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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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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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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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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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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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. -
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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Open Service Mesh
Cloud native service mesh to uniformly manage environments
Open Service Mesh (OSM) is a lightweight, extensible, cloud-native service mesh that allows users to uniformly manage, secure, and get out-of-the-box observability features for highly dynamic microservice environments. The OSM project builds on the ideas and implementations of many cloud-native ecosystem projects including Linkerd, Istio, Consul, Envoy, Kuma, Helm, and the SMI specification. OSM runs an Envoy based control plane on Kubernetes, can be configured with SMI APIs, and works by injecting an Envoy proxy as a sidecar container next to each instance of your application. The proxy contains and executes rules around access control policies, implements routing configuration, and captures metrics. The control plane continually configures proxies to ensure policies and routing rules are up to date and ensures proxies are healthy. -
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Keptn
Cloud-native application life-cycle orchestration
Cloud-native application life-cycle orchestration. Keptn automates your SLO-driven multi-stage delivery and operations & remediation of your applications. Keptn is an event-based control plane for continuous delivery and automated operations for cloud-native applications. -
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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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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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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. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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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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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. -
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Opik
Debug, evaluate, and monitor your LLMapps, RAG systems, and agentic AI
Confidently evaluate, test, and monitor LLM applications. Opik is an open-source platform for evaluating, testing, and monitoring LLM applications. Built by Comet. Record, sort, search, and understand each step your LLM app takes to generate a response. Manually annotate, view, and compare LLM responses in a user-friendly table. Log traces during development and in production. Run experiments with different prompts and evaluate against a test set. Choose and run pre-configured evaluation metrics or define your own with our convenient SDK library. Consult built-in LLM judges for complex issues like hallucination detection, factuality, and moderation. -
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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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Go gRPC Middleware
Golang gRPC Middlewares: interceptor chaining, auth, logging, retries
gRPC Go has support for "interceptors", i.e. middleware that is executed either on the gRPC Server before the request is passed onto the user's application logic, or on the gRPC client either around the user call. It is a perfect way to implement common patterns: auth, logging, tracing, metrics, validation, retries, rate limiting, and more, which can be great generic building blocks that make it easy to build multiple microservices. Especially for observability signals (logging, tracing, metrics) interceptors offer semi-auto-instrumentation that improves the consistency of your observability and allows great correlation techniques (e.g. exemplars and trace ID in logs). Demo-ed in examples. This repository offers ready-to-use middleware that implements gRPC interceptors with examples. In some cases, dedicated projects offer great interceptors, so this repository skips those, and we link them in the interceptors list. -
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Hubble
Network, Service & Security Observability for Kubernetes using eBPF
Hubble is a fully distributed networking and security observability platform for cloud native workloads. It is built on top of Cilium and eBPF to enable deep visibility into the communication and behavior of services as well as the networking infrastructure in a completely transparent manner. The Linux kernel technology eBPF is enabling visibility into systems and applications at a granularity and efficiency that was not possible before. It does so in a completely transparent way, without requiring the application to change or for the application to hide information. By building on top of Cilium, Hubble can leverage eBPF for visibility. By leveraging eBPF, all visibility is programmable and allows for a dynamic approach that minimizes overhead while providing deep and detailed insight where required. Hubble has been created and specifically designed to make best use of these new eBPF powers. -
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Kyma
Kyma is an opinionated set of Kubernetes-based modular building blocks
Kyma is an opinionated set of Kubernetes-based modular building blocks, including all necessary capabilities to develop and run enterprise-grade cloud-native applications. It is the open path to the SAP ecosystem supporting business scenarios end-to-end. Kyma is an actively maintained open-source project supported by SAP. The Kyma project is also a foundation of SAP BTP, Kyma runtime which is a part of SAP Business Technology Platform (BTP). You can use Kyma modules in your own Kubernetes cluster, or try the managed version from SAP BTP with a ready-to-use Kubernetes cluster powered by Gardener. Kyma is built upon leading cloud-native, open-source projects and open standards, such as Istio, NATS, Cloud Events, and Open Telemetry. We created an opinionated set of modules you can easily enable in your Kubernetes cluster to speed up cloud application development and operations. With Kyma, you save the time to pick the right tools and the effort to keep them secure and up to date. -
