Alternatives to Prefix

Grafana Labs provides an open and composable observability stack built around Grafana, the leading open source technology for dashboards and visualization. Recognized as a 2025 Gartner® Magic Quadrant™ Leader for Observability Platforms and positioned furthest to the right for Completeness of Vision, Grafana Labs supports over 25M users and 5,000+ customers. Grafana Cloud delivers the full power of Grafana’s open and composable observability stack—without the overhead of managing infrastructure. As a fully managed SaaS offering from Grafana Labs, it unifies metrics, logs, and traces in one place, giving engineering teams real-time visibility into systems and applications. Built around the LGTM Stack—Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics—Grafana Cloud provides a scalable foundation for modern observability. With built-in integrations for Kubernetes, cloud services, CI/CD pipelines, and OpenTelemetry, Grafana Cloud accelerates time to value while reducing operational complexity. Grafana Cloud also supports OLAP-style analytics through integrations with data warehouses and analytical engines like BigQuery, ClickHouse, and Druid—enabling multi-dimensional exploration across observability and business data. Teams gain access to powerful features like Adaptive Metrics for cost optimization, incident response workflows, and synthetic monitoring for performance testing—all within a secure, globally distributed platform. Whether you’re modernizing infrastructure, scaling observability, or driving SLO-based performance, Grafana Cloud delivers the insights you need—fast, flexible, and vendor-neutral.

Azure Monitor enhances the reliability and efficiency of your applications and services by providing a holistic approach to gathering, analyzing, and responding to telemetry from both cloud and on-premises settings. This tool enables you to gain insights into the performance of your applications while also proactively detecting problems that may impact them and their associated resources. By leveraging Azure Monitor, organizations can ensure better service continuity and improve user satisfaction through timely interventions.

Edge Delta is a new way to do observability. We are the only provider that processes your data as it's created and gives DevOps, platform engineers and SRE teams the freedom to route it anywhere. As a result, customers can make observability costs predictable, surface the most useful insights, and shape your data however they need. Our primary differentiator is our distributed architecture. We are the only observability provider that pushes data processing upstream to the infrastructure level, enabling users to process their logs and metrics as soon as they’re created at the source. Data processing includes: * Shaping, enriching, and filtering data * Creating log analytics * Distilling metrics libraries into the most useful data * Detecting anomalies and triggering alerts We combine our distributed approach with a column-oriented backend to help users store and analyze massive data volumes without impacting performance or cost. By using Edge Delta, customers can reduce observability costs without sacrificing visibility. Additionally, they can surface insights and trigger alerts before data leaves their environment.

DataBahn is an advanced platform that harnesses the power of AI to manage data pipelines and enhance security, streamlining the processes of data collection, integration, and optimization from a variety of sources to various destinations. Boasting a robust array of over 400 connectors, it simplifies the onboarding process and boosts the efficiency of data flow significantly. The platform automates data collection and ingestion, allowing for smooth integration, even when dealing with disparate security tools. Moreover, it optimizes costs related to SIEM and data storage through intelligent, rule-based filtering, which directs less critical data to more affordable storage options. It also ensures real-time visibility and insights by utilizing telemetry health alerts and implementing failover handling, which guarantees the integrity and completeness of data collection. Comprehensive data governance is further supported by AI-driven tagging, automated quarantining of sensitive information, and mechanisms in place to prevent vendor lock-in. In addition, DataBahn's adaptability allows organizations to stay agile and responsive to evolving data management needs.

Splunk Observability Cloud serves as an all-encompassing platform for real-time monitoring and observability, aimed at enabling organizations to achieve complete insight into their cloud-native infrastructures, applications, and services. By merging metrics, logs, and traces into a single solution, it delivers uninterrupted end-to-end visibility across intricate architectures. The platform's robust analytics, powered by AI-driven insights and customizable dashboards, empower teams to swiftly pinpoint and address performance challenges, minimize downtime, and enhance system reliability. Supporting a diverse array of integrations, it offers real-time, high-resolution data for proactive monitoring purposes. Consequently, IT and DevOps teams can effectively identify anomalies, optimize performance, and maintain the health and efficiency of both cloud and hybrid environments, ultimately fostering greater operational excellence.

IBM Instana sets the benchmark for incident prevention, offering comprehensive full-stack visibility with one-second precision and a notification time of just three seconds. In the current landscape of rapidly evolving and intricate cloud infrastructures, the financial repercussions of an hour of downtime can soar into the six-figure range or more. Conventional application performance monitoring (APM) tools often fall short, lacking the speed and depth required to effectively address and contextualize technical issues, and they usually necessitate extensive training for super users before they can be utilized effectively. In contrast, IBM Instana Observability transcends the limitations of standard APM tools by making observability accessible to a wider audience, enabling individuals from DevOps, SRE, platform engineering, ITOps, and development teams to obtain the necessary data and context without barriers. The Instana Dynamic APM functions through a specialized agent architecture, utilizing sensors—automated, lightweight programs specifically designed to monitor particular entities and ensure optimal performance. As a result, organizations can respond to incidents proactively and maintain a higher level of service continuity.

