Langfuse vs LangSmith vs Braintrust vs Helicone (2026): The Definitive Comparison

Q: What is the main difference between Langfuse and LangSmith?

The core distinction is ecosystem alignment versus infrastructure independence. LangSmith is optimised for LangChain and LangGraph — teams using these frameworks get automatic instrumentation with no SDK code required. Langfuse is framework-agnostic and MIT-licensed, meaning it works equally well with direct OpenAI API calls, custom Python stacks, or TypeScript applications, and can be fully self-hosted to meet data residency requirements. LangSmith's per-trace pricing also becomes significant above two to three million daily traces, whereas Langfuse self-hosted has no per-trace cost.

Q: Is Langfuse genuinely free to self-host?

Yes. Langfuse is released under the MIT licence, which means the complete product — including all features available on the managed SaaS — is available to self-host at no licence cost. The cost of self-hosting is purely infrastructure: a PostgreSQL instance, a ClickHouse cluster for analytics, and a Redis instance for caching. At high trace volumes (tens of millions per month), this infrastructure cost is typically 60 to 80 percent cheaper than per-trace SaaS pricing. The trade-off is the DevOps overhead of operating three database systems.

Q: What is Braintrust best used for?

Braintrust is best used as a CI/CD-native evaluation platform — the tooling that blocks deployments when LLM quality scores fall below defined thresholds. Its primary workflow is experiment comparison: running a new prompt version or model version against a golden dataset, comparing scores to previous baselines, and making a go/no-go decision before release. It is not the right tool if your primary need is real-time production monitoring or live paging on quality regressions.

Q: Can I use Helicone alongside another observability platform?

Yes, and this is a valid pattern for early-stage applications. Helicone's proxy approach captures cost and latency data at the HTTP level with zero SDK integration, making it useful as a lightweight cost monitor while a more capable platform handles nested tracing and evaluation. However, as applications mature towards multi-step agent architectures, the gaps in Helicone's depth — no nested tracing, no evaluation, no prompt management — typically become blockers that motivate migrating the primary observability responsibility to Langfuse or LangSmith.

Q: Which platform is best for teams with GDPR or data residency requirements?

Langfuse self-hosted is the only platform in this comparison that provides a complete self-hostable solution under a permissive open-source licence. LangSmith offers enterprise on-premises deployment but this requires a commercial agreement. Braintrust and Helicone are SaaS-only. For organisations under GDPR Article 28, APPI, or MAS TRM constraints that prohibit sending production LLM traces to third-party processors, Langfuse self-hosted is the standard recommendation.

Q: How does Langfuse's Prompt Registry work?

The Prompt Registry is a centralised store for production prompt templates, versioned and managed through the Langfuse UI. Applications retrieve the current production prompt at runtime via SDK call without any code change or redeployment. This means prompt engineers can update, test, and roll back prompts through the Langfuse UI while every production trace records which prompt version was active — enabling exact attribution of quality changes to specific prompt edits.

Q: What does migration from Helicone to Langfuse involve?

Helicone's proxy approach means there is no application-level instrumentation to remove — only the client configuration changes. Migration involves removing the Helicone base URL override, adding the Langfuse SDK, and adding @observe() decorators to key application functions. For a simple prompt-response application, this typically takes two to four hours. For a complex agent pipeline with many tool functions, identifying and decorating all the relevant call sites takes longer but does not require any changes to business logic.

Q: Is LangSmith worth it if I am not using LangChain?

The honest answer is: probably not, when Langfuse is available as a free alternative. LangSmith's primary competitive advantage is automatic instrumentation of LangChain and LangGraph components. Without that stack, you are manually adding SDK instrumentation regardless of which platform you choose — at which point the comparison reduces to pricing and features, and Langfuse's self-hosted option, its Prompt Registry, and its ClickHouse-backed scale are meaningful advantages that LangSmith's per-trace pricing does not offset for most custom-stack teams.