List of the Top 21 Free Telemetry Software in 2026

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 is Grafana Labs’ fully managed observability platform designed for scale, intelligence, and efficiency. Built on the open-source LGTM Stack—Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics—it delivers a complete, composable observability experience without operational overhead. Grafana Cloud leverages machine learning and intelligent data management to help teams optimize performance and control costs. Features like Adaptive Metrics and cardinality management automatically aggregate high-volume telemetry data for precision insights at a fraction of the cost. With AI-driven alerting and incident correlation, teams can detect anomalies faster, reduce alert fatigue, and focus on what matters most—system reliability and user experience. Grafana Cloud supports OLAP-style analysis through integrations with analytical databases and data warehouses, allowing teams to visualize and correlate multi-dimensional datasets alongside observability data. Seamlessly integrated with OpenTelemetry and hundreds of data sources, Grafana Cloud provides a single pane of glass for monitoring applications, infrastructure, and digital experiences across hybrid and multi-cloud environments. Backed by Grafana Labs’ global expertise and trusted by 5,000+ customers, it empowers organizations to achieve observability at scale—open, intelligent, and future-ready.

Azure Monitor significantly improves the dependability and effectiveness of applications and services by offering a comprehensive system for collecting, analyzing, and reacting to telemetry data from both cloud-based and on-premises environments. This powerful tool not only allows you to understand how well your applications are performing but also helps in identifying potential issues that could affect their operation and the resources they rely on. As a result, organizations utilizing Azure Monitor can enhance service quality and boost user satisfaction by implementing timely and informed interventions. Furthermore, the insights provided by Azure Monitor empower teams to make data-driven decisions that lead to continuous improvement and optimized performance.

Splunk AppDynamics brings together observability, business analytics, and runtime security to create a unified platform for managing hybrid and on-prem application environments. Designed for enterprises with complex infrastructures, it correlates application, transaction, and end-user data with business metrics, ensuring that performance improvements translate directly into measurable business outcomes. The platform excels at anomaly detection and root cause analysis, powered by AI and ML baselining that continuously learns and adapts to system behavior. It supports monitoring of critical SAP and non-SAP workflows, enabling organizations to trace issues to the deepest levels of ABAP code or database queries while maintaining business continuity. AppDynamics extends observability across APIs, SaaS, ISPs, and third-party services, offering a full view of network and infrastructure dependencies. For security, it integrates runtime protection, proactively blocking attacks and ensuring compliance with enterprise standards. With Digital Experience Monitoring, organizations gain end-to-end visibility into customer journeys across web, mobile, and synthetic environments. Flexible data collection through agents or OpenTelemetry ensures seamless integration into existing architectures. By preventing costly outages and optimizing resources, AppDynamics has demonstrated tangible ROI, saving enterprises millions while improving user satisfaction. It’s a solution built for businesses that need to unify performance, security, and business impact in one platform.

Elastic is a prominent search technology firm that has created a suite known as the Elastic Stack, which includes Elasticsearch, Kibana, Beats, and Logstash. These software-as-a-service solutions enable users to leverage data for real-time analytics, security measures, search functionalities, and logging at scale. With a community of over 100,000 members spread across 45 nations, Elastic's products have been downloaded more than 400 million times since their launch. Currently, numerous organizations, including notable names like Cisco, eBay, Dell, Goldman Sachs, Groupon, HP, Microsoft, Netflix, Uber, Verizon, and Yelp, rely on Elastic Stack and Elastic Cloud to enhance their critical systems, driving significant revenue growth and reducing costs. Headquartered in both Amsterdam, The Netherlands, and Mountain View, California, Elastic employs a workforce of more than 1,000 individuals across more than 35 countries, contributing to its global impact in the tech industry. This extensive reach and adoption highlight Elastic's vital role in transforming how enterprises manage and utilize their data.

