Open Source Mac Observability Tools
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Observability Tools for Mac
View 31 business solutionsBrowse free open source Observability tools and projects for Mac 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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1
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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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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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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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. -
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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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Robusta
Kubernetes observability and automation
Keep your Kubernetes microservices up and running. Connect your existing Prometheus, gain 360° observability. Robusta is both an automation engine for Kubernetes and a multi-cluster observability platform. Robusta is commonly used alongside Prometheus, but other tools are supported too. By listening to all the events in your cluster, Robusta can tell you why alerts fired, what happened at the same time, and what you can do about it. Robusta can either improve your existing alerts or be used to define new alerts triggered by APIServer changes. -
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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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GoFrame
GoFrame is a modular, powerful, high-performance development framework
GoFrame is a modular, powerful, high-performance, and enterprise-class application development framework of Golang. Modular, loosely coupled design, rich components, out-of-the-box, automatic codes generating for efficiency, simple and easy to use, detailed documentation, interface designed components, with high scalability, fully supported tracing and error stack feature, specially developed and powerful ORM component. Robust engineering design specifications, convenient development CLI tool provide, OpenTelemetry observability features support, OpenAPIV3 documentation generating, automatically, and much, much more...ready to explore? -
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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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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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fluentbit
Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX
Fluent Bit is a super-fast, lightweight, and highly scalable logging and metrics processor and forwarder. It is the preferred choice for cloud and containerized environments. A robust, lightweight, and portable architecture for high throughput with low CPU and memory usage from any data source to any destination. Proven across distributed cloud and container environments. Highly available with I/O handlers to store data for disaster recovery. Granular management of data parsing and routing. Filtering and enrichment to optimize security and minimize cost. The lightweight, asynchronous design optimizes resource usage: CPU, memory, disk I/O, network. No more OOM errors! Integration with all your technology, cloud-native services, containers, streaming processors, and data backends. Fully event-driven design leverages the operating system API for performance and reliability. All operations to collect and deliver data are asynchronous. -
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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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Alibaba iLogtail
Fast and Lightweight Observability Data Collector
iLogtail was born for observable scenarios and has many production-level features such as lightweight, high performance, and automated configuration, which are widely used internally by Alibaba Group and tens of thousands of external Alibaba Cloud customers. You can deploy it in physical machines, Kubernetes and other environments to collect telemetry data, such as logs, traces and metrics. Supports a variety of Logs, Traces, and Metrics data collection, and is friendly to container and Kubernetes environment support. The resource cost of data collection is quite low, 5-20 times better than similar telemetry data collection Agent performance. High stability, used in the production of Alibaba and tens of thousands of Alibaba Cloud customers, and collecting dozens of petabytes of observable data every day with nearly tens of millions deployments. -
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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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Elementary
Open-source data observability for analytics engineers
Elementary is an open-source data observability solution for data & analytics engineers. Monitor your dbt project and data in minutes, and be the first to know of data issues. Gain immediate visibility, detect data issues, send actionable alerts, and understand the impact and root cause. Generate a data observability report, host it or share with your team. Monitoring of data quality metrics, freshness, volume and schema changes, including anomaly detection. Elementary data monitors are configured and executed like native tests in dbt your project. Uploading and modeling of dbt artifacts, run and test results to tables as part of your runs. Get informative notifications on data issues, schema changes, models and tests failures. Inspect upstream and downstream dependencies to understand impact and root cause of data issues. -
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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. -
18
LINKERD
Ultralight, security-first service mesh for Kubernetes
Enterprise power without enterprise complexity. Linkerd adds security, observability, and reliability to any Kubernetes cluster. 100% open source, CNCF graduated, and written in Rust. Instantly add latency-aware load balancing, request retries, timeouts, and blue-green deploys to keep your applications resilient. Incredibly small and blazing fast Linkerd2-proxy micro-proxy written in Rust for security and performance. Self-contained control plane, incrementally deployable data plane, and lots and lots of diagnostics and debugging tools. Transparently add mutual TLS to any on-cluster TCP communication with no configuration. Designed by engineers, for engineers. -
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Lunary
The production toolkit for LLMs. Observability, prompt management
Lunary helps developers of LLM Chatbots develop and improve them. -
20
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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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. -
22
PacketStreamer
Distributed tcpdump for cloud native environments
Deepfence PacketStreamer is a high-performance remote packet capture and collection tool. It is used by Deepfence's ThreatStryker security observability platform to gather network traffic on demand from cloud workloads for forensic analysis. PacketStreamer sensors are started on the target servers. Sensors capture traffic, apply filters, and then stream the traffic to a central receiver. Traffic streams may be compressed and/or encrypted using TLS. The PacketStreamer receiver accepts PacketStreamer streams from multiple remote sensors and writes the packets to a local pcap capture file. PacketStreamer sensors collect raw network packets on remote hosts. It selects packets to capture using a BPF filter, and forwards them to a central receiver process where they are written in pcap format. Sensors are very lightweight and impose little performance impact on the remote hosts. PacketStreamer sensors can be run on bare-metal servers, on Docker hosts, and on Kubernetes nodes. -
23
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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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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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.
