Evaluation and observability for RAG and LLM applications in production
RagMetrics is an evaluation and observability platform for retrieval-augmented generation (RAG) systems and broader LLM applications. You log every prompt, retrieval, and response, then run automated quality checks — hallucination, retrieval precision, answer faithfulness — at scale.
Think Datadog for your AI pipeline: traces, metrics, and alerting designed for the failure modes that LLMs actually have, not the ones HTTP services have.
You drop the SDK into your application (Python or Node.js are the main supported runtimes). Every LLM call, retrieval, and tool invocation gets logged with metadata — model, tokens, latency, retrieved chunks, final answer. RagMetrics builds a trace graph so you can see exactly what happened on a bad response.
For evaluation, you define metrics (built-in: faithfulness, relevance, hallucination, toxicity; or custom LLM-judge prompts). Run them on production traces or on offline datasets to compare model versions. Dashboards show drift over time, broken down by user segment or query type.
Free tier covers up to 10,000 traces/month — enough for prototyping and small production apps. Pro at $99/month bumps to 100,000 traces and adds custom evaluators, team seats, and longer retention. Business is custom pricing with SSO, on-prem options, and SLA.
Watch the trace count, not just the price: each LLM call plus retrieval call is one trace, so a multi-step agent burns traces fast. Model your usage before signing.
| Tool | Starting price | Best for |
|---|---|---|
| RagMetrics | Free / $99/mo | RAG and agent observability |
| LangSmith | Free / $39/user/mo | LangChain users |
| Arize Phoenix | Open source | Self-hosted, ML platform teams |
| Langfuse | Free / $59/mo | Open-source-friendly teams |
Free tier covers 10,000 traces/month — enough to prove the value before paying.
Founders shipping RAG-powered search or Q&A features need proof that retrieval quality justifies LLM costs. RagMetrics surfaces retrieval miss rates and token spend per user query, helping founders make go/no-go decisions on feature rollout and pricing models.
Agencies building custom RAG solutions for clients need to diagnose why retrieval fails or generation lags. RagMetrics provides replay and step-through debugging without requiring clients to grant direct database access.
Product teams need to isolate whether poor answers stem from weak retrieval or weak generation. RagMetrics decouples these signals, letting teams A/B test embedding models or ranking strategies independently.
Quarterly access to product leadership.
Bonus credits redeemable on partner tooling.
We re-verify the offer every quarter so it never goes stale.
Hit the button on this page — opens the partner site in a new tab.
Check your investor or accelerator benefits portal for the RagMetrics partner code. Y Combinator, Sequoia, and most Tier 1 VCs have codes available.
Renewals stay at the same rate — verified by us, not the vendor.
| Feature | RagMetrics |
|---|---|
| Free trial | 14 days |
| Cheapest paid plan | $0/mo |
| Annual discount | Up to 25% |
| Refund window | 30 days |
| Setup time | < 1 hour |
| Best for | Founders |
“Hallucination detection for healthcare RAG is genuinely critical”
“CI/CD integration makes LLM quality a proper engineering discipline”
“Automated RAG evaluation caught a retrieval regression we'd have missed”