ThingsBoard enhances device connectivity by utilizing popular IoT protocols like MQTT, CoAP, and HTTP, catering to both cloud-based and local deployments. It is designed with a focus on scalability, robustness, and outstanding performance, safeguarding your data effectively. Users can securely provision, monitor, and manage their IoT devices through extensive server-side APIs. The platform enables the seamless connection of devices, assets, customers, and other entities, simplifying integration. Telemetry data can be collected and stored effectively in a resilient and scalable manner. Users have the option to visualize their data through built-in or customized widgets, along with adaptable dashboards that can be shared with clients. Additionally, ThingsBoard allows for the creation of data processing rule chains, which help transform and standardize device data efficiently. Alerts can be triggered based on incoming telemetry events, attribute updates, device inactivity, and user interactions, further enhancing system responsiveness. To maximize scalability and fault tolerance, users can build a ThingsBoard cluster utilizing its microservices architecture. Furthermore, the platform supports both cloud and on-premises configurations, providing a flexible solution for diverse deployment requirements. This adaptability empowers users to customize their IoT implementations to meet specific needs and preferences, ensuring an optimal experience.

VirtualMetric is a cutting-edge telemetry pipeline and security monitoring platform designed to provide enterprise-level data collection, analysis, and optimization. Its flagship solution, DataStream, simplifies the process of collecting and enriching security logs from a variety of systems, including Windows, Linux, and MacOS. By filtering out non-essential data and reducing log sizes, VirtualMetric helps organizations cut down on SIEM ingestion costs while improving threat detection and response times. The platform’s advanced features, such as zero data loss, high availability, and long-term compliance storage, ensure businesses can handle increasing telemetry volumes while maintaining robust security and compliance standards. With its comprehensive access controls and scalable architecture, VirtualMetric enables businesses to optimize their data flows and bolster their security posture with minimal manual intervention.

Cribl Stream enables the creation of an observability pipeline that facilitates the parsing and reformatting of data in real-time before incurring costs for analysis. This tool ensures that you receive the necessary data in your desired format and at the appropriate destination. It allows for the translation and structuring of data according to any required tooling schema, efficiently routing it to the suitable tools for various tasks or all necessary tools. Different teams can opt for distinct analytics platforms without needing to install additional forwarders or agents. A staggering 50% of log and metric data can go unutilized, encompassing issues like duplicate entries, null fields, and fields that lack analytical significance. With Cribl Stream, you can eliminate superfluous data streams, focusing solely on the information you need for analysis. Furthermore, it serves as an optimal solution for integrating diverse data formats into the trusted tools utilized for IT and Security purposes. The universal receiver feature of Cribl Stream allows for data collection from any machine source and facilitates scheduled batch collections from REST APIs, including Kinesis Firehose, Raw HTTP, and Microsoft Office 365 APIs, streamlining the data management process. Ultimately, this functionality empowers organizations to enhance their data analytics capabilities significantly.

Transform your log management practices with Honeycomb, a platform meticulously crafted for modern development teams that seek to extract valuable insights into application performance while improving log management efficiency. Honeycomb’s fast query capabilities allow you to reveal concealed issues within your system’s logs, metrics, and traces, employing interactive charts that deliver thorough examinations of raw data with high cardinality. By establishing Service Level Objectives (SLOs) that align with user priorities, you can minimize unnecessary alerts and concentrate on critical tasks. This streamlined approach not only reduces on-call duties but also accelerates code deployment, ultimately ensuring high levels of customer satisfaction. You can pinpoint the root causes of performance issues, optimize your code effectively, and gain a clear view of your production environment in impressive detail. Our SLOs provide timely alerts when customers face challenges, facilitating quick investigations into the underlying issues—all managed from a unified interface. Furthermore, the Query Builder allows for seamless data analysis, enabling you to visualize behavioral patterns for individual users and services, categorized by various dimensions for enriched analytical perspectives. This all-encompassing strategy guarantees that your team is equipped to proactively tackle performance obstacles while continuously enhancing the user experience, thus fostering greater engagement and loyalty. Ultimately, Honeycomb empowers your team to maintain a high-performance environment that is responsive to users' needs.

Elevate your monitoring and alerting strategies by utilizing a leading open-source tool known as Prometheus. This powerful platform organizes its data in the form of time series, which are essentially sequences of values linked to specific timestamps, metrics, and labeled dimensions. Beyond the stored time series, Prometheus can generate temporary derived time series based on the results of queries, enhancing versatility. Its querying capabilities are powered by PromQL (Prometheus Query Language), which enables users to real-time select and aggregate data from time series. The results from these queries can be visualized as graphs, presented in a table format via Prometheus's expression browser, or retrieved by external applications through its HTTP API. To configure Prometheus, users can employ both command-line flags and a configuration file, where flags define unchangeable system parameters such as storage locations and retention thresholds for disk and memory. This combination of configuration methods offers a customized monitoring experience that can accommodate a variety of user requirements. If you’re keen on delving deeper into this feature-rich tool, additional information is available at: https://sourceforge.net/projects/prometheus.mirror/. With Prometheus, you can achieve a level of monitoring sophistication that optimizes performance and responsiveness.