The Dynatrace software intelligence platform revolutionizes the way organizations operate by offering a unique combination of observability, automation, and intelligence all within a single framework. Say goodbye to cumbersome toolkits and embrace a unified platform that enhances automation across your dynamic multicloud environments while facilitating collaboration among various teams. This platform fosters synergy between business, development, and operations through a comprehensive array of tailored use cases centralized in one location. It enables you to effectively manage and integrate even the most intricate multicloud scenarios, boasting seamless compatibility with all leading cloud platforms and technologies. Gain an expansive understanding of your environment that encompasses metrics, logs, and traces, complemented by a detailed topological model that includes distributed tracing, code-level insights, entity relationships, and user experience data—all presented in context. By integrating Dynatrace’s open API into your current ecosystem, you can streamline automation across all aspects, from development and deployment to cloud operations and business workflows, ultimately leading to increased efficiency and innovation. This cohesive approach not only simplifies management but also drives measurable improvements in performance and responsiveness across the board.

ServiceNow Cloud Observability provides real-time visibility and monitoring of cloud infrastructure, applications and services. It allows organizations to identify and resolve performance problems by integrating data from different cloud environments into a single dashboard. ServiceNow Cloud Observability's advanced analytics and alerting features help IT and DevOps departments detect anomalies, troubleshoot issues, and ensure optimal performance. The platform supports AI-driven insights and automation, allowing teams the ability to respond quickly to incidents. Overall, the platform improves operational efficiency while ensuring a seamless user-experience across cloud environments.

Google Cloud Observability is designed to give you full visibility into the health and performance of your applications. Through the collection of key telemetry data, such as metrics, logs, and traces, the platform empowers you to proactively detect and address issues, keeping your applications reliable and available. With tools for monitoring, troubleshooting, and debugging, Google Cloud's observability services make it easier to analyze complex, distributed systems and respond to unexpected changes efficiently. The ability to view performance patterns and gain actionable insights helps you optimize your strategies and maintain seamless operations across your environment.

Identify and resolve performance issues and errors within your microservices architecture. Establish connections between root causes by analyzing traces, logs, and metrics. Reduce your costs associated with OpenTelemetry traces through Aspecto's integrated remote sampling feature. The way OTel data is visualized plays a crucial role in enhancing your troubleshooting efficiency. Transition seamlessly from a broad overview to intricate details using top-tier visualization tools. Link logs directly to their corresponding traces effortlessly, maintaining context to expedite issue resolution. Utilize filters, free-text searches, and grouping options to navigate your trace data swiftly and accurately locate the source of the problem. Optimize expenses by sampling only essential data, allowing for trace sampling based on programming languages, libraries, specific routes, and error occurrences. Implement data privacy measures to obscure sensitive information within traces, specific routes, or other critical areas. Moreover, integrate your everyday tools with your operational workflow, including logs, error monitoring, and external event APIs, to create a cohesive and efficient system for managing and troubleshooting issues. This holistic approach not only improves visibility but also empowers teams to tackle problems proactively.

OpenTelemetry provides high-quality, widely accessible, and portable telemetry for enhanced observability. It consists of a suite of tools, APIs, and SDKs designed to help you instrument, generate, collect, and export telemetry data, including metrics, logs, and traces, which are essential for evaluating your software's performance and behavior. This framework is available in multiple programming languages, making it versatile and suitable for diverse applications. You can effortlessly create and gather telemetry data from your software and services, subsequently forwarding it to various analytical tools for deeper insights. OpenTelemetry seamlessly integrates with well-known libraries and frameworks like Spring, ASP.NET Core, and Express, among others. The process of installation and integration is streamlined, often requiring just a few lines of code to get started. As a completely free and open-source solution, OpenTelemetry enjoys widespread adoption and support from major players in the observability industry, ensuring a robust community and continual improvements. This makes it an appealing choice for developers seeking to enhance their software monitoring capabilities.

Built on the open-source framework OpenTelemetry, TelemetryHub is the ultimate observability guide, providing data in a single pane of glass for all logs, metrics, and tracing data. A simple, reliable full-stack application monitoring tool that visualizes your complex telemetry data in a consumable format with no propriety configuration or customizations required. TelemetryHub is an easy-to-use and affordable full-stack observability solution provided by Scout APM, an established Application Performance Monitoring tool.

