Braintrust vs Langfuse: Choosing an Eval Platform

Every team building on LLMs eventually hits the same wall: they ship a prompt change, it feels better in testing, and they have no reliable way to prove it didn't regress something else. Braintrust and Langfuse both exist to close that gap, and both show up on nearly every shortlist for "LLM evaluation platform." What rarely gets said clearly is that they were built to solve the problem from opposite ends: one starts at the eval loop and works outward, the other starts at the production trace and works inward. That difference shapes almost everything else about which one fits your team.

What Braintrust actually is

Braintrust is a hosted platform built around the loop of writing a prompt, running it against a dataset, scoring the outputs, and comparing runs side by side. Its own pricing page frames the product around that scale-as-you-go model rather than seat licensing: "Predictable pricing. Designed to scale." according to Braintrust's pricing page. The Starter tier is free with a small allotment of processed data and scores; Pro runs roughly $249 a month with more headroom; Enterprise is custom. All tiers include unlimited users, projects, and experiments, so the meter runs on how much evaluation and trace data you push through the system, not on how many people touch it.

Braintrust is hosted only. There is no open source core and no self-managed deployment option beyond enterprise-negotiated arrangements, so you're trusting their infrastructure with your prompts, datasets, and traces. In exchange you get a genuinely tight loop between experimentation and production: evaluations, datasets, and logs live in the same place, and comparing a candidate prompt against a baseline is a first-class, fast workflow rather than something stitched together from exports.

What Langfuse actually is

Langfuse started as, and remains, an observability tool first. It traces LLM calls, agent steps, and retrieval chains, and evaluation features (scoring, datasets, an experiment view) were added on top of that tracing foundation rather than being the reason the product exists. The practical result is a tool that feels most natural when your question is "what happened in production," and slightly less native when your question is "which of these five prompt variants is best," even though it can answer both.

The bigger structural difference is licensing. Langfuse's core product, tracing, evaluation, prompt management, datasets, and the playground, ships under the MIT license with no usage caps when self-hosted. Langfuse's own self-hosting page is direct about this: "Langfuse is open source and can be self-hosted using Docker on your own infrastructure" (Langfuse self-hosting docs). What stays behind a commercial license is the administrative layer: project-level RBAC, audit logs, SCIM provisioning, data retention policies, and managed ClickHouse hosting, not the evaluation or tracing functionality itself (Langfuse self-host pricing). Langfuse was acquired by ClickHouse in January 2026, which is worth knowing if vendor stability factors into your decision, though it hasn't changed the licensing model as of this writing.

Self-hosting vs hosted-only

This is the single biggest fork in the decision tree, and it's worth being blunt about it before comparing feature checklists.

Braintrust gives you zero infrastructure to run. There's no proxy, no database, no ClickHouse cluster, and no upgrade cadence to manage. That's a real advantage for a small team that wants an eval workflow today without becoming operators of another stateful service.

Langfuse gives you the option to run it yourself, for free, with the full feature set. The tradeoff shows up in operational cost rather than license cost: a self-hosted Langfuse deployment at real production scale means operating Postgres, ClickHouse, Redis, and typically Kubernetes or an equivalent, which is genuine ongoing engineering work, not a one-time setup task. Langfuse Cloud exists specifically for teams that want the open architecture without owning the operations: Hobby is free with 50k units and 30-day retention, Core is $29 a month, Pro is $199, and Enterprise runs $2,499, according to Langfuse's pricing page.

How is llm evaluation platform pricing actually structured

Neither vendor prices like traditional per-seat SaaS, and that's worth understanding before you compare a number on one page to a number on the other. Braintrust meters on processed trace data volume and score count, with unlimited users baked into every tier. Langfuse Cloud meters on "units," a blended measure of ingested events, with unlimited users starting at the Core tier and administrative features gated by tier rather than usage. Self-hosted Langfuse removes the usage meter entirely and replaces it with your own infrastructure bill. If your evaluation volume is spiky, run the actual math against your expected trace volume on both real pricing pages rather than trusting a single comparison table, since both vendors adjust tiers periodically.

Sourced from Braintrust's pricing page, Langfuse's cloud pricing page, and Langfuse's self-host pricing page, September 2026.

