Open Source Windows Observability Tools
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Observability Tools for Windows
View 42 business solutionsBrowse free open source Observability tools and projects for Windows below. Use the toggles on the left to filter open source Observability tools by OS, license, language, programming language, and project status.
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Easily Host LLMs and Web Apps on Cloud RunRun frontend and backend services, batch jobs, host LLMs, and queue processing workloads without the need to manage infrastructure. Cloud Run gives you on-demand GPU access for hosting LLMs and running real-time AI—with 5-second cold starts and automatic scale-to-zero so you only pay for actual usage. New customers get $300 in free credit to start.
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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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Grafana
Leading open-source visualization and observability platform
Grafana OSS is a leading open-source visualization and observability platform that lets you query, visualize, alert on, and explore your data—regardless of where it’s stored. With support for 100+ data source plugins (such as Prometheus, Loki, Elasticsearch, InfluxDB, SQL/NoSQL databases, OTel, and more), you can unify metrics, logs, traces, and other observability signals in one place. Grafana OSS empowers you to build dynamic, reusable dashboards with rich visualizations, template variables, interactive filtering, and cross-panel linking. Its Explore mode enables ad-hoc queries and side-by-side comparisons of time ranges, queries, and data sources. Grafana also includes built-in alerting, allowing you to define threshold-based rules and send notifications to external systems (e.g. Slack, PagerDuty, OpsGenie). Backed by a strong community (https://grafana.com/community/) and open governance, Grafana OSS is free to use, modify, and deploy under the AGPL-3.0 license. -
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Deckhouse
Kubernetes platform from Flant
Deckhouse is a Kubernetes platform that allows you to create homogeneous K8s clusters on any infrastructure. It manages clusters comprehensively and “automagically” and provides all necessary modules and add-ons for autoscaling, observability, security, and service mesh implementation. Deckhouse has vanilla Kubernetes under the hood and integrates a balanced set of Open Source tools that have become the industry standard. Out-of-the-box secure configuration of the Kubernetes cluster, least component privileges, pre-configured role model, end-to-end object identity in the audit system, and integration with external directory services. Built-in implementation of Pod Security Standards and a ready-to-use, extensible set of recommended policies. Deckhouse automates many routine deployment, scaling, and infrastructure management operations out of the box. It manages system software on the nodes (kernel, CRI, kubelet), basic Kubernetes components (control plane, etc, certificates, etc.). -
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Conduit
Conduit streams data between data stores. Kafka Connect replacement
Conduit is a data streaming tool written in Go. It aims to provide the best user experience for building and running real-time data pipelines. Conduit comes with batteries included, it provides a UI, common connectors, processors and observability data out of the box. Sync data between your production systems using an extensible, event-first experience with minimal dependencies that fit within your existing workflow. Eliminate the multi-step process you go through today. Just download the binary and start building. Conduit connectors give you the ability to pull and push data to any production datastore you need. If a datastore is missing, the simple SDK allows you to extend Conduit where you need it. Conduit pipelines listen for changes to a database, data warehouse, etc., and allows your data applications to act upon those changes in real-time. Run it in a way that works for you; use it as a standalone service or orchestrate it within your infrastructure. -
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coroot
Open-source observability for microservices
Collecting metrics, logs, and traces alone doesn't make your applications observable. Coroot turns that data into actionable insights for you. Enable system observability in minutes, no code changes required. Each release is automatically compared with the previous one, so you'll never miss even the slightest performance degradation. With integrated Cost Monitoring, developers can track how each change affects their cloud bill. Understand your cloud costs down to any given application. Doesn't require access to your cloud account or any other configurations. Analyze any unexpected spike in CPU or memory usage down to the precise line of code. Don't make assumptions, know exactly what the resources were spent on. Easily investigate any anomaly by comparing it to the system's baseline behavior. -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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OpenObserve
