Langfuse vs Helicone (2026): Which LLM Observability Tool to Pick

If you are choosing an LLM observability platform in 2026, one common decision is Langfuse vs Helicone. This post compares them head to head. For how Langfuse stacks up against LangChain’s native option, see our LangSmith vs Langfuse comparison, and for the full landscape including Braintrust and Portkey, see our Langfuse vs LangSmith vs Braintrust vs Helicone vs Portkey roundup.

The short answer

• Langfuse - pick this if you want deep application tracing, evaluations, prompt management, and datasets through an SDK or OpenTelemetry instrumentation. Best when you need a full picture of multi-step chains and agents, and you are willing to add tracing code to get it.
• Helicone - pick this if you want the fastest-to-integrate proxy-based logging of requests, responses, cost, latency, and usage. Best when you want instant spend visibility plus gateway features like caching and rate-limiting with almost no code change.
• Both - used together when you want Helicone as the proxy gateway for instant cost tracking across all calls and Langfuse for structured tracing and evals on the flows that matter.

The rest of this post unpacks that decision in detail.

Deciding factor to pick

Match your priority to the recommendation. This is the Langfuse vs Helicone decision in one table:

If you only remember one rule: Langfuse is the SDK-instrumented tracing and eval platform, Helicone is the proxy-based instant logging and gateway layer.

What each tool is

• Langfuse is an open-source LLM engineering and observability platform covering tracing, evaluations, prompt management, and datasets. You instrument your application through its SDK or OpenTelemetry, giving you deep, structured visibility into chains, agents, and tool calls. It is self-hostable with an Apache or MIT-licensed core plus a managed Langfuse Cloud tier.
• Helicone is an open-source LLM observability platform that integrates primarily as a proxy. A one-line base-URL change routes your LLM calls through Helicone, which logs requests and responses and tracks cost, latency, and usage, while adding gateway features like caching, rate-limiting, and prompt tooling. It also offers async and SDK options, and is self-hostable alongside a managed cloud tier.

Langfuse vs Helicone: head-to-head

When to choose Langfuse

Pick Langfuse when:

• You need deep, structured tracing of multi-step chains, agents, and tool calls, not just request-level logs.
• You want built-in evaluations and dataset experiments as part of the platform, with scores written back to traces.
• Prompt management and versioning belong at the center of your workflow.
• Your instrumentation should sit outside the request path - you prefer SDK or OpenTelemetry hooks over a proxy.
• You are building a continuous evaluation loop that samples production traffic and grades it.
• You want a single platform that covers tracing, evals, prompts, and datasets end to end.

When to choose Helicone

Pick Helicone when:

• You want the fastest possible integration - a one-line base-URL change and you are logging.
• You need instant cost, latency, and usage visibility across every LLM call with almost no code.
• You want gateway features like caching to cut spend and rate-limiting to protect upstream providers.
• Your priority is spend tracking and operational control more than deep multi-step trace analysis.
• You want observability that works the same way regardless of framework, since the proxy sits in front of the API.
• You need to ship logging today and can layer richer tracing on later.

Can you use them together?

Yes, and it is a sensible pattern when you want both instant cost control and deep trace depth. The split we see:

• Helicone as the gateway - route every LLM call through the Helicone proxy for instant cost, latency, and usage tracking across all traffic, plus caching and rate-limiting that apply uniformly.
• Langfuse for depth - instrument the flows that matter most with the Langfuse SDK or OpenTelemetry for structured tracing of chains and agents, dataset experiments, and evaluations.

You get Helicone’s low-friction spend visibility on everything and Langfuse’s eval and tracing depth where it earns its keep. For the wider set of options including Braintrust and Portkey, see our Langfuse vs LangSmith vs Braintrust vs Helicone vs Portkey roundup. Most teams eventually pick one primary system of record to keep dashboards consistent and avoid double-storing the same data.

Cost comparison

Neither tool’s software cost is the real driver - both are open-source and free to self-host, so the question is self-hosted infrastructure versus managed cloud fees.

• Langfuse is free as open-source if you self-host; you pay only for your own compute and storage. There is also a managed Langfuse Cloud tier with a free starting plan and usage-based pricing above it.
• Helicone is likewise free as open-source and self-hostable, with a managed Helicone Cloud tier that starts free and scales with usage. Its caching feature can directly reduce LLM spend by serving repeated requests without hitting the provider.

At low volume, both are inexpensive and the managed tiers are the convenient choice. At high volume, self-hosting either tool controls cost better than usage-based SaaS, so the decision usually comes down to features and integration effort rather than price. Standard cost controls apply to both: sample evaluation rather than scoring 100% of traffic, lean on caching where requests repeat, and set sensible retention windows.

Common pitfalls

• Expecting deep agent traces from a proxy alone - Helicone logs at the request level by default; for structured multi-step tracing you need SDK instrumentation, which is where Langfuse fits.
• Underestimating Langfuse instrumentation effort - the trace depth is excellent, but you have to add tracing code; it does not come for free from a base-URL swap.
• Putting a proxy in the critical path without a fallback - if every call routes through a gateway, plan for what happens if it is unreachable.
• Running both as full systems of record - paying to store the same data twice. Use Helicone for gateway-wide cost and Langfuse for deep traces, then pick one primary for dashboards.
• Skipping sampling on evaluation - scoring every trace with an LLM judge gets expensive fast. Sample 1-5% of production traffic on either platform.

Related reading

• LangSmith vs Langfuse - LangChain-native observability vs open-source self-hostable tracing
• Langfuse vs LangSmith vs Braintrust vs Helicone vs Portkey - the full five-way observability landscape

Getting help

We deploy Langfuse and Helicone stacks for Series A-C AI startups running production LLM and agent applications. A genai.qa Readiness Assessment delivers a working observability pipeline, calibrated evaluation thresholds, and an audit-grade report in 2-3 weeks. Engagements from AED 15k.

Book a free scope call.