Open Source Windows Observability Tools
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Observability Tools for Windows
View 43 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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Grafana Pyroscope
Continuous Profiling Platform. Debug performance issues
Find and debug your most painful performance issues across code, infrastructure and CI/CD pipelines. Let you tag your data on the dimensions important for your organization. Allows you to store large volumes of high cardinality profiling data cheaply and efficiently. FlameQL enables custom queries to select and aggregate profiles quickly and efficiently for easy analysis. Analyze application performance profiles using our suite of profiling tools. Understand usage of CPU and memory resources at any point in time and identify performance issue before your customer do. Collect, store, and analyze profiles from various external profiling tools in one central location. Link to your Open Telemetry tracing data and get request-specific or span-specific profiles to enhance other observability data like traces and logs. -
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Grafana
Leading open-source visualization and observability platform
Grafana OSS is the leading open-source platform for visualization and observability. It enables teams to query, visualize, alert on, and explore telemetry data from multiple sources in a single interface. With support for 100+ data source plugins—including Prometheus, Loki, Elasticsearch, InfluxDB, SQL/NoSQL databases, and OpenTelemetry—Grafana helps teams correlate metrics, logs, and traces across applications and infrastructure. Users can build interactive dashboards with rich visualizations, template variables, and reusable panels to monitor systems and troubleshoot issues in real time. Grafana includes capabilities such as ad hoc data exploration, alerting, annotations, and flexible query support. Its extensible plugin ecosystem integrates with cloud platforms, databases, and developer tools—allowing teams to build observability workflows without vendor lock-in. The easiest way to get started with Grafana is with Grafana Cloud, our fully managed, full-stack observability platform. -
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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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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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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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qryn
All-in-one Polyglot Observability stack with ClickHouse storage
All the greatest observability formats and integrations you love, at once - LGTM Drop-in compatible. Let's get Polyglot. qryn independently implements popular observability standards, protocols and query languages. Make sure you have sufficient memory and disk resources allocated for your node service and clickhouse server when dealing with large amounts of data and fingerprints. We suggest 8GB RAM or higher for most setups with 100k-1M fingerprints. Observe your daily and weekly data consumption to forecast your disk usage requirements. Compression codecs and other optimizations can be performed at the ClickHouse level. -
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Dagster
An orchestration platform for the development, production
Dagster is an orchestration platform for the development, production, and observation of data assets. Dagster as a productivity platform: With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early. Dagster as a robust orchestration engine: Put your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally. Dagster as a unified control plane: The ‘single plane of glass’ data teams love to use. Rein in the chaos and maintain control over your data as the complexity scales. Centralize your metadata in one tool with built-in observability, diagnostics, cataloging, and lineage. Spot any issues and identify performance improvement opportunities. -
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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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Elastic APM Node.js Agent
Elastic APM Node.js Agent
This is the official Node.js application performance monitoring (APM) agent for the Elastic Observability solution. It is a Node.js package that runs with your Node.js application to automatically capture errors, tracing data, and performance metrics. APM data is sent to your Elastic Observability deployment -- hosted in Elastic's cloud or in your own on-premises deployment -- where you can monitor your application, create alerts, and quick identify root causes of service issues. First, you will need an Elastic Stack deployment. This is a deployment of APM Server (which receives APM data from the APM agent running in your application), Elasticsearch (the database that stores all APM data), and Kibana (the application that provides the interface to visualize and analyze the data). If you do not already have an Elastic deployment to use, follow this APM Quick Start guide to create a free trial on Elastic's cloud. -
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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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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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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. -
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KubeSphere
The container platform tailored for Kubernetes multi-cloud, datacenter
KubeSphere is a distributed operating system for cloud-native application management, using Kubernetes as its kernel. It provides a plug-and-play architecture, allowing third-party applications to be seamlessly integrated into its ecosystem. KubeSphere is also a multi-tenant container platform with full-stack automated IT operation and streamlined DevOps workflows. It provides developer-friendly wizard web UI, helping enterprises to build out a more robust and feature-rich platform, which includes most common functionalities needed for enterprise Kubernetes strategy, see Feature List for details. KubeSphere Lite provides you with free, stable, and out-of-the-box managed cluster service. After registration and login, you can easily create a K8s cluster with KubeSphere installed in only 5 seconds and experience feature-rich KubeSphere. -