Contemporary cloud-native applications are characterized by a dramatic increase in dependencies, shorter lifecycles, frequent releases, and a wealth of telemetry data. Traditional proprietary monitoring and application performance management (APM) tools were designed for a time when monolithic applications and stable infrastructure were the norm. These outdated solutions are often expensive, intrusive, and disjointed, leading to more confusion than insight. Although open-source and cloud monitoring alternatives present a good foundation, they require highly skilled engineers to integrate, maintain, and analyze the data effectively. As you work through the challenges of adapting to modern infrastructure, your current monitoring system might struggle to keep pace, indicating a need for a fresh approach. This is where OpsCruise comes into play! Our platform is deeply knowledgeable about Kubernetes, and when combined with our groundbreaking machine learning-driven behavior profiling, it empowers your team to foresee performance challenges and swiftly pinpoint their sources. Moreover, this can be accomplished at a significantly lower cost than traditional monitoring tools, eliminating the need for code instrumentation, agent deployment, or the management of open-source software. By choosing OpsCruise, you are not merely implementing a new tool; you are initiating a profound transformation in how you oversee and enhance your infrastructure, paving the way for greater efficiency and effectiveness in your operations.

Open source continuous profiling provides a robust method for pinpointing and addressing critical performance issues across your code, infrastructure, and CI/CD workflows. It enables organizations to label data according to relevant dimensions that matter most to them. This approach promotes the cost-effective and efficient storage of large quantities of high cardinality profiling data. With the use of FlameQL, users have the capability to run tailored queries that allow for quick selection and aggregation of profiles, simplifying the analysis process. You can conduct an in-depth assessment of application performance profiles utilizing our comprehensive set of profiling tools. By gaining insights into CPU and memory resource usage at any given time, you can proactively identify performance problems before they impact users. The platform also gathers profiles from various external profiling tools into a single, centralized repository, streamlining management efforts. Additionally, by integrating with your OpenTelemetry tracing data, you can access request-specific or span-specific profiles, which greatly enhance other observability metrics such as traces and logs, thus providing a deeper understanding of application performance. This all-encompassing strategy not only promotes proactive monitoring but also significantly improves overall system dependability. Furthermore, with consistent tracking and analysis, organizations can make informed decisions that lead to continuous performance optimization.

SigNoz offers an open-source alternative to Datadog and New Relic, delivering a holistic solution for all your observability needs. This all-encompassing platform integrates application performance monitoring (APM), logs, metrics, exceptions, alerts, and customizable dashboards, all powered by a sophisticated query builder. With SigNoz, users can eliminate the hassle of managing multiple tools for monitoring traces, metrics, and logs. It also features a collection of impressive pre-built charts along with a robust query builder that facilitates in-depth data exploration. By embracing an open-source framework, users can sidestep vendor lock-in while enjoying enhanced flexibility in their operations. OpenTelemetry's auto-instrumentation libraries can be utilized, allowing teams to get started with little to no modifications to their existing code. OpenTelemetry emerges as a comprehensive solution for all telemetry needs, establishing a unified standard for telemetry signals that enhances productivity and maintains consistency across teams. Users can construct queries that span all telemetry signals, carry out aggregations, and apply filters and formulas to derive deeper insights from their data. Notably, SigNoz harnesses ClickHouse, a high-performance open-source distributed columnar database, ensuring that data ingestion and aggregation are exceptionally swift. Consequently, it serves as an excellent option for teams aiming to elevate their observability practices without sacrificing performance, making it a worthy investment for forward-thinking organizations.

Distributed tracing platforms such as Jaeger are essential for the effective operation of modern software systems built on microservices architecture. By monitoring the flow of requests and data across a distributed network, Jaeger offers insights into the interactions among various services, which can sometimes result in delays or errors. This tool skillfully connects these components, allowing users to identify performance bottlenecks, troubleshoot issues, and improve the overall dependability of their applications. In addition, Jaeger is notable for being a fully open-source solution that is designed to be cloud-native and can scale without limits. Its capacity to deliver profound insights into intricate systems makes it a crucial asset for developers looking to enhance application performance. Moreover, the insights gained from using Jaeger can contribute to more efficient resource allocation and better user experiences.

