Free to list, always.No paid rankings. Every recommendation explains its trade-offs.
OpenSourceChoice

SigNoz is an open-source, OpenTelemetry-native observability platform for your team and their AI agents. Get logs, metrics, and traces in one tool with features like APM, distributed tracing, log management, infra monito Key capabilities include self-hosted, typescript, open-source. Common stack signals: TypeScript, Github Actions, JavaScript, NodeJS.

SigNoz upstream project preview
Upstream preview from the project website or source repository. The current interface may differ.

Best for AI product teamsyou need an inspectable building block for model, agent, retrieval, or inference workflows

Skip ifa fully managed black-box service is a hard requirementMore

Open-source alternative to
01

What it is

SigNoz is an open-source, OpenTelemetry-native observability platform for your team and their AI agents. Get logs, metrics, and traces in one tool with features like APM, distribut GitHub popularity snapshot captured on 2026-07-16: 29,776 stars and 2,333 forks.

Teams can evaluate SigNoz as an open alternative to DataDog while keeping the implementation, license, and repository signals visible. Confirm the official documentation against your exact workflow before treating it as a production dependency.

02

Who it’s for — and when to skip it

AI product teams

Evaluate it when you need an inspectable building block for model, agent, retrieval, or inference workflows.

Skip if a fully managed black-box service is a hard requirement.

Platform engineers

Use the repository and stack signals to assess how it fits your existing data and deployment boundaries.

Skip if your team cannot own upgrades, observability, or capacity planning.

Prototype-focused developers

Consider it when an open implementation helps you validate an idea without committing to a proprietary API.

Skip if the project does not document the models, hardware, or data constraints you need.

03

Strengths and trade-offs

Why teams consider it

self-hosted

typescript

open-source

Source code and NOASSERTION license are visible before adoption

Repository activity is available as a current maintenance signal

What to validate

Self-hosting transfers upgrades, backups, monitoring, and incident response to your team

GitHub popularity is not a security, quality, or product-fit guarantee

The NOASSERTION license still needs review against your distribution and commercial model

Support quality, migration effort, and production hardening vary by project

04

Pricing

Free and open source

No commercial plan is documented in this catalog entry. Self-hosting or third-party infrastructure may still incur operating costs.

Verified Jul 17, 2026 · Official pricing page. Confirm current rates before purchase — catalog figures are discovery aids, not quotes.

Open source

$0Project license
  • Source available under the listed license
  • No catalog-documented paid edition
  • Infrastructure costs depend on how you run it
05

Capabilities and stack fit

01self-hosted
02typescript
03open-source
04react

Catalog metadata supports discovery, not installation. Verify supported versions, dependencies, deployment topology, and production requirements in the official repository.

06

Guides for SigNoz

No project-specific guide is published yet.

Use the learning library to find a guide by technology, category, or difficulty.

Browse guides
07

Related articles

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08

Approved community reviews

No approved review signal yet.

We do not display synthetic testimonials or ratings without sufficient moderated data.

09

Before you adopt it

  1. 01

    Read the license and confirm it fits your intended use and distribution model.

  2. 02

    Review recent commits, open issues, releases, and the maintainer response pattern.

  3. 03

    Run a small proof of concept with representative data, users, and integrations.

  4. 04

    Plan backups, upgrades, secrets, monitoring, and rollback before self-hosting.

  5. 05

    Document an export or migration path before storing critical data.

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