Pydantic Logfire serves as an observability solution aimed at enhancing the monitoring of Python applications by converting logs into practical insights. It offers valuable performance metrics, tracing capabilities, and a comprehensive view of application dynamics, which encompasses request headers, bodies, and detailed execution traces. Built upon OpenTelemetry, Pydantic Logfire seamlessly integrates with widely-used libraries, ensuring user-friendliness while maintaining the adaptability of OpenTelemetry’s functionalities. Developers can enrich their applications with structured data and easily queryable Python objects, allowing them to obtain real-time insights through a variety of visualizations, dashboards, and alert systems. In addition, Logfire facilitates manual tracing, context logging, and exception handling, presenting a contemporary logging framework. This tool is specifically designed for developers in search of a streamlined and efficient observability solution, boasting ready-to-use integrations and user-centric features. Its flexibility and comprehensive capabilities make it a valuable asset for anyone looking to improve their application's monitoring strategy.

Phoenix serves as a comprehensive open-source observability toolkit tailored for experimentation, evaluation, and troubleshooting purposes. It empowers AI engineers and data scientists to swiftly visualize their datasets, assess performance metrics, identify problems, and export relevant data for enhancements. Developed by Arize AI, the creators of a leading AI observability platform, alongside a dedicated group of core contributors, Phoenix is compatible with OpenTelemetry and OpenInference instrumentation standards. The primary package is known as arize-phoenix, and several auxiliary packages cater to specialized applications. Furthermore, our semantic layer enhances LLM telemetry within OpenTelemetry, facilitating the automatic instrumentation of widely-used packages. This versatile library supports tracing for AI applications, allowing for both manual instrumentation and seamless integrations with tools like LlamaIndex, Langchain, and OpenAI. By employing LLM tracing, Phoenix meticulously logs the routes taken by requests as they navigate through various stages or components of an LLM application, thus providing a clearer understanding of system performance and potential bottlenecks. Ultimately, Phoenix aims to streamline the development process, enabling users to maximize the efficiency and reliability of their AI solutions.

SigNoz serves as an open-source alternative to Datadog and New Relic, providing a comprehensive solution for all your observability requirements. This all-in-one platform encompasses APM, logs, metrics, exceptions, alerts, and customizable dashboards, all enhanced by an advanced query builder. With SigNoz, there's no need to juggle multiple tools for monitoring traces, metrics, and logs. It comes equipped with impressive pre-built charts and a robust query builder that allows you to explore your data in depth. By adopting an open-source standard, users can avoid vendor lock-in and enjoy greater flexibility. You can utilize OpenTelemetry's auto-instrumentation libraries, enabling you to begin with minimal to no coding changes. OpenTelemetry stands out as a comprehensive solution for all telemetry requirements, establishing a unified standard for telemetry signals that boosts productivity and ensures consistency among teams. Users can compose queries across all telemetry signals, perform aggregates, and implement filters and formulas to gain deeper insights from their information. SigNoz leverages ClickHouse, a high-performance open-source distributed columnar database, which ensures that data ingestion and aggregation processes are remarkably fast. This makes it an ideal choice for teams looking to enhance their observability practices without compromising on performance.

Fluent Bit is capable of reading data from both local files and network devices, while also extracting metrics in the Prometheus format from your server environment. It automatically tags all events to facilitate filtering, routing, parsing, modification, and output rules effectively. With its built-in reliability features, you can rest assured that in the event of a network or server failure, you can seamlessly resume operations without any risk of losing data. Rather than simply acting as a direct substitute, Fluent Bit significantly enhances your observability framework by optimizing your current logging infrastructure and streamlining the processing of metrics and traces. Additionally, it adheres to a vendor-neutral philosophy, allowing for smooth integration with various ecosystems, including Prometheus and OpenTelemetry. Highly regarded by prominent cloud service providers, financial institutions, and businesses requiring a robust telemetry agent, Fluent Bit adeptly handles a variety of data formats and sources while ensuring excellent performance and reliability. This positions it as a versatile solution that can adapt to the evolving needs of modern data-driven environments.

Langtrace is an open-source observability solution designed to gather and evaluate traces and metrics, aiming to enhance your LLM applications. It prioritizes security with its cloud platform being SOC 2 Type II certified, ensuring your data remains highly protected. The tool is compatible with a variety of popular LLMs, frameworks, and vector databases. Additionally, Langtrace offers the option for self-hosting and adheres to the OpenTelemetry standard, allowing traces to be utilized by any observability tool of your preference and thus avoiding vendor lock-in. Gain comprehensive visibility and insights into your complete ML pipeline, whether working with a RAG or a fine-tuned model, as it effectively captures traces and logs across frameworks, vector databases, and LLM requests. Create annotated golden datasets through traced LLM interactions, which can then be leveraged for ongoing testing and improvement of your AI applications. Langtrace comes equipped with heuristic, statistical, and model-based evaluations to facilitate this enhancement process, thereby ensuring that your systems evolve alongside the latest advancements in technology. With its robust features, Langtrace empowers developers to maintain high performance and reliability in their machine learning projects.

