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OpenObserve

OpenObserve

AI

Open-source unified observability for logs, metrics and traces, with an AI SRE agent that correlates signals and an LLM cost and eval monitor.

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About OpenObserve

OpenObserve is an open-source observability platform that unifies logs, metrics, traces, RUM, session replay and error tracking in one system, built in Rust on Apache DataFusion with columnar Parquet storage and no index to maintain. That architecture is the basis of its central claim: keeping a year of logs on roughly 1/140th the storage and 1/30th the compute of Elasticsearch, and up to eight times lower cost than Datadog. On top of the data layer sits an autocorrelation engine that pairs signals across the frontend, API, application, database, network and infrastructure layers, feeding an AI SRE agent that investigates incidents end to end — building a service graph, sizing blast radius against SLOs, naming a root cause from trace evidence, and proposing or applying a corrective action such as a rollback. A proactive mode reviews every service daily and flags the ones degrading over a two-week window rather than waiting for an alert. For teams running models in production, agentic observability tracks token spend, per-model usage mix and error rates, and surfaces failed evaluations with the prompt, the output and the grader score that caused the failure. OpenObserve can be self-hosted or run as a fully managed cloud with pay-as-you-go pricing.

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Key Features

Unified Telemetry Store: Holds logs, metrics, traces, RUM, session replay and error tracking in a single system instead of separate tools per signal type.
Columnar Parquet Storage in Rust: Built on the DataFusion engine with no index to build, which underpins the claimed 140x storage and 30x compute reduction versus Elasticsearch.
Autocorrelation Engine: Continuously pairs signals across frontend, API, application, database, network and infrastructure layers at over a million signals per second.
AI SRE Agent: Investigates an incident by building a service graph, quantifying SLO and revenue impact, identifying the root cause from trace evidence, and applying a corrective action such as a rollback.
Proactive Daily Briefing: Reviews every service over a rolling 14-day window and flags the ones degrading, with the deploy or change that coincided with the regression.
Agentic and LLM Observability: Tracks token spend, per-model usage mix and error rates across models in production, with failed evaluations shown alongside prompt, output and grader score.
Transparent Usage Pricing: Charges per GB ingested and per GB queried with retention included, rather than tiered seat or host licensing.
Self-Hosted or Managed Cloud: The same platform can run entirely inside your own infrastructure or as a fully managed service, including BYOB for enterprise deployments.

Use Cases

Cutting Observability Spend: Replace an Elastic or Datadog deployment while keeping a year of log retention, using far less storage and compute for the same data.
Automated Incident Triage: Let the SRE agent correlate an error-rate spike to a specific deploy and propose the rollback before an engineer is paged.
Monitoring LLM Applications in Production: Track token cost, model mix and evaluation failures across several models serving live traffic.
Catching Slow Regressions: Surface a service whose p95 latency quietly tripled after an index rebuild, which threshold alerting would miss.
Full-Stack Root Cause Analysis: Trace a checkout failure from the browser through the API and into the database on one correlated timeline.
Compliance-Constrained Deployments: Self-host the whole observability stack so telemetry never leaves your own infrastructure.
SLO Management: Measure which service level objectives an ongoing incident is putting at risk and how much of a user flow is affected.

Frequently asked questions about OpenObserve

What is OpenObserve?

Open-source unified observability for logs, metrics and traces, with an AI SRE agent that correlates signals and an LLM cost and eval monitor.

How does OpenObserve work?

OpenObserve works by combining Unified Telemetry Store: Holds logs, metrics, traces, RUM, session replay and error tracking in a single system instead of separate tools per signal type., Columnar Parquet Storage in Rust: Built on the DataFusion engine with no index to build, which underpins the claimed 140x storage and 30x compute reduction versus Elasticsearch., Autocorrelation Engine: Continuously pairs signals across frontend, API, application, database, network and infrastructure layers at over a million signals per second., AI SRE Agent: Investigates an incident by building a service graph, quantifying SLO and revenue impact, identifying the root cause from trace evidence, and applying a corrective action such as a rollback., Proactive Daily Briefing: Reviews every service over a rolling 14-day window and flags the ones degrading, with the deploy or change that coincided with the regression. to help users with Cutting Observability Spend: Replace an Elastic or Datadog deployment while keeping a year of log retention, using far less storage and compute for the same data., Automated Incident Triage: Let the SRE agent correlate an error-rate spike to a specific deploy and propose the rollback before an engineer is paged., Monitoring LLM Applications in Production: Track token cost, model mix and evaluation failures across several models serving live traffic., Catching Slow Regressions: Surface a service whose p95 latency quietly tripled after an index rebuild, which threshold alerting would miss., Full-Stack Root Cause Analysis: Trace a checkout failure from the browser through the API and into the database on one correlated timeline..

What are the main features of OpenObserve?

Key features include Unified Telemetry Store: Holds logs, metrics, traces, RUM, session replay and error tracking in a single system instead of separate tools per signal type., Columnar Parquet Storage in Rust: Built on the DataFusion engine with no index to build, which underpins the claimed 140x storage and 30x compute reduction versus Elasticsearch., Autocorrelation Engine: Continuously pairs signals across frontend, API, application, database, network and infrastructure layers at over a million signals per second., AI SRE Agent: Investigates an incident by building a service graph, quantifying SLO and revenue impact, identifying the root cause from trace evidence, and applying a corrective action such as a rollback., Proactive Daily Briefing: Reviews every service over a rolling 14-day window and flags the ones degrading, with the deploy or change that coincided with the regression..

Who is OpenObserve for?

OpenObserve is useful for anyone interested in Cutting Observability Spend: Replace an Elastic or Datadog deployment while keeping a year of log retention, using far less storage and compute for the same data., Automated Incident Triage: Let the SRE agent correlate an error-rate spike to a specific deploy and propose the rollback before an engineer is paged., Monitoring LLM Applications in Production: Track token cost, model mix and evaluation failures across several models serving live traffic., Catching Slow Regressions: Surface a service whose p95 latency quietly tripled after an index rebuild, which threshold alerting would miss., Full-Stack Root Cause Analysis: Trace a checkout failure from the browser through the API and into the database on one correlated timeline..

How much does OpenObserve cost?

OpenObserve offers a free tier with paid plans for advanced features.

How do I get started with OpenObserve?

Visit https://openobserve.ai to sign up and explore OpenObserve.

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