Elasticsearch/Splunk/Datadog alternative for (logs, metrics, traces)
OpenObserve is a cloud-native observability platform built specifically for logs, metrics, traces, and analytics designed to work at a petabyte scale. It is very simple and easy to operate as opposed to Elasticsearch which requires a couple of dozen knobs to understand and tune which you can get up and running in under 2 minutes. It is a drop-in replacement for Elasticsearch if you are just ingesting data using APIs and searching using Kibana (Kibana is not supported nor required with OpenObserve. OpenObserve provides its own UI which does not require separate installation unlike Kibana). You can reduce your log storage costs by ~140x compared to Elasticsearch by using OpenObserve. Below are the results when we pushed logs from our production Kubernetes cluster to Elasticsearch and OpenObserve using fluent bit. OpenObserve stored data in Amazon s3 and Elasticsearch stored data on Amazon EBS volumes. -
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OneUptime
OneUptime is the complete open-source observability platform
OneUptime is a comprehensive solution for monitoring and managing your online services. Whether you need to check the availability of your website, dashboard, API, or any other online resource, OneUptime can alert your team when downtime happens and keep your customers informed with a status page. OneUptime also helps you handle incidents, set up on-call rotations, run tests, secure your services, analyze logs, track performance, and debug errors. -
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OpenLLMetry
Open-source observability for your LLM application
The repo contains standard OpenTelemetry instrumentations for LLM providers and Vector DBs, as well as a Traceloop SDK that makes it easy to get started with OpenLLMetry, while still outputting standard OpenTelemetry data that can be connected to your observability stack. If you already have OpenTelemetry instrumented, you can just add any of our instrumentations directly. -
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SigNoz
SigNoz is an open-source APM. It helps developers monitor their apps
Monitor your applications and troubleshoot problems in your deployed applications, an open-source alternative to DataDog, New Relic, etc. SigNoz helps developers monitor applications and troubleshoot problems in their deployed applications. SigNoz uses distributed tracing to gain visibility into your software stack. Visualise Metrics, Traces and Logs in a single pane of glass. You can see metrics like p99 latency, error rates for your services, external API calls and individual end points. You can find the root cause of the problem by going to the exact traces which are causing the problem and see detailed flamegraphs of individual request traces. Run aggregates on trace data to get business relevant metrics. Filter and query logs, build dashboards and alerts based on attributes in logs. -
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tapir
Declarative, type-safe web endpoints library
Declarative, type-safe web endpoints library. With tapir, you can describe HTTP API endpoints as immutable Scala values. Each endpoint can contain a number of input and output parameters. Compile-time guarantees, develop-time completions, read-time information. Separate the shape of the endpoint (the "what"), from the server logic (the "how"). Generate documentation from endpoint descriptions. Leverage the metadata to report rich metrics and tracing information. Re-use common endpoint definitions, as well as individual inputs/outputs. Library, not a framework, integrates with your stack. Is your company already using tapir? We're continually expanding the "adopters" section in the documentation; the more the merrier! It would be great to feature your company's logo, but in order to do that, we'll need to write permission to avoid any legal misunderstandings. -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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Artillery
Cloud-scale load testing. Fully serverless, test any stack
Artillery is cloud-native, open source, and integrates with your favorite monitoring and CI/CD stack. Load test anything, at any scale. The most advanced load-testing platform in the world. Get started and run a test in minutes from your local machine. Then scale it out effortlessly. Free & open-source. Artillery scales like no other. Run your tests from your own AWS account with no infra to set up or manage. Use Playwright to load test with real browsers. Test HTTP, WebSocket, Socket.io, gRPC, Kafka, HLS, and more. Write scenarios with multi-step interactions. Designed for testing transactional APIs and web apps. Use ready-made integrations or write custom logic in Node.js, using any of the thousands of useful npm modules. Artillery integrates with the software you know, love and rely on. -
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OTOMI
Self-hosted DevOps Platform for Kubernetes