Gain comprehensive insight into your cloud-native and distributed applications, encompassing everything from microservices to serverless setups, allowing for swift identification and resolution of underlying issues. Effortlessly integrate Application Performance Management (APM) to automatically detect anomalies, visualize service dependencies, and streamline the investigation of outliers and unusual behaviors. Enhance your application code with robust support for widely-used programming languages, OpenTelemetry, and distributed tracing methodologies. Recognize performance bottlenecks through automated, curated visual representations of all dependencies, which include cloud services, messaging systems, data storage, and third-party services along with their performance metrics. Investigate anomalies in detail, diving into transaction specifics and various metrics for a more profound analysis of your application’s performance. By employing these strategies, you can ensure that your services run optimally and deliver a superior user experience.

Open source continuous profiling allows you to identify and resolve your most critical performance challenges across code, infrastructure, and CI/CD pipelines. It offers the ability to tag data based on dimensions that are significant to your organization. This solution facilitates the economical and efficient storage of vast amounts of high cardinality profiling data. With FlameQL, users can execute custom queries to swiftly select and aggregate profiles, making analysis straightforward and efficient. You can thoroughly examine application performance profiles using our extensive suite of profiling tools. Gain insights into CPU and memory resource utilization at any moment, enabling you to detect performance issues before your customers notice them. The platform also consolidates profiles from various external profiling tools into a single centralized repository for easier management. Moreover, by linking to your OpenTelemetry tracing data, you can obtain request-specific or span-specific profiles, which significantly enrich other observability data such as traces and logs, ensuring a comprehensive understanding of application performance. This holistic approach fosters proactive monitoring and enhances overall system reliability.

Leverage the most extensively utilized observability platform, founded on the reliable Elastic Stack (commonly referred to as the ELK Stack), to integrate disparate data sources, providing cohesive visibility and actionable insights. To truly monitor and extract insights from your distributed systems, it is essential to consolidate all your observability data within a single framework. Eliminate data silos by merging application, infrastructure, and user information into a holistic solution that facilitates comprehensive observability and alerting. By integrating limitless telemetry data collection with search-driven problem-solving capabilities, you can achieve superior operational and business outcomes. Unify your data silos by assimilating all telemetry data, including metrics, logs, and traces, from any source into a platform that is open, extensible, and scalable. Enhance the speed of problem resolution through automatic anomaly detection that leverages machine learning and sophisticated data analytics, ensuring you stay ahead in today's fast-paced environment. This integrated approach not only streamlines processes but also empowers teams to make informed decisions swiftly.

Improving business outcomes involves making it easier to spot and address high-priority incidents. The WatchTower Platform serves as a comprehensive observability tool that streamlines incident resolution specifically within mainframe environments by effectively integrating and correlating events, data flows, and metrics across various IT silos. It provides a cohesive and intuitive interface for operations teams, allowing them to optimize their workflows. Leveraging established AIOps solutions, WatchTower is adept at detecting potential problems at an early stage, which aids in proactive mitigation. Additionally, it utilizes OpenTelemetry to transmit mainframe data and insights to observability tools, allowing enterprise SREs to pinpoint bottlenecks and improve operational effectiveness. By enhancing alerts with relevant context, WatchTower eliminates the necessity for logging into multiple tools to gather essential information. Its workflows expedite the processes of problem identification, investigation, and incident resolution, while also simplifying the handover and escalation of issues. With such capabilities, WatchTower not only enhances incident management but also empowers teams to proactively maintain high service availability.

A specialized application performance monitoring tool tailored for distributed systems, particularly optimized for microservices, cloud-native environments, and containerized architectures like Kubernetes. One SkyWalking cluster has the capacity to collect and analyze over 100 billion pieces of telemetry data. It boasts capabilities for log formatting, metric extraction, and the implementation of diverse sampling policies via a high-performance script pipeline. Additionally, it allows for the configuration of alarm rules that can be service-centric, deployment-centric, or API-centric. The tool also has the functionality to forward alarms and all telemetry data to third-party services. Furthermore, it is compatible with various metrics, traces, and logs from established ecosystems, including Zipkin, OpenTelemetry, Prometheus, Zabbix, and Fluentd, ensuring seamless integration and comprehensive monitoring across different platforms. This adaptability makes it an essential tool for organizations looking to optimize their distributed systems effectively.

Bindplane is an advanced telemetry pipeline solution based on OpenTelemetry, designed to streamline observability by centralizing the collection, processing, and routing of critical data. It supports a variety of environments such as Linux, Windows, and Kubernetes, making it easier for DevOps teams to manage telemetry at scale. Bindplane reduces log volume by 40%, enhancing cost efficiency and improving data quality. It also offers intelligent processing capabilities, data encryption, and compliance features, ensuring secure and efficient data management. With a no-code interface, the platform provides quick onboarding and intuitive controls for teams to leverage advanced observability tools.

