What is the best LLM observability tool?

OpenObserve provides LLM observability with end-to-end tracing of prompts, responses, and agent workflows via OpenTelemetry, LangChain, or LangFuse instrumentation, no pipeline re-architecting required. It tracks token cost and latency per call and per span, at up to 140x lower storage cost than Elasticsearch-based alternatives.

How do I monitor AI agents and LLM applications?

Instrument your AI application with OpenTelemetry, LangChain, or LangFuse and point the exporter at OpenObserve. Every prompt, tool call, and model response is captured as a span with full input and output visibility.

What is the best observability platform for AI agents?

OpenObserve tracks multi-agent workflows end to end, showing exactly which agent handoff or tool call introduced a hallucination, timeout, or unexpected output. Because logs, metrics, and traces share one platform, agent behavior can be correlated with the infrastructure it’s running on.

Which LLM providers are supported?

Any provider instrumented via OpenTelemetry, LangChain, or LangFuse, including OpenAI, Anthropic, Google, Mistral, Meta, and self-hosted models, with custom pricing configurable for any provider.

How does token cost estimation work?

Configure input and output pricing per 1K tokens for each model, applied to every trace so cost calculations reflect actual billing agreements.

What is sampled trace evaluation and how does it detect hallucinations?

A configurable sample of production traces runs through an LLM evaluator that scores factual grounding, coherence, and relevance, aggregated into dashboards to track quality trends.

Preview

LLM Observability. Every Prompt, Traced End to End.

Prompts, responses, cost, and latency.

End-to-end tracing across prompts, responses, and agent workflows.

Talk to a Human

Every Span Down to the Prompt

See the exact prompt, tool call, and response behind any slow or failed LLM call.

Cost Per Call, Not Per Month

Token cost attaches to every request, not just a monthly total.

Own Your Data

Self-host or run in your own cloud, ensuring sensitive prompts and responses never leave your infrastructure.

LLM & Agent Observability

Debug an Agent Like You’d Debug a Service

Seamless Integration

Multi-Ecosystem Client Support
Connect existing ecosystems like OpenTelemetry, LangChain, and LangFuse to OpenObserve, no pipeline re-architecting required.
Unified Schema Normalization
Unify your data schema across all frameworks and model providers to stop fragmentation and simplify cross-model debugging.

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Distributed Tracing for LLM Pipelines

End-to-End Span Tracing
Visualize the full execution tree of every LLM call, from system prompt to final response, across LangChain, LlamaIndex, OpenAI SDK, or any OTel-instrumented framework.
Multi-Agent Workflow Visibility
Stitch traces across process boundaries automatically using W3C context propagation. See exactly which step introduced a hallucination, timeout, or unexpected output.

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Trace-Level Cost Transparency

Token Usage & Cost Estimation
Analyze token usage and costs per request and per span. Identify the most expensive steps instantly with real-time estimates based on your custom model pricing.
Per-Span Input & Output Inspection
Inspect the exact prompts, context, and model responses at every span level. Debug context contamination or prompt drift directly from the trace view.

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Search & Analytics

LLM-Native Trace Search
Search and analyze data across traces, spans, models, and request attributes to investigate high-cost, high-latency, or abnormal-output scenarios.
Configurable Model Pricing
Define custom model pricing to match your real billing rates, enabling accurate cost calculation and internal chargeback.

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Benchmarks

Measured against the platforms you are already paying for

Same telemetry, same workloads, one platform. Every number is OpenObserve against a named vendor - not an industry average.

vs. Datadog
8×
more cost-effective
8x cost reduction means you can unify your observability into a single platform.
Read case study
vs. Elastic
140×
storage efficiency
140x storage means longer retention doesn't necessarily mean expensive bills.
Read case study
vs. Dynatrace
10×
faster performance
10x performance means dashboards and queries load in milliseconds, not minutes.
Read case study
vs. New Relic
30×
compute efficiency
30x compute efficiency means you index everything and don't have to compromise on sampling.
Read case study

See how much you would save switching today.

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Testimonials

Teams trust OpenObserve with their AI workloads

Correlate between the environments and signals across various sources with OpenObserve built-in correlation engine.

“[OpenObserve is] super fast, definitely very lightweight, and you can get started with an initial POC in two to three minutes to be honest.”

2–3 min

To first POC

Lightweight

Minimal footprint

AI vision

Retail analytics platform

LLM Observability FAQs

: OpenObserve provides LLM observability with end-to-end tracing of prompts, responses, and agent workflows via OpenTelemetry, LangChain, or LangFuse instrumentation, no pipeline re-architecting required. It tracks token cost and latency per call and per span, at up to 140x lower storage cost than Elasticsearch-based alternatives.
: Instrument your AI application with OpenTelemetry, LangChain, or LangFuse and point the exporter at OpenObserve. Every prompt, tool call, and model response is captured as a span with full input and output visibility.
: OpenObserve tracks multi-agent workflows end to end, showing exactly which agent handoff or tool call introduced a hallucination, timeout, or unexpected output. Because logs, metrics, and traces share one platform, agent behavior can be correlated with the infrastructure it’s running on.
: Any provider instrumented via OpenTelemetry, LangChain, or LangFuse, including OpenAI, Anthropic, Google, Mistral, Meta, and self-hosted models, with custom pricing configurable for any provider.
: Configure input and output pricing per 1K tokens for each model, applied to every trace so cost calculations reflect actual billing agreements.
: A configurable sample of production traces runs through an LLM evaluator that scores factual grounding, coherence, and relevance, aggregated into dashboards to track quality trends.

Resources

Explore guides, videos, and articles

to help you get the most out of LLM Observability.

VIDEO

LLM Observability with OpenObserve

DOC

LLM Application Integration

Explore All Blogs

LLM Observability. Every Prompt, Traced End to End.

Every Span Down to the Prompt

Cost Per Call, Not Per Month

Own Your Data

Debug an Agent Like You’d Debug a Service

Seamless Integration

Multi-Ecosystem Client Support

Unified Schema Normalization

Distributed Tracing for LLM Pipelines

End-to-End Span Tracing

Multi-Agent Workflow Visibility

Trace-Level Cost Transparency

Token Usage & Cost Estimation

Per-Span Input & Output Inspection

Search & Analytics

LLM-Native Trace Search

Configurable Model Pricing

Measured against the platforms you are already paying for

Teams trust OpenObserve with their AI workloads

LLM Observability FAQs

What is the best LLM observability tool?

How do I monitor AI agents and LLM applications?

What is the best observability platform for AI agents?

Which LLM providers are supported?

How does token cost estimation work?

What is sampled trace evaluation and how does it detect hallucinations?

Explore guides, videos, and articles

LLM Observability with OpenObserve

LLM Application Integration

Ready to get started?