Otomi is an open source self-hosted PaaS to run on top of any Kubernetes cluster and is placed in the CNCF landscape under the PaaS/Container Service section. A PaaS attempts to connect many of the technologies found in the CNCF landscape in a way to provide direct value. Deploy containerized apps with a few click without writing any K8s YAML manifests. Get access to logs and metrics of deployed apps. Store charts and images in a private registry. Build and run custom CI pipelines. Enable declarative end-to-end app lifecycle management. Configure ingress for apps with a single click. Manage your own secrets. Onboard development teams on shared clusters in a comprehensive multi-tenant setup. Get all the required observability tools in an integrated way. Ensure governance with security policies. Implement zero-trust networking with east-west and north-south network control within K8s. Provide self-service features to development teams. -
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HyperDX
An open source observability platform unifying session replays & logs
HyperDX helps engineers figure out why production is broken faster by centralizing and correlating logs, metrics, traces, exceptions and session replays in one place. An open-source and developer-friendly alternative to Datadog and New Relic. The HyperDX stack ingests, stores, and searches/graphs your telemetry data. After standing up the Docker Compose stack, you'll want to instrument your app to send data over to HyperDX. -
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Jaeger
Monitor and troubleshoot transactions in complex distributed systems
As on-the-ground microservice practitioners are quickly realizing, the majority of operational problems that arise when moving to a distributed architecture are ultimately grounded in two areas: networking and observability. It is simply an orders of magnitude larger problem to network and debug a set of intertwined distributed services versus a single monolithic application. Jaeger, inspired by Dapper and OpenZipkin, is a distributed tracing system released as open source by Uber Technologies. It is used for monitoring and troubleshooting microservices-based distributed systems. OpenTracing compatible data model and instrumentation libraries include Go, Java, Node, Python, C++ and C#. Jaeger uses consistent upfront sampling with individual per service/endpoint probabilities and it has multiple storage backends: Cassandra, Elasticsearch, memory. -
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OpenTelemetry Collector
OpenTelemetry Collector
The OpenTelemetry Collector offers a vendor-agnostic implementation on how to receive, process, and export telemetry data. In addition, it removes the need to run, operate, and maintain multiple agents/collectors in order to support open-source telemetry data formats (e.g. Jaeger, Prometheus, etc.) to multiple open-source or commercial back-ends. -
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Perses
The CNCF sandbox for observability visualisation
Perses is an open-source project for creating and managing time-series dashboards, focused on flexibility and user customization. -
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QuestDB
An open source SQL database designed to process time series data
QuestDB is a high-performance, open-source SQL database for applications in financial services, IoT, machine learning, DevOps and observability. It includes endpoints for PostgreSQL wire protocol, high-throughput schema-agnostic ingestion using InfluxDB Line Protocol, and a REST API for queries, bulk imports, and exports. QuestDB implements ANSI SQL with native extensions for time-oriented language features. These extensions make it simple to correlate data from multiple sources using relational and time series joins. QuestDB achieves high performance from a column-oriented storage model, massively-parallelized vector execution, SIMD instructions, and various low-latency techniques. The entire codebase was built from the ground up in Java and C++, with no dependencies, and is 100% free from garbage collection. We provide a live demo provisioned with the latest QuestDB release and sample datasets. -
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Vector
A high-performance observability data pipeline
Vector is a Rust‑based, high‑performance observability data pipeline tool (agent + aggregator) designed to collect, transform, and route logs and metrics at scale. Created by Datadog, it aims to be the only tool needed from ingestion to vendor output, providing cost-efficient, safe, and flexible telemetry processing. -
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Arize Phoenix
Uncover insights, surface problems, monitor, and fine tune your LLM
Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative) are an amazing technology that will power many of future ML use cases. A large set of these technologies are being deployed into businesses (the real world) in what we consider a production setting. -
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Devtron
Tool integration platform for Kubernetes