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KubeVela
The Modern Application Platform
KubeVela is a modern software delivery platform that makes deploying and operating applications across today's hybrid, multi-cloud environments easier, faster and more reliable. KubeVela is infrastructure agnostic, programmable, yet most importantly, application-centric. It allows you to build powerful software, and deliver them anywhere. Declare your deployment plan as workflow, run it automatically with any CI/CD or GitOps system, extend or re-program the workflow steps with CUE. Glue and orchestrate all your infrastructure capabilities as reusable modules and share the large growing community addons. No ad-hoc scripts, no dirty glue code, just deploy. The deployment workflow in KubeVela is powered by Open Application Model. -
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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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Tetragon
eBPF-based Security Observability and Runtime Enforcement
Tetragon is a flexible Kubernetes-aware security observability and runtime enforcement tool that applies policy and filtering directly with eBPF, allowing for reduced observation overhead, tracking of any process, and real-time enforcement of policies. Observe the complete lifecycle of every process on your machine with Kubernetes context awareness. Translate high-level policies for file monitoring, network observability, container security, and more into low-overhead eBPF programs. Synchronous monitoring, filtering, and enforcement completely in the kernel with eBPF. -
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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. -
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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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Envoy
Cloud-native high-performance edge/middle/service proxy
Envoy is an open source, high-performance edge/middle/service proxy designed for cloud-native applications. It was built by Lyft to solve the common problem of networking and observability when moving to a distributed architecture. Envoy is a proxy designed for single services and applications. Aside from that it is also a communication bus and “universal data plane” designed for large microservice “service mesh” architectures. It runs right along with every application, and abstracts the network by providing common features in a platform-agnostic manner. With Envoy, visualizing problem areas becomes a lot easier thanks to consistent observability. It also helps with overall performance tuning, and easily adding substrate features in one place. -
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Eye
Process monitoring tool. Inspired from Bluepill and God
Process monitoring tool. Inspired from Bluepill and God. Requires Ruby(MRI) >= 1.9.3-p194. Uses Celluloid and Celluloid::IO. Eye is an image processing and analysis library for quickly analyzing image patterns and features, often used in computer vision tasks. -
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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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OpenClaw Opik Observability Plugin
Official plugin for OpenClaw that exports agent traces to Opik
OpenClaw Opik Observability Plugin is an open-source plugin designed to add observability and monitoring capabilities to OpenClaw autonomous AI agents by exporting operational traces to the Opik observability platform. The project integrates directly with OpenClaw’s plugin architecture so that developers can capture detailed runtime information about how their agents behave while executing tasks. Each time an AI agent performs an action—such as calling a large language model, invoking a tool, accessing memory, or delegating to a sub-agent—the plugin records the full interaction and sends it to Opik for analysis and visualization. This allows developers to inspect inputs, outputs, token usage, latency, and execution flow across complex multi-step agent workflows. The goal of the project is to provide transparency into the internal reasoning and operational pipeline of agent systems so developers can diagnose failures, control costs, and improve reliability. -
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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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memphis
Next-Generation Event Processing Platform
Memphis enables building modern queue-based applications that require large volumes of streamed and enriched data, modern protocols, zero ops, up to x9 faster development, up to x46 fewer costs, and significantly lower dev time for data-oriented developers and data engineers. Queues and brokers are a mission-critical component in the modern application architecture and should be highly available and stable as possible. Provide great performance while maintaining efficient resource consumption. Increase observability, integrations with 3rd-party monitoring tools, real-time notifications, stream lineage, and therefore troubleshooting time reduction. Enable rapid development and ultra-short time-to-production. -
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Fluid
Fluid, elastic data abstraction and acceleration for BigData/AI apps
Fluid, elastic data abstraction and acceleration for BigData/AI applications in the cloud. Provide DataSet abstraction for underlying heterogeneous data sources with multidimensional management in a cloud environment. Enable dataset warmup and acceleration for data-intensive applications by using a distributed cache in Kubernetes with observability, portability, and scalability. Taking characteristics of application and data into consideration for cloud application/dataset scheduling to improve the performance.