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LangKit
An open-source toolkit for monitoring Language Learning Models (LLMs)
LangKit is an open-source text metrics toolkit for monitoring language models. It offers an array of methods for extracting relevant signals from the input and/or output text, which are compatible with the open-source data logging library whylogs. Productionizing language models, including LLMs, comes with a range of risks due to the infinite amount of input combinations, which can elicit an infinite amount of outputs. The unstructured nature of text poses a challenge in the ML observability space - a challenge worth solving, since the lack of visibility on the model's behavior can have serious consequences. -
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Loggie
A lightweight, cloud-native data transfer agent and aggregator
Loggie is a lightweight, high-performance, cloud-native agent and aggregator based on Golang. Loggie includes LogConfig/ClusterLogConfig/Interceptor/Sink CRDs, allowing for the creation of data collection, transfer, processing, and sending pipelines through simple YAML file creation. Supports deployment as an independent intermediate machine, which can receive aggregated data sent by Loggie Agent and can also be used to consume and process various data sources. Configure Filebeat and Loggie to collect logs, and send them to a Kafka topic without using client compression, with the Kafka topic partition configured as 3. With sufficient resources for the Agent specification, modify the number of files collected, the concurrency of the sending client (configure Filebeat worker and Loggie parallelism), and observe their respective CPU, memory, and pod network card transmission rates. -
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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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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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kubernetes-event-exporter
Export Kubernetes events to multiple destinations with routing
This tool allows exporting the often missed Kubernetes events to various outputs so that they can be used for observability or alerting purposes. You won't believe what you are missing. Configuration is done via a YAML file, when run in Kubernetes, ConfigMap. The tool watches all the events and the user has to option to filter out some events, according to their properties. Critical events can be routed to alerting tools such as Opsgenie, or all events can be dumped to an Elasticsearch instance. You can use namespaces, and labels on the related object to route some Pod-related events to owners via Slack. The final routing is a tree which allows flexibility. -
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Apache APISIX
The cloud-native API gateway
Provides rich traffic management features such as load balancing, dynamic upstream, canary release, circuit breaking, authentication, observability, and more. Based on the Nginx library and etcd. Cloud-native microservices API gateway, delivering the ultimate performance, security, open source and scalable platform for all your APIs and microservices. Apache APISIX is based on Nginx and etcd. Compared with traditional API gateways, APISIX has dynamic routing and plug-in hot loading, which is especially suitable for API management under micro-service system. You can use Apache APISIX as a traffic entrance to process all business data, including dynamic routing, dynamic upstream, dynamic certificates, A/B testing, canary release, blue-green deployment, limit rate, defense against malicious attacks, metrics, monitoring alarms, service observability, service governance, etc. -
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BFE
A modern layer 7 load balancer from baidu
BFE (Beyond Front End) is a modern layer 7 load balancer from baidu. BFE has a builtin plugin framework that makes it possible to develop new features rapidly by writing plugins. BFE is designed to provide every tenant a dedicated share of the instance. Each tenant’s configuration is isolated and remains invisible to other tenants. BFE supports HTTP, HTTPS, SPDY, HTTP2, gRPC, WebSocket, TLS, FastCGI, etc. Future support is planned for HTTP/3. BFE provides an advanced domain-specific language to describe routing rules which are easy to understand and maintain. BFE supports global load balancing and distributed load balancing for zone aware balancing, zone level failure resilience, overload protection etc. BFE provides a rich set of plugins for traffic management, security, observability, etc. BFE includes detailed built-in metrics for all subsystems. BFE writes various logs for trouble shooting, data analysis and visualization. BFE also supports distributed tracing. -
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DeepFlow
Application Observability using eBPF
DeepFlow provides a universal map with Zero Code by eBPF for production environments, including your services in any language, third-party services without code and all cloud-native infrastructure services. In addition to analyzing common protocols, Wasm plugins are supported for your private protocols. Full-stack golden signals of applications and infrastructures are calculated, pinpointing performance bottlenecks at ease. Zero Code distributed tracing powered by eBPF supports applications in any language and infrastructures including gateways, service meshes, databases, message queues, DNS, and NICs, leaving no blind spots. Full-stack network performance metrics and file I/O events are automatically collected for each Span. Distributed tracing enters a new era, Zero Instrumentation. DeepFlow collects profiling data at a cost of below 1% with Zero Code, plots OnCPU/OffCPU function call stack flame graphs, and locates Full Stack performance bottleneck in the application. -
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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.
