through Retrieval-Augmented Generation (RAG). Its powerful speech understanding capabilities encompass both American and British English across a variety of speaking styles and acoustic environments, with plans to incorporate more languages in the near future. Notably, Nova Sonic manages interruptions from users seamlessly while preserving the context of the conversation, demonstrating its resilience against background noise interference and enhancing the overall user experience. This technology represents a significant leap forward in conversational AI, ensuring that interactions are not only efficient but also genuinely engaging.

Amazon Nova Premier is a cutting-edge model released as part of the Amazon Bedrock family, designed for tackling sophisticated tasks with unmatched efficiency. With the ability to process text, images, and video, it is ideal for complex workflows that require deep contextual understanding and multi-step execution. This model boasts a significant advantage with its one-million token context, making it suitable for analyzing massive documents or expansive code bases. Moreover, Nova Premier's distillation feature allows the creation of more efficient models, such as Nova Pro and Nova Micro, that deliver high accuracy with reduced latency and operational costs. Its advanced capabilities have already proven effective in various scenarios, such as investment research, where it can coordinate multiple agents to gather and synthesize relevant financial data. This process not only saves time but also enhances the overall efficiency of the AI models used.

Cohere Rerank serves as an advanced semantic search solution that enhances enterprise search and retrieval by accurately prioritizing results based on their relevance. It analyzes a query alongside a selection of documents, arranging them from highest to lowest semantic alignment while providing each document with a relevance score that ranges from 0 to 1. This process guarantees that only the most relevant documents enter your RAG pipeline and agentic workflows, effectively cutting down on token consumption, reducing latency, and improving precision. The newest iteration, Rerank v3.5, is capable of handling English and multilingual documents, as well as semi-structured formats like JSON, with a context limit of 4096 tokens. It efficiently chunks lengthy documents, taking the highest relevance score from these segments for optimal ranking. Rerank can seamlessly plug into current keyword or semantic search frameworks with minimal coding adjustments, significantly enhancing the relevancy of search outcomes. Accessible through Cohere's API, it is designed to be compatible with a range of platforms, including Amazon Bedrock and SageMaker, making it a versatile choice for various applications. Its user-friendly integration ensures that businesses can quickly adopt this tool to improve their data retrieval processes.

Claude Opus 4.1 represents a notable incremental enhancement over its predecessor, Claude Opus 4, designed to elevate coding, agentic reasoning, and data-analysis capabilities while maintaining the same level of deployment complexity. This version boosts coding accuracy to an impressive 74.5 percent on SWE-bench Verified and enhances the depth of research and detailed tracking for agentic search tasks. Furthermore, GitHub has reported significant advancements in multi-file code refactoring, and Rakuten Group emphasizes its ability to accurately identify precise corrections within extensive codebases without introducing any bugs. Independent benchmarks indicate that junior developer test performance has improved by approximately one standard deviation compared to Opus 4, reflecting substantial progress consistent with previous Claude releases.

Claude Sonnet 4.5 represents Anthropic's latest advancement in AI, crafted to thrive in extended coding environments, complex workflows, and heavy computational tasks while prioritizing safety and alignment. It sets new benchmarks with its top-tier performance on the SWE-bench Verified benchmark for software engineering and excels in the OSWorld benchmark for computer usage, demonstrating an impressive capacity to maintain concentration for over 30 hours on intricate, multi-step assignments. Enhancements in tool management, memory capabilities, and context interpretation empower the model to engage in more advanced reasoning, leading to a better grasp of various fields, including finance, law, and STEM, as well as a deeper understanding of coding intricacies. The system incorporates features for context editing and memory management, facilitating prolonged dialogues or multi-agent collaborations, while it also permits code execution and the generation of files within Claude applications. Deployed at AI Safety Level 3 (ASL-3), Sonnet 4.5 is equipped with classifiers that guard against inputs or outputs related to hazardous domains and includes defenses against prompt injection, ensuring a more secure interaction. This model signifies a significant leap forward in the intelligent automation of complex tasks, aiming to reshape how users engage with AI technologies.