Apica offers a unified platform for efficient data management, addressing complexity and cost challenges. The Apica Ascent platform enables users to collect, control, store, and observe data while swiftly identifying and resolving performance issues. Key features include: *Real-time telemetry data analysis *Automated root cause analysis using machine learning *Fleet tool for automated agent management *Flow tool for AI/ML-powered pipeline optimization *Store for unlimited, cost-effective data storage *Observe for modern observability management, including MELT data handling and dashboard creation This comprehensive solution streamlines troubleshooting in complex distributed systems and integrates synthetic and real data seamlessly

Application Performance Management Get complete visibility into the health and performance of applications. Detect and resolve performance issues no matter where they occur in the entire stack. No sampling. No blindspots. Log Analytics Search and analyze event data across your applications, infrastructure, security, or business without worrying about indexing, data tiers, retention policies, or cost. Keep all log data always hot. Infrastructure Monitoring Capture metrics across your infrastructure – cloud, Kubernetes, serverless, applications or from over 400 pre-built integrations. Visualize the entire stack and troubleshoot performance issues in real-time. O11y AI Investigate and resolve incidents faster with O11y Investigator. Use natural language to explore observability data with O11y Copilot, generate Regular Expressions effortlessly with O11y Regex, and obtain precise answers with O11y GPT. Observe for Snowflake Comprehensive observability into Snowflake workloads. Optimize performance and resource utilization. Deliver secure and compliant operations.

OpenObserve is a robust open-source observability platform designed for managing logs, metrics, and traces, focusing on exceptional performance, scalability, and significantly reduced costs. It enables observability at a petabyte scale by incorporating features like columnar storage data compression and the flexibility of “bring your own bucket” storage options, including local disks and cloud services such as S3, GCS, and Azure Blob. Developed in Rust, it utilizes the DataFusion query engine for direct querying of Parquet files, and it boasts a stateless, horizontally scalable framework that employs caching strategies for both results and disk to ensure rapid performance even during peak loads. By adhering to open standards, including compatibility with OpenTelemetry and vendor-neutral APIs, OpenObserve seamlessly integrates into pre-existing monitoring and logging ecosystems. Its essential components encompass logs, metrics, traces, frontend monitoring, pipelines, alerts, and comprehensive dashboards for visualizations. Ultimately, OpenObserve empowers organizations to achieve efficient and cost-effective observability solutions in their operations.

When organizations choose to invest in a comprehensive observability platform that integrates data, insights, and actions throughout their IT landscape, they are able to address issues more swiftly while also removing data silos, reducing the need for resource-intensive war rooms, and alleviating alert fatigue. The Riverbed IQ unified observability solution empowers both business and IT to make quick and effective decisions by encapsulating expert troubleshooting knowledge, enabling less experienced staff to deliver more first-level resolutions, which in turn fosters digital innovation and enhances the overall digital experience for both customers and employees. By utilizing broad-based telemetry, organizations can attain a cohesive view of performance and insights, establishing a solid foundation of unified observability that supports the delivery of all other capabilities. Riverbed IQ’s methodology towards unified observability initiates with our full-fidelity telemetry, which spans across network and infrastructure components and incorporates metrics related to the end-user experience, ensuring a comprehensive understanding of system performance. This holistic approach not only streamlines troubleshooting but also positions organizations to respond adeptly to evolving digital demands.

OpenLIT serves as an observability tool that is fully integrated with OpenTelemetry, specifically tailored for application monitoring. It simplifies the integration of observability into AI projects, requiring only a single line of code for setup. This tool is compatible with leading LLM libraries, such as those from OpenAI and HuggingFace, making its implementation feel both easy and intuitive. Users can monitor LLM and GPU performance, along with associated costs, to optimize efficiency and scalability effectively. The platform streams data for visualization, enabling rapid decision-making and adjustments without compromising application performance. OpenLIT's user interface is designed to provide a clear view of LLM expenses, token usage, performance metrics, and user interactions. Additionally, it facilitates seamless connections to widely-used observability platforms like Datadog and Grafana Cloud for automatic data export. This comprehensive approach ensures that your applications are consistently monitored, allowing for proactive management of resources and performance. With OpenLIT, developers can focus on enhancing their AI models while the tool manages observability seamlessly.

Enhanced high-definition APM visibility through real user monitoring, synthetic monitoring, and OpenTelemetry offers a solution that is scalable, user-friendly, and simplifies the integration of insights from end users, applications, networks, and the cloud-native space. The rise of microservices within containerized environments on dynamic cloud infrastructures has resulted in a highly transient and distributed landscape at an unprecedented scale. Traditional methods of enhancing APM, which rely on sampled transactions, partial traces, and aggregate metrics, have become ineffective, as legacy APM solutions struggle to identify the reasons behind slow or stalling critical business applications. The Riverbed platform provides cohesive visibility across the contemporary application landscape, ensuring ease of deployment and management, while facilitating quicker resolution of even the most challenging performance issues. Riverbed APM is thoroughly designed for the cloud-native environment, offering extensive monitoring and observability for transactions that operate on the latest cloud and application infrastructures, ultimately enhancing operational efficiency and user experience. This comprehensive approach not only addresses current performance challenges but also positions organizations to adapt to future technological advancements seamlessly.

