Researched and Edited by Rajat Gupta
Last updated: · How we review
Editor's Summary · LLM Observability & Evaluation Software
These tools answer two different questions and most teams need both. Tracing tells you what your application did — which prompts ran, what the retriever returned, where latency and cost went. Evaluation tells you whether the output was any good. LangSmith, Langfuse, Helicone and Traceloop lead on the first; Confident AI, Patronus, TruLens and Arize Phoenix lead on the second.
Self-hosting is the fastest way to shorten the list. Prompts and completions routinely contain customer data, and many teams cannot send them to a hosted service. Langfuse, Phoenix, Opik, Lunary, Athina and LangWatch all run in your own environment.
If you already run an observability stack, Traceloop's OpenLLMetry sends traces into Datadog, Grafana or Honeycomb rather than asking you to adopt another dashboard.
Portkey is the odd one out and worth knowing about: it sits in the request path as a gateway, so it adds routing, fallback and caching alongside observability, and captures traces without any application instrumentation.
For agents specifically, single-response evaluation misses the failure mode that matters — an agent can answer every individual turn well and still fail the conversation. Maxim and LangWatch test at the scenario level.
Quick picks for LLM Observability & Evaluation Software
- Best open-source all-rounder — Langfuse
- Best for RAG evaluation — Arize Phoenix
- Best gateway plus observability — Portkey
Who gets the most from LLM Observability & Evaluation Software
- 1Engineers debugging why a RAG pipeline returns poor answers
- 2Teams gating deployments on evaluation results in CI
- 3Platform owners tracking model cost and latency across providers
How to choose LLM Observability & Evaluation Software
Separate tracing from evaluation before shortlisting, because tools lead on one and follow on the other. If your prompts carry customer data, filter for self-hosting first and accept the shorter list. If you already run Datadog or Grafana, an OpenTelemetry-native tool avoids a second pane of glass. And if you are building agents rather than single-turn features, insist on scenario-level testing — per-response scoring will look healthy while the conversation fails.
Showing 1-3 out of 3
9.4
SpotScore
Langfuse
Open-source LLM observability platform for tracing, evaluation, and prompt management
Best for: SMB teams
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What is Langfuse?
Langfuse is an open-source LLM engineering platform for tracing, evaluation, prompt management, and metrics. It provides detailed traces of LLM application executions — capturing every LLM call, tool use, retrieval step, and agent action — and links traces to user sessions for production ...
Read more about LangfuseStarts from Freefree, also offers free forever plan
9.2
SpotScore
Helicone
Open-source LLM proxy with automatic logging, cost tracking, and caching in two minutes
Best for: SMB teams
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What is Helicone?
Helicone is an open-source LLM observability platform that works as a proxy — developers route their LLM API calls through Helicone's gateway with a single URL change, and it automatically logs every request, tracks cost and latency, enables prompt caching, and provides rate limiting. ...
Read more about HeliconeStarts from Freefree, also offers free forever plan
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9.0
SpotScore
LangSmith
LLM observability and evaluation platform for tracing, testing, and monitoring AI applications
Best for: SMB teams
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What is LangSmith?
LangSmith is the LLMOps observability and evaluation platform built by LangChain for teams developing production LLM applications. It provides tracing, debugging, and evaluation for any LLM application — not just LangChain ones. Developers use LangSmith to trace every LLM call and tool ...
Read more about LangSmithStarts from Freefree, also offers free forever plan
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Learn More About LLM Observability & Evaluation Software
Compare 22 LLM observability and evaluation platforms on tracing, evaluators, prompt management, self-hosting and OpenTelemetry compatibility.
LLM observability and evaluation software gives teams visibility into how their AI applications actually behave in production and during development — tracing every prompt, model call, and agent step, scoring output quality against test sets, and catching regressions before they reach users. Distinct from general APM tools, these platforms understand LLM-specific concerns: token cost, hallucination detection, prompt versioning, and multi-step agent traces, making them essential infrastructure for any team shipping products on top of large language models.
- Whether you need tracing (what happened) or evaluation (was it any good) — most teams need both?
- Whether prompts and completions can leave your environment, which decides self-hosting?
- Whether it fits your existing observability stack or asks you to adopt another dashboard?
What is llm observability and evaluation software?
