Compare FPT AI Factory vs. Unsloth

RunPod provides a cloud infrastructure that enables seamless deployment and scaling of AI workloads with GPU-powered pods. By offering access to a wide array of NVIDIA GPUs, such as the A100 and H100, RunPod supports training and deploying machine learning models with minimal latency and high performance. The platform emphasizes ease of use, allowing users to spin up pods in seconds and scale them dynamically to meet demand. With features like autoscaling, real-time analytics, and serverless scaling, RunPod is an ideal solution for startups, academic institutions, and enterprises seeking a flexible, powerful, and affordable platform for AI development and inference.

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

StackAI is an enterprise AI automation platform that allows organizations to build end-to-end internal tools and processes with AI agents. It ensures every workflow is secure, compliant, and governed, so teams can automate complex processes without heavy engineering. With a visual workflow builder and multi-agent orchestration, StackAI enables full automation from knowledge retrieval to approvals and reporting. Enterprise data sources like SharePoint, Confluence, Notion, Google Drive, and internal databases can be connected with versioning, citations, and access controls to protect sensitive information. AI agents can be deployed as chat assistants, advanced forms, or APIs integrated into Slack, Teams, Salesforce, HubSpot, ServiceNow, or custom apps. Security is built in with SSO (Okta, Azure AD, Google), RBAC, audit logs, PII masking, and data residency. Analytics and cost governance let teams track performance, while evaluations and guardrails ensure reliability before production. StackAI also offers model flexibility, routing tasks across OpenAI, Anthropic, Google, or local LLMs with fine-grained controls for accuracy. A template library accelerates adoption with ready-to-use workflows like Contract Analyzer, Support Desk AI Assistant, RFP Response Builder, and Investment Memo Generator. By consolidating fragmented processes into secure, AI-powered workflows, StackAI reduces manual work, speeds decision-making, and empowers teams to build trusted automation at scale.

LM-Kit.NET is an enterprise-grade toolkit designed for seamlessly integrating generative AI into your .NET applications, fully supporting Windows, Linux, and macOS. Empower your C# and VB.NET projects with a flexible platform that simplifies the creation and orchestration of dynamic AI agents. Leverage efficient Small Language Models for on‑device inference, reducing computational load, minimizing latency, and enhancing security by processing data locally. Experience the power of Retrieval‑Augmented Generation (RAG) to boost accuracy and relevance, while advanced AI agents simplify complex workflows and accelerate development. Native SDKs ensure smooth integration and high performance across diverse platforms. With robust support for custom AI agent development and multi‑agent orchestration, LM‑Kit.NET streamlines prototyping, deployment, and scalability—enabling you to build smarter, faster, and more secure solutions trusted by professionals worldwide.

Google AI Studio is a user-friendly, web-based workspace that offers a streamlined environment for exploring and applying cutting-edge AI technology. It acts as a powerful launchpad for diving into the latest developments in AI, making complex processes more accessible to developers of all levels. The platform provides seamless access to Google's advanced Gemini AI models, creating an ideal space for collaboration and experimentation in building next-gen applications. With tools designed for efficient prompt crafting and model interaction, developers can quickly iterate and incorporate complex AI capabilities into their projects. The flexibility of the platform allows developers to explore a wide range of use cases and AI solutions without being constrained by technical limitations. Google AI Studio goes beyond basic testing by enabling a deeper understanding of model behavior, allowing users to fine-tune and enhance AI performance. This comprehensive platform unlocks the full potential of AI, facilitating innovation and improving efficiency in various fields by lowering the barriers to AI development. By removing complexities, it helps users focus on building impactful solutions faster.

Our decentralized platform streamlines AI data collection and labeling through a worldwide contributor network. By combining crowdsourcing with blockchain technology, we deliver high-quality, traceable datasets. Platform Highlights: Worldwide Collection: Tap into global contributors for comprehensive data gathering Blockchain Security: Every contribution tracked and verified on-chain Quality Focus: Expert validation ensures exceptional data standards Platform Benefits: Rapid scaling of data collection Complete data providence tracking Validated datasets ready for AI use Cost-efficient global operations Flexible contributor network How It Works: Define Your Needs: Create your data collection task Community Activation: Global contributors notified and start gathering data Quality Control: Human verification layer validates all contributions Sample Review: Get dataset sample for approval Full Delivery: Complete dataset delivered once approved

Ango Hub is an all-in-one, quality-oriented data annotation platform that AI teams can use. Ango Hub is available on-premise and in the cloud. It allows AI teams and their data annotation workforces to quickly and efficiently annotate their data without compromising quality. Ango Hub is the only data annotation platform that focuses on quality. It features features that enhance the quality of your annotations. These include a centralized labeling system, a real time issue system, review workflows and sample label libraries. There is also consensus up to 30 on the same asset. Ango Hub is versatile as well. It supports all data types that your team might require, including image, audio, text and native PDF. There are nearly twenty different labeling tools that you can use to annotate data. Some of these tools are unique to Ango hub, such as rotated bounding box, unlimited conditional questions, label relations and table-based labels for more complicated labeling tasks.

BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises. Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.

Accelerate your data journey with AnalyticsCreator—a metadata-driven data warehouse automation solution purpose-built for the Microsoft data ecosystem. AnalyticsCreator simplifies the design, development, and deployment of modern data architectures, including dimensional models, data marts, data vaults, or blended modeling approaches tailored to your business needs. Seamlessly integrate with Microsoft SQL Server, Azure Synapse Analytics, Microsoft Fabric (including OneLake and SQL Endpoint Lakehouse environments), and Power BI. AnalyticsCreator automates ELT pipeline creation, data modeling, historization, and semantic layer generation—helping reduce tool sprawl and minimizing manual SQL coding. Designed to support CI/CD pipelines, AnalyticsCreator connects easily with Azure DevOps and GitHub for version-controlled deployments across development, test, and production environments. This ensures faster, error-free releases while maintaining governance and control across your entire data engineering workflow. Key features include automated documentation, end-to-end data lineage tracking, and adaptive schema evolution—enabling teams to manage change, reduce risk, and maintain auditability at scale. AnalyticsCreator empowers agile data engineering by enabling rapid prototyping and production-grade deployments for Microsoft-centric data initiatives. By eliminating repetitive manual tasks and deployment risks, AnalyticsCreator allows your team to focus on delivering actionable business insights—accelerating time-to-value for your data products and analytics initiatives.

Amp is a next-generation AI-powered coding assistant created by Sourcegraph to transform how software is developed by individuals and teams alike. Powered by cutting-edge models, Amp delivers production-ready code changes by autonomously reasoning through tasks and executing complex edits. It integrates smoothly into existing developer workflows through CLI and VS Code extensions, making it accessible without additional user interface overhead. The tool encourages collaboration by default, allowing teams to share code threads, context, and best practices, which drives continuous improvement and adoption. Designed to support everything from solo developers to large-scale enterprises, Amp ensures security and compliance with features like enterprise single sign-on and zero retention of large language model data. The product’s quality focus sets it apart, delivering results that users describe as faster and more reliable than alternatives. Amp’s community of engineers and creators actively share feedback to refine the tool, supported by extensive documentation and podcasts. Its mission is to accelerate software building while maintaining high-quality outcomes.