LLM Eval Tools Compared 2026 - Langfuse, LangSmith, Braintrust, Arize, Humanloop, HoneyHive
Every existing comparison of these tools is published by one of the six vendors. This is a neutral side-by-side comparison with honest “avoid if” notes. No tool is the best for every team - the right choice depends on your stack, team size, and budget.
Some tool links on this page are affiliate links. Rankings are not influenced by affiliate relationships. Ranking methodology: OSS availability, self-host option, eval feature depth, CI integration quality, pricing transparency, and honest weakness assessment.
At-a-Glance Matrix
| Tool | OSS / Cloud | Free tier | Self-host | Best for | Avoid if |
|---|---|---|---|---|---|
| Braintrust | Cloud | Yes (generous) | No | Developer experience, CI integration, prompt management | You need on-premise data residency or want to avoid vendor lock-in on the dashboard |
| Langfuse | OSS + Cloud | Generous (cloud) + Free (OSS) | Yes - full | Self-hosting, cost control, teams that want to own their data | You need the best-in-class CI integration or prompt management UX (Braintrust is stronger here) |
| LangSmith | Cloud | Limited | No | Teams already using LangChain or LangGraph | Your stack does not use LangChain - instrumentation is much more work |
| Arize Phoenix | OSS + Arize AI (cloud) | Yes - OSS is fully free | Yes - full (OSS) | Production monitoring, tracing, multimodal evaluation, embedding analysis | You just need offline eval - Langfuse or Braintrust are simpler for that use case |
| Humanloop | Cloud | Trial only | No | Prompt management, non-technical team collaboration on prompts | You are an engineering team that wants programmatic control - the API is less developer-first than Braintrust |
| HoneyHive | Cloud | Trial | No | Agent observability, multi-step trace debugging, agentic workflow evaluation | You are evaluating a simple single-turn LLM call - HoneyHive is overkill |
| Weights & Biases (Weave) | Cloud | Yes (personal use) | No | Teams already using W&B for ML training, want unified ML + LLM observability | You are not using W&B for ML training - the LLM eval features alone do not justify the platform |
| Confident AI / DeepEval | OSS + Cloud | Yes - OSS fully free | Yes - eval runs locally | Engineering teams, CI integration, pytest-style eval, OSS-first | You need a rich dashboard for non-technical stakeholders - the UI is functional but not polished |
| Pricing as of April 2026. Vendor pricing changes frequently. Some links are affiliate links. | |||||
Tool Deep Dives
Braintrust
Braintrust is built by ex-Stripe and ex-Anthropic engineers with a strong developer-experience focus. The SDK (Python + TypeScript) is clean, the CI integration is first-class, and the prompt playground is excellent. Dataset versioning and experiment tracking are the strongest in class for offline eval. The main limitation is cloud-only - no self-host option and no on-premise. Production tracing exists but is less mature than Arize or Langfuse for online monitoring.
Langfuse
Langfuse is the most mature open-source LLM observability and eval platform as of April 2026. Self-host via Docker Compose or Helm chart in under 30 minutes. Strong tracing (OpenTelemetry-compatible), decent eval functionality, and an active community. The cloud version is hosted on EU/US infrastructure with GDPR compliance. The main limitation: the eval UX is less polished than Braintrust, and the CI integration requires more manual setup.
LangSmith
LangSmith is the observability and eval platform built by LangChain, Inc. If you are using LangChain, adding LangSmith is a one-line change that gives you automatic trace capture, latency tracking, and token counts. The eval features work with any model API. Outside the LangChain ecosystem, the instrumentation is more manual and the developer experience is not as smooth as Braintrust or Langfuse. Cloud-only, US/EU options.
Arize Phoenix
Arize Phoenix is the OSS version of Arize AI's production monitoring platform. It installs as a Python package and runs a local UI server. Extremely strong for tracing, embedding drift analysis, and hallucination detection in production. The eval API supports Ragas metrics natively and integrates with LlamaIndex, LangChain, and direct API calls. The Arize AI cloud version adds real-time monitoring, alerting, and team features. Best choice for teams that need both offline eval and production monitoring with a single tool.
Humanloop
Humanloop started as a prompt management platform and has added eval functionality. The standout feature is the prompt editor designed for non-technical users - product managers and domain experts can edit and test prompts without touching code. The eval features are solid but not as deep as Braintrust or Langfuse. Best for mixed engineering/non-engineering teams where multiple stakeholders need to manage prompts. YC-backed, growing quickly.
HoneyHive
HoneyHive is purpose-built for agentic workflows - multi-step chains, tool calls, and agent loops where debugging requires understanding the full trace of actions, not just input and output. Strong agent-specific evaluation features: step-by-step trace analysis, tool-use attribution, and cost-per-step breakdown. Newer than Langfuse or Braintrust; feature set is growing. Best for teams building complex agent pipelines who need to understand why an agent took a particular path.
Weights & Biases (Weave)
Weights & Biases added LLM evaluation and tracing as 'Weave' in 2024. For teams already using W&B for training run tracking, Weave is a natural extension - unified dashboards for model training metrics and LLM eval results. The eval features are solid but not as mature as Langfuse or Braintrust. The main value is the W&B ecosystem integration, not the LLM eval features in isolation.
Confident AI / DeepEval
DeepEval is an open-source Python eval framework with a pytest-style API. Evals are written as Python functions, run locally or in CI, and optionally push results to the Confident AI cloud dashboard. Supports a wide range of metrics: G-Eval, faithfulness, relevancy, hallucination, contextual precision/recall, RAGAS metrics, and custom LLM-as-judge metrics. The strongest choice for engineering teams that want maximum control, no cloud dependency, and integration with existing Python test suites.
Decision Trees
I want OSS, self-hosted, no vendor lock-in
- 1.Langfuse - most feature-complete OSS
- 2.Arize Phoenix - if production monitoring is primary
- 3.DeepEval - if you want pytest-style eval in CI with no cloud
I want the best developer experience and CI integration
- 1.Braintrust - first choice
- 2.DeepEval - if you need it fully local
- 3.LangSmith - if you are on LangChain
I am on LangChain and want zero-friction setup
- 1.LangSmith - auto-traces LangChain with one env var
- 2.Langfuse - good LangChain integration, plus self-host option
When You Don't Need a Tool
If you have fewer than 100 test examples and no production traffic, a Python script and a CSV is fine. Write your golden dataset in a spreadsheet, run inference in a loop, call the LLM judge API, and append results to a CSV. The total setup time is 2-3 days. The API cost per full run is $10-50.
A dedicated tool earns its cost when you have: (a) regression tracking across model versions that requires a database to manage, (b) team collaboration on prompt versions where multiple people need visibility, or (c) production traces at scale where you need sampling, alerting, and aggregation. For most teams before product-market fit, the CSV approach is the right starting point.