Dash0 serves as a comprehensive observability platform rooted in OpenTelemetry, amalgamating metrics, logs, traces, and resources into a single, user-friendly interface that facilitates swift and context-aware monitoring while avoiding vendor lock-in. It consolidates metrics from Prometheus and OpenTelemetry, offering robust filtering options for high-cardinality attributes, alongside heatmap drilldowns and intricate trace visualizations to help identify errors and bottlenecks immediately. Users can take advantage of fully customizable dashboards powered by Perses, featuring code-based configuration and the ability to import from Grafana, in addition to smooth integration with pre-established alerts, checks, and PromQL queries. The platform's AI-driven tools, including Log AI for automated severity inference and pattern extraction, enhance telemetry data seamlessly, allowing users to benefit from sophisticated analytics without noticing the underlying AI processes. These artificial intelligence features facilitate log classification, grouping, inferred severity tagging, and efficient triage workflows using the SIFT framework, ultimately improving the overall monitoring experience. Additionally, Dash0 empowers teams to respond proactively to system issues, ensuring optimal performance and reliability across their applications.

Small Hours serves as an AI-driven observability platform designed to diagnose server exceptions, evaluate their impact, and direct them to the appropriate personnel or team. You can utilize Markdown or your current runbook to assist our tool in troubleshooting various issues effectively. We offer seamless integration with any stack through OpenTelemetry support. You can connect to your existing alerts to pinpoint critical problems swiftly. By linking your codebases and runbooks, you can provide necessary context and instructions for smoother operations. Rest assured, your code and data remain secure and are never stored. The platform intelligently categorizes issues and can even generate pull requests as needed. It is specifically optimized for enterprise-scale performance and speed. With our 24/7 automated root cause analysis, you can significantly reduce downtime while maximizing operational efficiency, ensuring your systems run smoothly at all times.

Security and observability tailored for Kubernetes environments. Implementing security and observability as code is essential for modern cloud-native applications. This approach encompasses cloud-native security as code for various elements, including hosts, virtual machines, containers, Kubernetes components, workloads, and services, ensuring protection for both north-south and east-west traffic while facilitating enterprise security measures and maintaining continuous compliance. Furthermore, Kubernetes-native observability as code allows for the gathering of real-time telemetry, enhanced with context from Kubernetes, offering a dynamic view of interactions among components from hosts to services. This enables swift troubleshooting through machine learning-driven detection of anomalies and performance issues. Utilizing a single framework, organizations can effectively secure, monitor, and address challenges in multi-cluster, multi-cloud, and hybrid-cloud environments operating on either Linux or Windows containers. With the ability to update and deploy security policies in mere seconds, businesses can promptly enforce compliance and address any emerging issues. This streamlined process is vital for maintaining the integrity and performance of cloud-native infrastructures.

Stay updated with comprehensive software and hardware inventories, ensure software license compliance, and receive critical alerts about asset status; with PrefixNE, you maintain complete oversight of your network. This innovative tool automates various daily tasks and allows for remote execution of an extensive array of functions, such as software deployment, powering off computers, and performing restarts, all of which enhance efficiency and increase IT productivity. You can manage devices like memory sticks, PDAs, and MP3 players connected to your network, effectively safeguarding against malware threats and data breaches without the need for a complete USB port lockdown. PrefixNE stands out as the most budget-friendly, all-encompassing solution available for IT professionals today. By integrating all essential features required to secure and manage network assets into a single user-friendly application, PrefixNE not only saves you valuable time but also significantly enhances overall productivity, all while remaining highly cost-effective. With its powerful capabilities, PrefixNE is the smart choice for any organization looking to streamline their IT operations.

Sawmills stands out as the pioneering smart telemetry management platform that empowers businesses to manage their logs, metrics, and traces efficiently before incurring expenses from traditional observability tools. Its innovative use of AI for noise detection sets it apart from other solutions in the market, allowing Sawmills to effectively minimize waste, enhance data integrity, and enforce governance measures. Unlike conventional filtering methods and manually crafted rules, which often fall short in scalability, Sawmills leverages AI to analyze telemetry data in real-time, identifying noise patterns, duplicate events, low-value attributes, excessive cardinality, PII/policy breaches, and faulty schemas. For every issue detected, Sawmills suggests a corresponding action—options include filtering, redacting, aggregating, downsampling, normalizing, enriching, and it features the ability to auto-remediate with necessary approvals, service level agreements, and rollbacks. This proactive approach not only shifts oversight upstream but also significantly decreases waste, enhances the quality of the data being collected, and automates governance, ultimately leading to more efficient data management practices across enterprises.

