Traceloop and Helicone both sit in the LLM observability category — capture every call, track cost and latency, and give you a dashboard to see what your application is actually doing. Where they diverge is architecture: one is built on OpenTelemetry as an instrumentation layer, the other is a proxy that sits in front of your API traffic. That difference matters more than it sounds once you're running agents, where a single user action can fan out into dozens of LLM calls across multiple tools and models.
Traceloop
Traceloop is built around OpenLLMetry, an open-source OpenTelemetry instrumentation library for LLM applications. It auto-instruments calls to OpenAI, Anthropic, Cohere, and most major providers, plus frameworks like LangChain and LlamaIndex, and exports standard OpenTelemetry traces and spans. Because it speaks OTel natively, you can route the data to Traceloop's own platform or to any existing observability backend you already run (Datadog, Honeycomb, Grafana Tempo) without a second vendor relationship.
Architecture. Instrumentation-based, not a proxy. Your SDK calls still go directly to the model provider; OpenLLMetry wraps the call to capture the trace and ships it out of-band. This means no added network hop and no latency penalty on the request path, which matters for agent loops where every hop compounds.
What it's strong at. Trace-level visibility into multi-step agent chains — seeing the full tree of a request as it moves through retrieval, tool calls, and sub-agent invocations, which a flat request log doesn't represent well. Because it's OpenTelemetry-native, it composes cleanly with whatever the rest of your infrastructure already uses for tracing.
Key limitation. OpenLLMetry captures what happened; it doesn't intervene in the call before it fires. There's no budget enforcement or per-user spend cap at the instrumentation layer — you'd need to build that separately even with full trace visibility in place.
Helicone
Helicone runs as a proxy: you point your existing OpenAI or Anthropic client at Helicone's endpoint (a one-line base URL change), and it logs, caches, and can rate-limit every request as it passes through. It's open source and self-hostable, or available as a managed cloud service.
Architecture. Proxy-based. Every request routes through Helicone before reaching the model provider, which adds visibility (and optional caching) at the cost of a network hop most requests didn't need to take.
What it's strong at. Built-in response caching (repeated identical prompts can be served from cache instead of re-billed), and rate limiting configured directly at the proxy layer rather than in application code. The self-hosted option means you can keep request bodies inside your own infrastructure if that's a compliance requirement.
Key limitation. The proxy hop is a new dependency in the request path — an outage or latency spike in Helicone's infrastructure (or your self-hosted instance) now affects every LLM call your product makes, not just the ones you're actively debugging.
Neither enforces budget before the call fires
Both tools answer "what happened and what did it cost" — after the request has already gone out and been billed. Helicone's rate limiting can throttle request volume, but volume isn't cost: a user under the rate limit can still fire a handful of requests with massive context windows and blow through a budget that request-count limiting never saw coming. Traceloop's tracing tells you exactly which agent step caused the spend, after the fact, which is excellent for debugging but doesn't stop the spend from happening.
For teams running agents specifically — where a single user action can trigger an unbounded number of downstream calls — that gap between "observed" and "enforced" is where runaway cost incidents happen. Neither Traceloop nor Helicone estimates the cost of a call before it fires and blocks it if a user or project is over budget; that requires a separate enforcement layer regardless of which observability tool you're running underneath it.
Where noburn fits
The tools compared in this article handle observability, routing, or evaluation — all of which operate after the LLM call completes. noburn operates before it. It wraps your existing OpenAI, Anthropic, LangChain, and the Vercel AI SDK client, estimates the token cost of each call, and blocks it if the calling user or project has exceeded their budget. Nothing in this comparison does that at a self-serve price point.
Per-user metering lets you enforce separate limits per end-customer, and Stripe passthrough lets you bill them for their LLM usage without writing a billing layer yourself. The free tier covers 100 requests per month. Documentation and SDKs are at noburn.dev/docs.