AI Tool

AI Observability by OpenObserve Review

OpenObserve provides OpenTelemetry-native observability for agents and large language models (LLMs), allowing users to trace every agent, tool call, and model request, and attribute costs to tokens in a unified platform.

shipped Sep 10, 2026agentsfreemium

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agents

AI Observability by OpenObserve — product screenshot

Why it matters

1OpenTelemetry-native observability for AI agents and LLMs.

2Offers a freemium pricing model with a free tier up to 50GB.

3Secured $10 million in Series A funding on April 28, 2026.

4Crossed 20,000 GitHub stars on July 16, 2026.

About AI Observability by OpenObserve

Business Model

Usage-Based (Pay Per Use)

Usage Pricing

$X per GB per GB

Free Credits

Free up to 50GB

Headquarters

Menlo Park, CA, USA

Platforms

Web, API

Target Audience

Developers and organizations utilizing AI and LLM technologies

Pricing Plans

Free Tier

Free

• Up to 50GB of ingested data

Enterprise Professional

Contact sales

• Advanced features for growing teams
• Self-hosted

Enterprise Premium

Contact sales

• Enterprise-grade support
• SSO and SLAs for large-scale deployments

Cost Examples

• Ingesting data calculated per GB

API Docs GitHubOpen Source

Specs

API Docs

View Documentation →

GitHub

View Repository →

API Available

Yes, public API

Screenshots

overview

What is AI Observability by OpenObserve?

AI Observability by OpenObserve is a AI observability tool developed by OpenObserve that enables developers, SREs, and DevOps teams to monitor, evaluate, and improve AI systems, particularly Large Language Models (LLMs) and agentic applications, in production environments. It integrates AI-specific telemetry with traditional observability data like logs, metrics, and traces, providing comprehensive visibility into the behavior and performance of AI applications.

features

Key Features of AI Observability by OpenObserve

AI Observability by OpenObserve provides a suite of features designed for comprehensive monitoring and management of AI workloads. The platform is built on an OpenTelemetry-native architecture, ensuring broad compatibility and standardized data collection.

OpenTelemetry-native observability for agents and LLMs.
Tracing of every agent, tool call, and model request.
Attribution of costs to tokens for granular expense tracking.
API availability for programmatic interaction and integration.
Support for both cloud and self-hosted deployment models.
Built-in quality metrics for evaluating AI outputs.
Configurable API rate limits at organization and role levels.
Automatic throttling for suspicious activity and resource quotas.
Autonomous AI SRE and anomaly detection capabilities (Observability 3.0).
Support for Service Level Objectives (SLOs) and grouped alerts.

use cases

Who Should Use AI Observability by OpenObserve?

AI Observability by OpenObserve is primarily designed for engineering teams responsible for deploying and managing AI applications in production. Its capabilities address specific challenges faced by developers, SREs, and DevOps teams working with LLMs and AI agents.

Developers: For debugging multi-turn conversations and agentic workflows, and following failures from LLM calls through the backend and database.
SREs and DevOps teams: For monitoring and understanding LLM application behavior, detecting loops, running online evaluations in AI agents, and correlating AI performance with infrastructure logs, traces, and metrics.
Teams running AI workloads in production: For tracing agent sessions to analyze cost, time, and quality across models, tools, services, datastores, and user sessions, and for cost optimization of agent calls.

how to use

How to Use AI Observability by OpenObserve

To begin using AI Observability by OpenObserve, users typically integrate their AI applications with the platform's OpenTelemetry-native agents. This involves configuring data collection and sending telemetry to the OpenObserve backend for analysis and visualization.

1Sign up for an OpenObserve account or deploy a self-hosted instance.
2Integrate AI agents and LLM applications using OpenTelemetry SDKs.
3Configure tracing to capture agent sessions, tool calls, and model requests.
4Utilize the platform's dashboards to monitor token usage, latency, and errors.
5Set up alerts for anomalies in AI performance or cost spikes.
6Leverage evaluation features to assess model quality and identify areas for improvement.

pricing

AI Observability by OpenObserve Pricing & Plans

AI Observability by OpenObserve operates on a freemium and usage-based business model, offering a free tier and tiered enterprise plans. The primary cost driver is data ingestion, calculated per gigabyte.

Free Tier: Free, includes up to 50GB of data ingestion per month.
Enterprise Professional: Contact sales for pricing and features.
Enterprise Premium: Contact sales for pricing and advanced features.

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Pros

+OpenTelemetry-native architecture ensures broad compatibility and standardized data collection.
+Unified platform for correlating AI telemetry with traditional logs, metrics, and traces.
+Granular cost attribution to tokens, enabling precise LLM cost monitoring and optimization.
+Freemium model with a generous free tier (50GB) makes it accessible for smaller projects.
+Strong focus on AI agent debugging, including detection of loops and online evaluations.
+Recent updates like Observability 3.0 introduce autonomous AI SRE and anomaly detection.

Cons

−Some advanced security features like RBAC and SSO are primarily available in paid enterprise tiers.
−Older user feedback mentioned potential UI issues, though recent reviews suggest improvements.
−While OpenTelemetry-native, integrating with specific AI frameworks not explicitly listed might require custom configuration.
−Pricing for Enterprise Professional and Premium tiers requires contacting sales, lacking transparent public figures.

Policies

Pricing Page

View Pricing→

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AI Observability by OpenObserve vs Competitors

OpenObserve positions its AI Observability solution as a cost-effective, OpenTelemetry-native, and unified platform for AI workloads, differentiating itself from both traditional observability suites and specialized LLM monitoring tools.

HeliconeOn Stork

Offers an open-source, self-hostable platform with a strong focus on cost optimization, caching, and rate limiting for LLM applications.

Helicone provides a robust open-source option for self-hosting and a managed service, but its OpenTelemetry integration might require more manual setup compared to OpenObserve's native OpenTelemetry approach.

LangSmithOn Stork

Deeply integrated with the LangChain framework, providing comprehensive debugging, testing, evaluation, and monitoring specifically for LangChain-based LLM applications.

LangSmith is highly optimized for LangChain users, whereas OpenObserve is OpenTelemetry-native and aims for broader framework compatibility. If not using LangChain, integration might be less seamless.

TraceloopOn Stork

Provides OpenTelemetry-native LLM observability with a focus on tracing, evaluation, and fine-tuning insights for AI applications.

Traceloop is very similar to OpenObserve in its OpenTelemetry-native approach to LLM observability, potentially offering more advanced evaluation and fine-tuning features, but the core tracing and cost attribution are comparable.

OpenTelemetry with Jaeger and Grafana↗

Offers a completely customizable, open-source stack for collecting, storing, and visualizing traces and metrics, built on industry standards like OpenTelemetry.

While completely free and open source, this solution requires significant manual setup, configuration, and maintenance of multiple components, unlike OpenObserve's unified, managed platform. It also lacks out-of-the-box LLM-specific cost attribution and agent monitoring features that OpenObserve provides, requiring custom implementation.

See every AI Observability by OpenObserve alternative, compared→