You can innovate faster in the cloud, improve user experience and future-proof applications. Splunk is designed for cloud-native enterprises and helps you solve current problems. Splunk helps you detect any problem before it becomes a customer problem. Our AI-driven Directed Problemshooting reduces MTTR. Flexible, open-source instrumentation eliminates lock-in. Optimize performance by seeing all of your application and using AI-driven analytics. You must observe everything in order to deliver an excellent end-user experience. NoSample™, full-fidelity trace ingestion allows you to leverage all your trace data and identify any anomalies. Directed Troubleshooting reduces MTTR to quickly identify service dependencies, correlations with the underlying infrastructure, and root-cause errors mapping. You can break down and examine any transaction by any dimension or metric. You can quickly and easily see how your application behaves in different regions, hosts or versions.

Uptrace is an observability platform built on OpenTelemetry that enables users to track, comprehend, and enhance intricate distributed systems effectively. With a single, streamlined dashboard, you can oversee your entire application stack efficiently. This setup provides a swift view of all services, hosts, and systems in one place. The distributed tracing feature allows you to follow the journey of a request as it flows through various services and components, highlighting the timing of each operation along with any logs and errors that arise in real-time. Through metrics, you can swiftly gauge, visualize, and monitor a variety of operations using tools such as percentiles, heatmaps, and histograms. By receiving alerts when your application experiences downtime or when a performance issue is detected, you can respond to incidents more promptly. Moreover, the platform allows you to monitor all aspects—spans, logs, errors, and metrics—using a unified query language, simplifying the observability process further. This comprehensive approach ensures that you have all the necessary insights to maintain optimal performance in your distributed systems.

Observability platforms that utilize distributed tracing, like Jaeger, play a crucial role in the functioning of contemporary software applications designed with a microservices architecture. By tracking the movement of requests and data through a distributed system, Jaeger provides visibility into how these requests interact with various services, which can often lead to delays or errors. This platform adeptly links these different elements, enabling users to pinpoint performance issues, diagnose errors, and enhance the overall reliability of applications. Furthermore, Jaeger stands out as a fully open source solution that is designed to be cloud-native and capable of scaling indefinitely. Its ability to provide deep insights into complex systems makes it an invaluable tool for developers aiming to optimize application performance.

Modern cloud-native applications come with significantly more dependencies, fleeting lifecycles, releases, and telemetry data than ever before. Traditional proprietary monitoring and application performance management (APM) solutions were developed for the age of monolithic applications and fixed infrastructure. These legacy tools tend to be costly, intrusive, and fragmented, often creating more confusion than clarity. While open-source and cloud monitoring options provide a solid starting point, they demand highly experienced engineers to effectively integrate, maintain, and interpret the data they generate. As you navigate the complexities of transitioning to contemporary infrastructure, your existing monitoring framework may be pushed to its limits. This signals the need for a new strategy. Enter OpsCruise! Our platform boasts an in-depth understanding of Kubernetes, and when paired with our innovative machine learning-based behavior profiling, it equips your team to anticipate performance issues and quickly identify their origins. Best of all, this can be achieved at a fraction of the cost of existing monitoring solutions, eliminating the need for code instrumentation, agent deployment, or the upkeep of open-source tools. With OpsCruise, you're not just adopting a new tool; you're embracing a transformational shift in how you manage and optimize your infrastructure.

Eliminate delays, pinpoint inefficiencies, and troubleshoot problems effectively. Become a part of a swiftly growing network of global businesses that are realizing up to 20 times the value and return on investment by utilizing KloudMate, far exceeding other observability platforms. Effortlessly track essential metrics, relationships, and identify irregularities through alerts and tracking issues. Swiftly find critical 'break-points' in your application development process to address problems proactively. Examine service maps for each component within your application while revealing complex connections and dependencies. Monitor every request and operation to gain comprehensive insights into execution pathways and performance indicators. Regardless of whether you are operating in a multi-cloud, hybrid, or private environment, take advantage of consolidated Infrastructure monitoring features to assess metrics and extract valuable insights. Enhance your debugging accuracy and speed with a holistic view of your system, ensuring that you can detect and remedy issues more quickly. This approach allows your team to maintain high performance and reliability in your applications.

TraceRoot.AI serves as an open-source, AI-driven observability and debugging platform that aims to assist engineering teams in swiftly addressing production challenges. By merging telemetry data into a unified correlated execution tree, it offers essential causal insights into failures. AI agents leverage this structured representation to summarize problems, identify probable root causes, and even propose actionable solutions or generate GitHub issues and pull requests. Users can engage in interactive trace exploration, featuring zoomable log clusters and detailed views on spans and latency, complemented by insights linked to the code itself. Additionally, lightweight SDKs for Python and TypeScript facilitate effortless instrumentation via OpenTelemetry, accommodating both self-hosted and cloud-based deployments. A key aspect of the platform is its human-in-the-loop interaction, which allows developers to influence the reasoning process by selecting relevant spans or logs, enabling them to validate the agent's reasoning with traceable context. This collaborative approach not only enhances debugging efficiency but also empowers teams with greater control over the issue resolution process.

