How to choose between Langfuse, LangSmith, Braintrust and Helicone

Four tools come up in almost every “what should we use for LLM observability and evals?” conversation: Langfuse, LangSmith, Braintrust and Helicone. They overlap enough to be confusing and differ enough that the choice matters. Here’s a framework rather than a verdict - the right answer depends on your team.

Tooling and pricing change fast. Treat the specifics below as a starting point and verify current details on each vendor’s site (linked from the Tools directory). Listings here are editorial, not paid.

First, separate the two jobs#

These tools sit across two adjacent jobs:

• Observability - capturing traces, latency, token cost and failures in production.
• Evaluation - datasets, scoring and regression testing.

All four touch both, but their centre of gravity differs. Knowing which job is your priority narrows the field fast.

Where each leans#

• Langfuse - open-source, self-hostable tracing + evals + prompt management. Strong fit if you want one tool across the lifecycle and the option to keep data in your own environment.
• LangSmith - tracing and evals with deep ties to the LangChain ecosystem. Natural if you already build on LangChain; capable standalone too.
• Braintrust - evaluation-first: experimentation, scoring and comparing prompt and model versions. Reach for it when rigorous evals are the priority.
• Helicone - proxy/gateway-first: drop-in logging, caching and cost tracking with minimal integration. Fastest path to “I can see my spend and requests.”

A decision framework#

Ask these in order; each one narrows the choice:

1. Do you need to self-host / keep data in your environment? If yes (regulated, sensitive data), the open-source, self-hostable options move up the list. Check the Self-host and data attributes in the directory.
2. Is your priority observability or evals? Spend-and-trace visibility first → a gateway-style tool. Rigorous, release-gating evals first → an eval-first tool.
3. What’s your integration budget? A proxy you point your base URL at is minutes of work; SDK-based tracing is richer but more invasive. Match to how much you can change right now.
4. Ecosystem fit? Already deep in a framework? The native option saves glue code.
5. Open source vs managed? OSS gives control and self-hosting; managed gives less ops. Several here offer both an OSS core and a hosted cloud.

Don’t over-optimise the choice#

Two things matter more than picking the “best” tool:

• Instrument something this week. The cost of no observability dwarfs the difference between any two of these. You can migrate later - they all export traces.
• Avoid lock-in where you can. Prefer tools that speak OpenTelemetry or let you export your data, so switching stays cheap.

A reasonable default path#

• Small team, want to see spend and traces fast → start with a proxy-style tool (Helicone) and add evals later.
• Want one open-source tool across trace + eval + prompts, possibly self-hosted → Langfuse.
• Evals are the priority and you want strong experimentation → Braintrust.
• Already building on LangChain → LangSmith.

Whatever you pick, the goal is the same: every request traced, every change evaluated. Browse all options with their attributes in the Tools directory, and pressure-test your setup against the Production Checklist.