Which one actually fits a fast-iterating prompt team

If your day-to-day is prompt engineers and applied ML folks running experiments against a golden dataset, comparing candidate prompts, and gating deploys on eval scores, Braintrust's workflow is built directly around that loop and it shows in how little friction there is between writing an eval and seeing the comparison. Teams that already run everything else self-hosted, or that want CI to block a merge on an eval regression without paying for a hosted eval run, tend to reach for Langfuse's experiment view layered on its tracing, particularly if they're already ingesting OpenTelemetry data from elsewhere in their stack.

Neither is simply better here. A team with strict data residency requirements or an existing self-hosted observability stack has a real reason to prefer Langfuse regardless of how polished Braintrust's eval UI is. A team that wants zero ops and is willing to pay for it has a real reason to prefer Braintrust even though it means trusting a third party with prompt and dataset data.

Where the actual decision comes down to

Ask these questions in order:

• Do you need to self-host for compliance, data residency, or cost-at-scale reasons? If yes, Langfuse is close to the only serious open source option with a genuinely complete feature set at the MIT tier.
• Is your primary workflow "iterate on prompts against a dataset" or "understand what happened to a live request"? The former favors Braintrust's native loop; the latter favors Langfuse's tracing depth.
• Do you already have OpenTelemetry instrumentation elsewhere? Langfuse's ecosystem leans into that; Braintrust's does not require it but doesn't lean into it either.
• What's your actual expected monthly trace volume? Model it against both pricing pages directly rather than a marketing comparison, since usage-based tiers cross over at different points depending on your traffic shape.

Where llm11 fits in this picture

Worth naming plainly: llm11 is not a competitor to either of these, and a post about them shouldn't pretend otherwise. llm11 is a router that sits in front of your model calls and runs inline verification, schema and groundedness checks always, heavier checks like cross-model comparison on requests that earn them, escalating to a stronger model once if a check fails. That happens at request time, on every call, automatically. Braintrust and Langfuse both do something different and complementary: offline dataset evaluation, prompt experimentation, and long-term tracing of what happened across thousands of requests over weeks or months. If you want to know whether a specific answer was likely correct right now, that's llm11's verification layer. If you want to know whether your prompt is better this week than last week, or trace a production incident back to its root cause, that's what these two platforms are for. Many teams that use llm11 for inline verification still run Braintrust or Langfuse alongside it for that longer-horizon evaluation work; the two layers answer different questions and neither replaces the other. For the fuller landscape of tools in this category beyond just these two, our roundup of LLM evaluation tools covers more ground, and the evaluation rubric these platforms help you run is worth reading regardless of which platform you pick. If Langfuse specifically isn't fitting your team, our list of Langfuse alternatives covers the other options in its category, and the wider gateway and router comparison hub lines up several adjacent products at once.

Next step

If you're mid-evaluation, the fastest way to decide is to load a week of your own real prompts and traces into both free tiers and see which loop your team reaches for on day three, not day one. That's a more reliable signal than any feature table, including this one.

Frequently asked questions

Is Langfuse actually free to self-host, or is that misleading?

The core platform, tracing, evaluation, prompt management, datasets, and the playground, is genuinely MIT-licensed with no usage caps when you self-host it. What costs money is the enterprise tier for features like SCIM provisioning, audit logs, and managed ClickHouse hosting, plus the real infrastructure and engineering time to operate Postgres, ClickHouse, and Redis yourself at scale.

Does Braintrust have a self-hosted option?

Braintrust is hosted by default, with on-premises or dedicated deployment available only through custom Enterprise arrangements rather than as a standard self-host path. If self-hosting on your own terms and timeline is a hard requirement, Langfuse's MIT core is the more direct fit.

Can I use Braintrust and Langfuse together?

Some teams do, running Langfuse for always-on production tracing and Braintrust for the prompt experimentation loop where its UI is tightest. It's two subscriptions and two places data lives, so it only makes sense if each tool is covering a distinct, valuable part of your workflow rather than duplicating the other.

Which one is better for LLM observability specifically, not evaluation?

Langfuse, since observability is its origin and evaluation was added afterward, whereas Braintrust's tracing exists mainly to support its evaluation loop rather than standing alone as a full observability product. If observability is your primary need rather than eval iteration, weigh Langfuse against dedicated observability-first tools too, not just against Braintrust.