VirtualMetric is a comprehensive data monitoring solution that provides organizations with real-time insights into security, network, and server performance. Using its advanced DataStream pipeline, VirtualMetric efficiently collects and processes security logs, reducing the burden on SIEM systems by filtering irrelevant data and enabling faster threat detection. The platform supports a wide range of systems, offering automatic log discovery and transformation across environments. With features like zero data loss and compliance storage, VirtualMetric ensures that organizations can meet security and regulatory requirements while minimizing storage costs and enhancing overall IT operations.

Navigating performance visibility in today’s intricate IT landscapes can be quite challenging, particularly with applications that traverse traditional data centers, SaaS, and IaaS cloud setups. A conventional, siloed management approach often leads to a disjointed and incomplete understanding of performance metrics. Consequently, IT teams find themselves dedicating significant time to data analysis, frequently arriving at divergent and sometimes contradictory insights regarding the root causes of performance issues. The Riverbed Portal addresses this issue by consolidating performance telemetry, resulting in a unified and dynamic overview of performance metrics. This comprehensive perspective provides IT operations teams with a reliable single source of truth, which streamlines troubleshooting processes and delivers valuable insights for stakeholders across the organization. Ultimately, this enables IT to effectively manage and enhance applications, data, and traffic within the entire hybrid network, allowing key resources to concentrate on high-priority strategic initiatives while reducing the likelihood of performance-related disputes. By fostering a clearer understanding of performance, teams can make informed decisions that drive overall efficiency and effectiveness within the organization.

Experience a user-friendly and enjoyable web interface that enhances your interaction. The documentation is quite solid, catering to both users and developers effectively. Designed by network engineers with a deep enthusiasm for managing IP addresses, this tool outshines other options available in the market. It also provides a fully equipped command-line interface for those who prefer using libreadline over a web browser. Crafted in an exceptional programming language, it boasts a handy pynipap library, allowing seamless interaction with NIPAP through Python. You will no longer need to sift through extensive lists of IP addresses in search of the next available option. With NIPAP's automated address allocation feature, you can effortlessly retrieve the next free address. It includes an outstanding built-in search engine that meets your needs precisely. NIPAP comprehends IP prefixes and offers search operators tailored to match more specific prefixes, including coverage of prefixes. Additionally, regular expressions are supported for all other search requirements, ensuring a comprehensive search experience. This makes managing IP addresses not only simpler but also more efficient than ever before.

Kloudfuse is an observability platform powered by AI that efficiently scales while integrating various data sources, including metrics, logs, traces, events, and monitoring of digital experiences into a cohesive observability data lake. With support for more than 700 integrations, it facilitates seamless incorporation of both agent-based and open-source data without requiring any re-instrumentation, and it accommodates open query languages such as PromQL, LogQL, TraceQL, GraphQL, and SQL, while also allowing for the creation of custom workflows through notifications and webhooks. Organizations can easily deploy Kloudfuse within their Virtual Private Cloud (VPC) through a straightforward single-command installation and manage operations centrally using a control plane. The platform automatically collects and indexes telemetry data with smart facets, which helps deliver rapid search capabilities, context-aware alerts powered by machine learning, and service level objectives (SLOs) with minimized false positives. Users benefit from comprehensive visibility across the entire stack, enabling them to trace issues from user experience metrics and session replays all the way down to backend profiling, traces, and metrics, which makes troubleshooting more efficient. This holistic approach to observability ensures that teams can quickly identify and resolve code-level issues while maintaining a strong focus on enhancing user experience.

Tracetest is a powerful open-source testing framework that empowers developers to design and execute both end-to-end and integration tests by utilizing OpenTelemetry traces. This tool not only verifies the final results but also scrutinizes each stage of the workflow, guaranteeing that every part of a distributed system operates as intended. It integrates effortlessly with popular testing frameworks such as Cypress, Playwright, k6, and Postman, thus improving testability and transparency without necessitating any modifications to the existing codebase. By employing trace data, Tracetest uncovers problems like improper service interactions or performance hurdles that may go unnoticed with conventional testing approaches. Additionally, it works well with a wide range of observability platforms and can be seamlessly integrated into CI/CD pipelines to facilitate ongoing testing practices. Furthermore, Tracetest provides synthetic monitoring features, which help in the early identification of performance issues, ensuring that user experiences remain unaffected. This multifaceted tool not only enhances testing rigor but also promotes greater confidence in the reliability of distributed systems.