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 1-20 out of 22
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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Traceloop
OpenTelemetry-native LLM tracing via OpenLLMetry
Best for: SMB teams · Mid-market · Enterprise
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What is Traceloop?
Traceloop provides LLM observability built on OpenLLMetry, its open-source OpenTelemetry extension for generative AI. Because the instrumentation is standard OpenTelemetry, traces flow into existing observability backends such as Datadog, Honeycomb or Grafana rather than requiring a separate ...
Read more about TraceloopTraceloop offers custom pricing plan
Opik
Open-source LLM tracing, evaluation and production monitoring
Best for: SMB teams · Mid-market · Enterprise
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What is Opik?
Opik is Comet's open-source platform for tracing, evaluating and monitoring LLM applications, covering development-time experimentation and production monitoring in one tool. It records traces across chains and agents, supports LLM-as-judge and heuristic evaluators, and can gate CI on ...
Read more about OpikOpik offers custom pricing plan
Arize Phoenix
Open-source LLM tracing and evaluation, runs locally
Best for: SMB teams · Mid-market · Enterprise
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What is Arize Phoenix?
Phoenix is Arize's open-source observability and evaluation tool for LLM applications, built on OpenTelemetry so traces are portable rather than locked to one vendor. It runs locally in a notebook or as a self-hosted service, capturing spans across retrieval, prompting and tool calls, and ...
Read more about Arize PhoenixArize Phoenix offers custom pricing plan
Patronus AI
Automated evaluation and failure detection for LLM systems
Best for: SMB teams · Mid-market · Enterprise
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What is Patronus AI?
Patronus AI focuses on automated evaluation of LLM systems, scoring outputs for hallucination, safety and domain-specific criteria using purpose-trained evaluator models rather than general-purpose LLM judging. It supports adversarial test generation to find failures before release and ...
Read more about Patronus AIPatronus AI offers custom pricing plan
Lunary
Open-source LLM analytics, tracing and prompt management
Best for: SMB teams · Mid-market · Enterprise
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What is Lunary?
Lunary is an open-source observability platform for LLM applications, covering request logging, tracing across chains and agents, prompt versioning and per-user analytics. It supports self-hosting and includes guardrails for detecting problematic content. It is positioned for teams that want ...
Read more about LunaryLunary offers custom pricing plan
TruLens
Open-source evaluation and tracing for LLM apps and agents
Best for: SMB teams · Mid-market · Enterprise
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What is TruLens?
TruLens is an open-source library for evaluating and tracing LLM applications, built around programmatic feedback functions that score outputs for groundedness, context relevance and answer relevance — the triad it uses to diagnose retrieval-augmented generation. Instrumentation is added in ...
Read more about TruLensTruLens offers custom pricing plan
HoneyHive
Tracing, evaluation and prompt management for production AI
Best for: SMB teams · Mid-market · Enterprise
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What is HoneyHive?
HoneyHive provides observability and evaluation for production AI applications, combining distributed tracing across agent steps, online and offline evaluation, dataset curation from real traffic, and prompt management in one platform. Curating evaluation datasets directly from production ...
Read more about HoneyHiveHoneyHive offers custom pricing plan
Freeplay
Prompt experimentation and evaluation for cross-functional AI teams
Best for: SMB teams · Mid-market · Enterprise
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What is Freeplay?
Freeplay is an operations platform for teams building AI features, covering prompt management and versioning, batch testing, human review workflows and production monitoring. Its emphasis is collaboration between engineers and the domain experts who judge whether output is actually good, giving ...
Read more about FreeplayFreeplay offers custom pricing plan
Maxim AI
End-to-end simulation, evaluation and observability for AI agents
Best for: SMB teams · Mid-market · Enterprise
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What is Maxim AI?
Maxim AI covers the agent lifecycle from prompt experimentation through simulation and evaluation to production observability. Its simulation capability runs agents against generated user personas and scenarios to surface multi-turn failures that single-prompt evaluation misses — an agent can ...
Read more about Maxim AIMaxim AI offers custom pricing plan
Portkey
AI gateway with routing, caching, guardrails and observability
Best for: SMB teams · Mid-market · Enterprise
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What is Portkey?
Portkey is a gateway that sits between an application and model providers, adding routing and automatic fallback across models, semantic caching, rate limiting, guardrails and cost tracking, with observability over every request. Because it sits in the request path it captures traces without ...
Read more about PortkeyPortkey offers custom pricing plan
LangWatch
Open-source agent testing, evaluation and monitoring
Best for: SMB teams · Mid-market · Enterprise
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What is LangWatch?
LangWatch provides testing, evaluation and monitoring for LLM agents, with tracing across agent steps, automated evaluation including scenario-based agent testing, and prompt optimisation using DSPy. It is open-source with a self-hosted option and is framework-agnostic across common agent ...
Read more about LangWatchLangWatch offers custom pricing plan
Galileo
AI observability and evaluation platform to evaluate, monitor, and protect GenAI applications and agents at enterprise scale
Best for: Mid-market · Enterprise
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What is Galileo?
Galileo is an LLM evaluation and observability platform for testing, monitoring, and guardrailing generative AI applications and agents. It captures traces from an application and scores them using its proprietary evaluation models against research-backed metrics, converting offline evals into ...
Read more about Galileo
Braintrust (AI Observability)
The AI observability platform for building quality AI products
Best for: SMB teams · Mid-market · Enterprise
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What is Braintrust (AI Observability)?
Braintrust is an evaluation and observability platform for teams building production LLM applications and agents. It lets engineers log and trace LLM calls with latency, token, and cost metrics, run structured evals against datasets to score output quality, and replay logged production traces ...
Read more about Braintrust (AI Observability)
Athina AI
Evaluation and monitoring for LLM applications with a self-hosted option
Best for: Mid-market · Enterprise
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What is Athina AI?
Athina AI provides evaluation and monitoring for LLM applications, with a library of preset evaluators, support for custom ones, and dataset management for running experiments before shipping a prompt or model change. It offers a self-hosted deployment so prompts and completions stay inside the ...
Read more about Athina AIAthina AI offers custom pricing plan
Confident AI
Evaluation platform built on the DeepEval open-source framework
Best for: SMB teams · Mid-market · Enterprise
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What is Confident AI?
Confident AI is the platform built around DeepEval, an open-source evaluation framework that treats LLM testing like unit testing — assertions over metrics such as faithfulness, answer relevance and contextual precision, runnable in CI. The hosted platform adds dataset curation, regression ...
Read more about Confident AIStarts from $39.00/month when Billed Yearly, also offers free forever plan
PromptLayer
Revolutionize your GPT requests with PromptLayer.
Best for: SMB teams · Mid-market · Enterprise
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What is PromptLayer?
Unveiling PromptLayer, the pioneering platform tailored for prompt engineers. This innovative tool is tailored to tackle the complexities that accompany managing GPT requests, marking a paradigm shift in how you analyze, troubleshoot, and delve into these requests. With PromptLayer, you wield ...
Read more about PromptLayer
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What is Comet?
Comet is a machine learning experiment tracking and model monitoring platform that integrates with any ML library via a single line of code. It covers the full ML lifecycle from experiment management to production monitoring, and works on both local machines and cloud environments. Data ...
Read more about CometStarts from $179.00/user / month when Billed Yearly, also offers free forever plan
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.