OpenLIT functions as an advanced observability tool that seamlessly integrates with OpenTelemetry, specifically designed for monitoring applications. It streamlines the process of embedding observability into AI initiatives, requiring merely a single line of code for its setup. This innovative tool is compatible with prominent LLM libraries, including those from OpenAI and HuggingFace, which makes its implementation simple and intuitive. Users can effectively track LLM and GPU performance, as well as related expenses, to enhance efficiency and scalability. The platform provides a continuous stream of data for visualization, which allows for swift decision-making and modifications without hindering application performance. OpenLIT's user-friendly interface presents a comprehensive overview of LLM costs, token usage, performance metrics, and user interactions. Furthermore, it enables effortless connections to popular observability platforms such as Datadog and Grafana Cloud for automated data export. This all-encompassing strategy guarantees that applications are under constant surveillance, facilitating proactive resource and performance management. With OpenLIT, developers can concentrate on refining their AI models while the tool adeptly handles observability, ensuring that nothing essential is overlooked. Ultimately, this empowers teams to maximize both productivity and innovation in their projects.

Langtrace serves as a comprehensive open-source observability tool aimed at collecting and analyzing traces and metrics to improve the performance of your LLM applications. With a strong emphasis on security, it boasts a cloud platform that holds SOC 2 Type II certification, guaranteeing that your data is safeguarded effectively. This versatile tool is designed to work seamlessly with a range of widely used LLMs, frameworks, and vector databases. Moreover, Langtrace supports self-hosting options and follows the OpenTelemetry standard, enabling you to use traces across any observability platforms you choose, thus preventing vendor lock-in. Achieve thorough visibility and valuable insights into your entire ML pipeline, regardless of whether you are utilizing a RAG or a finely tuned model, as it adeptly captures traces and logs from various frameworks, vector databases, and LLM interactions. By generating annotated golden datasets through recorded LLM interactions, you can continuously test and refine your AI applications. Langtrace is also equipped with heuristic, statistical, and model-based evaluations to streamline this enhancement journey, ensuring that your systems keep pace with cutting-edge technological developments. Ultimately, the robust capabilities of Langtrace empower developers to sustain high levels of performance and dependability within their machine learning initiatives, fostering innovation and improvement in their projects.

Diagnosing and fixing performance problems and errors in your microservices involves a thorough examination of root causes through traces, logs, and metrics. By utilizing Aspecto's integrated remote sampling, you can significantly cut down on OpenTelemetry trace costs. The manner in which OTel data is presented plays a crucial role in your troubleshooting capabilities; with outstanding visualization, you can effortlessly drill down from a broad overview to detailed specifics. The ability to correlate logs with their associated traces with a simple click facilitates easy navigation. Throughout this process, maintaining context is vital for quicker issue resolution. Employ filters, free-text search, and grouping options to navigate your trace data efficiently, allowing for the quick pinpointing of issues within your system. Optimize costs by sampling only the essential information, directing your focus on traces by specific languages, libraries, routes, and errors. Ensure data privacy by masking sensitive details within trace data or certain routes. Moreover, incorporate your daily tools into your processes, such as logs, error monitoring, and external events APIs, to boost your operational efficiency. This holistic approach not only streamlines your troubleshooting but also makes it cost-effective and highly efficient. By actively engaging with these strategies, your team will be better equipped to maintain high-performing microservices that meet both user expectations and business goals.

Minimize delays, identify inefficiencies, and effectively resolve issues. Join a rapidly expanding network of global enterprises that are achieving up to 20 times the value and return on investment through the use of KloudMate, which significantly surpasses other observability solutions. Seamlessly monitor crucial metrics and relationships while detecting anomalies with alerts and tracking capabilities. Quickly locate vital 'break-points' in your application development cycle to tackle challenges before they escalate. Analyze service maps for each element of your application, unveiling intricate connections and dependencies among components. Track every request and action to obtain a thorough understanding of execution paths and performance metrics. No matter whether you are functioning within a multi-cloud, hybrid, or private setting, leverage unified infrastructure monitoring tools to evaluate metrics and derive meaningful insights. Improve your debugging precision and speed with a comprehensive overview of your system, enabling you to uncover and address problems more promptly. By adopting this strategy, your team can uphold exceptional performance and reliability across your applications, ultimately fostering a more resilient digital infrastructure. This proactive approach not only enhances operational efficiency but also contributes significantly to overall business success.

