Helicone puts an AI gateway in the shortlist; Langfuse brings an open-source tracing and evaluation platform. Compare the full system you need to operate.
Platform and AI engineers comparing Helicone with Langfuse
Helicone
Investigate its gateway when standardizing provider access is a core requirement.
Langfuse
Investigate its open-source tracing, prompt, and evaluation workflows when those define your platform needs.
Lunary
Consider a trial when customer conversation context and cross-team investigation should guide the workflow.
At a glance
| Area | Helicone | Langfuse |
|---|---|---|
| Architecture emphasis | AI Gateway with observability | Open-source AI engineering platform |
| Prompt workflows | Gateway-integrated prompt management | Versioning and SDK retrieval |
| MCP data access | Documented observability MCP server | Documented data MCP server |
| Data extraction | ETL and API extraction | UI and blob-storage exports |
| Trial focus | Routing behavior and trace context | Trace, score, and experiment workflow |
List the responsibilities of the platform
Provider routing, trace storage, prompt delivery, and evaluation execution are separate responsibilities even when one vendor supplies several. Identify what your current stack already handles well. Then decide which responsibility the new platform must improve. This prevents a tool comparison from turning into an unnecessary rewrite of your serving architecture.
Compare completeness on a representative task
Choose a task with a retrieval step, two model operations, and a tool error. Instrument the actual execution path, including operations outside the provider request. Review the resulting trace with someone unfamiliar with the code. Check whether they can distinguish a failed tool from a poor model decision, and whether cost and latency are attached to the right operations.
Both have documented data MCP access
Helicone and Langfuse both document MCP servers for working with observability data. Evaluate their exact tools, authentication, and permitted operations for the assistant you want to use. Do not confuse those integrations with MCP tool tracing or documentation search. Each solves a different problem and requires its own acceptance check.
Inspect a real export and a prompt revision
Helicone documents ETL extraction and prompt management. Langfuse documents filtered exports, blob storage, and versioned prompts. In the trial, export the same narrow workload and review a prompt revision. Confirm which payloads, identifiers, and scores are preserved, and verify the prompt that the application actually retrieved.
Write a boundary for the first rollout
Choose one application and identify the serving and telemetry components it depends on. Keep the rest of the stack stable while evaluating the candidate, then widen coverage only after the application owner can use the evidence. If replacing a gateway is part of the plan, document that as a separate milestone with request-level checks. A scoped trial makes it possible to attribute an improvement or failure to the component you actually changed.
Common questions
Can I choose based on MCP support alone?
No. Verify whether you need documentation retrieval, access to stored telemetry, or instrumentation of MCP tool execution. A server advertised as MCP-enabled does not imply all three.
Does choosing Langfuse replace my provider gateway?
Do not assume that. Evaluate gateway behavior separately from observability and keep the request architecture explicit.
Sources & methodology
Lunary publishes this guide. We compare documented workflows and explain where each approach fits; this is not an independent benchmark or a hands-on product rating. Features, limits, and commercial terms can change. Check the linked vendor documentation before deciding.