Helicone vs Langfuse for LLM observability

Most teams evaluating LLM observability hit the same fork: do you want a drop-in proxy or an instrumentation toolkit? The Helicone vs Langfuse decision usually comes down to whether you prioritize zero-code integration or deep traceability across multi-step agents.

Architecture: Proxy vs SDK

Helicone intercepts traffic. You point your OpenAI (or Anthropic) client at Helicone’s proxy URL and attach an auth header. Every request flows through their edge, gets logged, and forwards to the provider. Streaming responses are proxied byte-for-byte, so your existing SSE code works unchanged.

from openai import OpenAI
client = OpenAI(
    base_url="https://oai.hconeai.com/v1",
    api_key="sk-your-openai-key",
    default_headers={"Helicone-Auth": "Bearer hc-your-key"}
)
resp = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role":"user","content":"hi"}],
    stream=True
)

Langfuse instead gives you an SDK. You wrap generations, spans, and events in your own code. It does not sit in the network path; it sends traces asynchronously to a collector over HTTP. You control exactly what gets recorded.

from langfuse import Langfuse, ModelUsage
lf = Langfuse()
gen = lf.generation(name="gpt-call", model="gpt-4o", input=[{"role":"user","content":"hi"}])
# your LLM call here
gen.end(output="hello", usage=ModelUsage(input=10, output=5))

The architectural split dictates everything else: Helicone sees every byte automatically; Langfuse sees what you instrument.

Capabilities

Helicone covers proxy concerns: request logging, latency percentiles, cost aggregation, rate limiting, caching, and retry. You tag requests with custom properties (Helicone-Property-$key headers) and group by session. Dashboards and Slack alerts ship out of the box.

Langfuse goes broader on workflow tracing. Its object model is trace > span > generation. You can nest spans for retrieval, tool calls, and reasoning steps. It includes datasets for eval, prompt versioning, a playground, and scoring (human or LLM-judge). For a 12-step agent, Langfuse’s tree view shows where latency and tokens accumulated; Helicone shows 12 separate proxied calls with no inherent parent linkage unless you stamp session IDs yourself.

If you need to cap spend on a single endpoint, Helicone’s proxy rules win. If you need to debug prompt regressions across versions, Langfuse’s eval suite wins.

Price and Cost Model

Helicone uses subscription tiers. The free tier allows a limited number of requests per month; paid plans scale by volume and unlock longer retention, custom roles, and webhook routing. Self-hosting is possible via their open-core repo, but you pay for compute and ops.

Langfuse is open-source under MIT. Self-host on your own Postgres and Redis at zero license cost. Langfuse Cloud bills on observability events (generations, spans, events) with a free quota; beyond that it is usage-based per million events.

Neither charges a percentage of your LLM provider spend. Both meter your usage separately for your own dashboards.

Latency and Throughput

Helicone adds a synchronous network hop. In practice overhead is low (edge locations, sub-50ms typical), but it is a hard dependency: if Helicone’s proxy is degraded, your requests fail unless you build fallback logic in your client.

Langfuse logs asynchronously. The LLM call returns without waiting on Langfuse; the SDK queues and flushes in background. Throughput impact is negligible, but traces appear eventually—usually within seconds, occasionally delayed under load.

For high-throughput batch jobs (millions of generations), Langfuse’s fire-and-forget is safer. For low-latency interactive apps, Helicone’s proxy is fine if you trust its uptime SLA.

Ergonomics

Helicone is a two-line change for any OpenAI-compatible client. That is the whole pitch. You keep your existing code; you swap the base URL and add a header.

Langfuse requires instrumentation. Simple calls need a generation wrapper; complex flows need span hierarchies and explicit .end() calls. The SDK is clean, but it is still code you maintain and can forget to update when refactoring.

If you already use a gateway such as n4n.ai—which exposes one OpenAI-compatible endpoint for 240+ models with automatic fallback and per-token metering—Helicone’s proxy slots in cleanly because it just replaces the base URL, while Langfuse would trace the outbound calls from your gateway integration.

Ecosystem and Integrations

Helicone supports OpenAI, Anthropic, Cohere, and custom HTTP endpoints via proxy. It exposes a GraphQL API and webhooks for usage events. Langchain users get a callback handler that auto-forwards runs.

Langfuse ships SDKs for Python, JS/TS, and Rust. It integrates with Langchain, LlamaIndex, Haystack, and OpenTelemetry. Its prompt management API can serve prompts to your app directly, versioned and A/B tested.

Helicone’s strength is provider breadth through the proxy. Langfuse’s is framework depth through SDKs and standards like OTel.

Limits and Gotchas

Helicone’s free tier caps requests and retention. Complex traces (multiple sub-calls) are flattened unless you use custom session grouping headers. Non-HTTP workloads (e.g., raw websocket streaming) may need extra config.

Langfuse self-host demands Postgres, Redis, and worker maintenance. Cloud retains traces for a limited window on lower tiers. Instrumentation drift—forgetting to close a span—can leave orphaned traces that skew metrics.

Comparison Table

Which to Choose

Prototype a single LLM feature fast: Use Helicone. Change the base URL and you have dashboards in minutes.

Run multi-agent workflows in production: Use Langfuse. The span tree and eval datasets will save you during incidents.

Need strict data residency and have ops capacity: Self-host Langfuse. You control the database and avoid third-party proxy.

Already behind an inference gateway: If you route through a unified endpoint, Helicone’s proxy is the path of least resistance. Langfuse still works but requires wrapping your gateway client calls.

Cost control on a tight budget: Helicone’s free tier may suffice for low volume; Langfuse OSS is free forever if you run the stack.

The Helicone vs Langfuse choice is not about which is better—it’s about whether you want a proxy or a tracer. Pick the one that matches your architecture today, and revisit when agent complexity grows.