LLM observability and evaluation software gives teams visibility into how their AI applications actually behave in production and during development — tracing every prompt, model call, and agent step, scoring output quality against test sets, and catching regressions before they reach users. Distinct from general APM tools, these platforms understand LLM-specific concerns: token cost, hallucination detection, prompt versioning, and multi-step agent traces, making them essential infrastructure for any team shipping products on top of large language models.
LLM Observability & Evaluation Software compared
Spotsaas lists 22 llm observability and evaluation products. Entries below are ordered by how many verified reviews each carries; where a vendor publishes pricing openly it is shown.
| # | Product | SpotScore | Rating | Reviews | Starting price |
|---|---|---|---|---|---|
| 1 | LangSmithTop rated | 9 | ★★★★★4.50 | 310 | $39 |
| 2 | 9.4 | ★★★★★4.70 | 185 | $59 | |
| 3 | 9.2 | ★★★★★4.60 | 145 | $20 | |
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3 of the 22 listings carry verified Spotsaas reviews. Products without reviews are listed but not ranked.
LLM Observability and Evaluation pricing
3 of the 22 products listed publish pricing openly; the rest quote on request.
What to check before you buy
Whether you need tracing (what happened) or evaluation (was it any good) — most teams need both
Essential questions to ask the vendor:
- Whether you need tracing (what happened) or evaluation (was it any good) — most teams need both?
How to overcome it: Separate tracing from evaluation before shortlisting, because tools lead on one and follow on the other.
Whether prompts and completions can leave your environment, which decides self-hosting
Essential questions to ask the vendor:
- Whether prompts and completions can leave your environment, which decides self-hosting?
How to overcome it: If your prompts carry customer data, filter for self-hosting first and accept the shorter list.
Whether it fits your existing observability stack or asks you to adopt another dashboard
Essential questions to ask the vendor:
- Whether it fits your existing observability stack or asks you to adopt another dashboard?
How to overcome it: If you already run Datadog or Grafana, an OpenTelemetry-native tool avoids a second pane of glass.
Who uses LLM Observability & Evaluation Software
Typical roles include Engineers debugging why a RAG pipeline returns poor answers, Teams gating deployments on evaluation results in CI, and Platform owners tracking model cost and latency across providers.
Frequently asked questions
Basics FAQs
What is llm observability and evaluation software?
LLM observability and evaluation software gives teams visibility into how their AI applications actually behave in production and during development — tracing every prompt, model call, and agent step, scoring output quality against test sets, and catching regressions before they reach users. Distinct from general APM tools, these platforms understand LLM-specific concerns: token cost, hallucination detection, prompt versioning, and multi-step agent traces, making them essential infrastructure for any team shipping products on top of large language models.
What does LLM stand for?
LLM stands for large language model. LLM observability and evaluation software gives teams visibility into how their AI applications actually behave in production and during development — tracing every prompt, model call, and agent step, scoring output quality against test sets, and catching regressions before they reach users. Distinct from general APM tools, these platforms understand LLM-specific concerns: token cost, hallucination detection, prompt versioning, and multi-step agent traces, making them essential infrastructure for any team shipping products on top of large language models.
Pricing FAQs
Is there free llm observability and evaluation software?
Yes. 11 of the 22 products listed offer a genuinely free or freemium tier: Confident AI, Athina AI, Braintrust (AI Observability), Galileo, Arize AI, Weights & Biases, among others. A free trial is not the same thing, and is noted separately on each listing.
Choosing FAQs
How do I choose llm observability and evaluation software?
Separate tracing from evaluation before shortlisting, because tools lead on one and follow on the other. If your prompts carry customer data, filter for self-hosting first and accept the shorter list. If you already run Datadog or Grafana, an OpenTelemetry-native tool avoids a second pane of glass. And if you are building agents rather than single-turn features, insist on scenario-level testing — per-response scoring will look healthy while the conversation fails.
Buyers FAQs
Who uses llm observability and evaluation software?
Typically engineers debugging why a RAG pipeline returns poor answers; teams gating deployments on evaluation results in CI; platform owners tracking model cost and latency across providers.
Coverage FAQs
How many llm observability and evaluation products does Spotsaas track?
Spotsaas currently lists 22 products in this category, 3 of them with verified reviews. Listings are researched from vendor documentation and updated as the market changes.
Disclaimer: This research has been collated from a variety of authoritative sources. We welcome your feedback at [email protected].