Phoenix is an open-source library designed to improve observability for experimentation, evaluation, and troubleshooting. It enables AI engineers and data scientists to quickly visualize information, evaluate performance, pinpoint problems, and export data for further development. Created by Arize AI, the team behind a prominent AI observability platform, along with a committed group of core contributors, Phoenix integrates effortlessly with OpenTelemetry and OpenInference instrumentation. The main package for Phoenix is called arize-phoenix, which includes a variety of helper packages customized for different requirements. Our semantic layer is crafted to incorporate LLM telemetry within OpenTelemetry, enabling the automatic instrumentation of commonly used packages. This versatile library facilitates tracing for AI applications, providing options for both manual instrumentation and seamless integration with platforms like LlamaIndex, Langchain, and OpenAI. LLM tracing offers a detailed overview of the pathways traversed by requests as they move through the various stages or components of an LLM application, ensuring thorough observability. This functionality is vital for refining AI workflows, boosting efficiency, and ultimately elevating overall system performance while empowering teams to make data-driven decisions.

Pydantic Logfire emerges as an observability tool specifically crafted to elevate the monitoring of Python applications by transforming logs into actionable insights. It provides crucial performance metrics, tracing functions, and an extensive overview of application behavior, which includes request headers, bodies, and exhaustive execution paths. Leveraging OpenTelemetry, Pydantic Logfire integrates effortlessly with popular libraries, ensuring ease of use while preserving the versatility of OpenTelemetry's features. By allowing developers to augment their applications with structured data and easily accessible Python objects, it opens the door to real-time insights through diverse visualizations, dashboards, and alert mechanisms. Furthermore, Logfire supports manual tracing, context logging, and the management of exceptions, all within a modern logging framework. This versatile tool is tailored for developers seeking a simplified and effective observability solution, boasting out-of-the-box integrations and features designed with the user in mind. Its adaptability and extensive functionalities render it an indispensable resource for those aiming to enhance their application's monitoring approach, providing an edge in understanding and optimizing performance. Ultimately, Pydantic Logfire stands out as a key player in the realm of application observability, merging technical depth with user-friendly design.

Small Hours operates as an AI-enhanced observability platform that identifies server exceptions, assesses their significance, and routes them to the proper team or individual. By leveraging Markdown or your existing runbook, you can enhance our tool's ability to troubleshoot a variety of issues effectively. Our platform ensures seamless integration with any technology stack through support for OpenTelemetry. You can also link to your current alert systems to quickly identify pressing issues. By connecting your codebases and runbooks, you provide essential context and directives that facilitate smoother operations. Your code and data are kept secure and are never stored, giving you peace of mind. The platform adeptly categorizes problems and can even create pull requests when necessary. It is finely tuned for performance and speed, particularly in enterprise environments. With our continuous automated root cause analysis, you can effectively minimize downtime and enhance operational efficiency, guaranteeing that your systems operate seamlessly at all times. Additionally, the intuitive interface allows users to navigate and utilize the platform with ease, ensuring that teams can respond rapidly to any challenges that arise.

Security and observability specifically designed for Kubernetes ecosystems are crucial for the success of contemporary cloud-native applications. Adopting security and observability as code is vital for protecting various elements, such as hosts, virtual machines, containers, Kubernetes components, workloads, and services, ensuring the safeguarding of both north-south and east-west traffic while upholding enterprise security protocols and maintaining ongoing compliance. Additionally, Kubernetes-native observability as code enables the collection of real-time telemetry enriched with contextual information from Kubernetes, providing a comprehensive overview of interactions among all components, from hosts to services. This capability allows for rapid troubleshooting through the use of machine learning techniques to identify anomalies and performance challenges effectively. By leveraging a unified framework, organizations can seamlessly secure, monitor, and resolve issues across multi-cluster, multi-cloud, and hybrid-cloud environments that utilize both Linux and Windows containers. The capacity to swiftly update and implement security policies in just seconds empowers businesses to enforce compliance and tackle emerging vulnerabilities without delay. Ultimately, this efficient approach is essential for sustaining the integrity, security, and performance of cloud-native infrastructures, allowing organizations to thrive in increasingly complex environments.