Devtron deeply integrates with products across the lifecycle of microservices,i.e., CI, CD, security, cost, debugging, and observability via an intuitive web interface. Devtron is designed to be modular, and its functionality can be easily extended with the help of integrations. Devtron CI/CD with GitOps integration is used to automate the builds and deployments and enables the software development teams to focus on meeting the business requirements, code quality, and security. Devtron leverages Kubernetes auto-scaling and centralized caching to give you unlimited cost-efficient CI workers. Supports pre-CI and post-CI integrations for code quality monitoring. Provides deployment metrics like; deployment frequency, lead time, change failure rate, and mean-time recovery. Seamlessly integrates with Grafana for continuous application metrics like CPU and memory usage, status code, throughput, and latency on the dashboard. -
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Jina
Build cross-modal and multimodal applications on the cloud
Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP, GraphQL protocols with TLS. Intuitive design pattern for high-performance microservices. Seamless Docker container integration: sharing, exploring, sandboxing, versioning and dependency control via Jina Hub. Fast deployment to Kubernetes, Docker Compose and Jina Cloud. Improved engineering efficiency thanks to the Jina AI ecosystem, so you can focus on innovating with the data applications you build. -
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Kuma
The multi-zone service mesh for containers, Kubernetes and VMs
Kuma is a modern Envoy-based service mesh that can run on every cloud, in a single or multi-zone capacity, across both Kubernetes and VMs. Thanks to its broad universal workload support, combined with native support for Envoy as its data plane proxy technology (but with no Envoy expertise required), Kuma provides modern L4-L7 service connectivity, discovery, security, observability, routing, and more across any service on any platform, databases included. Easy to use, with built-in service mesh policies for security, traffic control, discovery, observability, and more, Kuma ships with advanced multi-zone and multi-mesh support that automatically enables cross-zone communication across different clusters and clouds, and automatically propagates service mesh policies across the infrastructure. Kuma is currently being adopted by enterprise organizations around the world to support distributed service meshes across the application teams, on both Kubernetes and VMs. -
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Lunary
The production toolkit for LLMs. Observability, prompt management
Lunary helps developers of LLM Chatbots develop and improve them. -
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ODD Platform
First open-source data discovery and observability platform
Unlock the power of big data with OpenDataDiscovery Platform. Experience seamless end-to-end insights, powered by unprecedented observability and trust - from ingestion to production - while building your ideal tech stack! Democratize data and accelerate insights. Find data that fits your use case and discover hints left by your peers to leverage existing knowledge. Explore tags, ownership details, links to other sources and other information to shorten and simplify data discovery phase. Forget unnerved stakeholders and wasting too much time on digging the root cause of data issues when it fails. With ODD’s automatic company-wide ingestion-to-product lineage you’ll have answers in just seconds and stakeholders won’t need to wait. Sleep well, knowing all your data is in check. Forget manual testing, days of debugging, and weeks of worrying. Know the impact of each code change with automatic testing. Enjoy lineage and alerts powered with data quality information. -
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OpenLIT
OpenLIT is an open-source LLM Observability tool
OpenLIT is an OpenTelemetry-native tool designed to help developers gain insights into the performance of their LLM applications in production. It automatically collects LLM input and output metadata and monitors GPU performance for self-hosted LLMs. OpenLIT makes integrating observability into GenAI projects effortless with just a single line of code. Whether you're working with popular LLM providers such as OpenAI and HuggingFace, or leveraging vector databases like ChromaDB, OpenLIT ensures your applications are monitored seamlessly, providing critical insights including GPU performance stats for self-hosted LLMs to improve performance and reliability. This project proudly follows the Semantic Conventions of the OpenTelemetry community, consistently updating to align with the latest standards in observability. -
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OpenTelemetry
OpenTelemetry Go API and SDK
OpenTelemetry-Go is the Go implementation of OpenTelemetry. It provides a set of APIs to directly measure the performance and behavior of your software and send this data to observability platforms. High-quality, ubiquitous, and portable telemetry to enable effective observability. OpenTelemetry is a collection of APIs, SDKs, and tools. Use it to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) to help you analyze your software’s performance and behavior.
