Traceloop vs Helicone: LLM Observability at Agent Scale

Prices updated November 6, 2026.

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 Apache-2.0 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.

Pricing and ownership. ServiceNow acquired Traceloop in March 2026. The only self-serve plan is a free tier with up to 50K spans per month and 24-hour retention; everything above that, including on-prem deployment (AWS, GCP, Azure, Kubernetes, or air-gapped), is an Enterprise conversation.

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 available as a managed cloud service, and its pricing page lists on-prem deployment under the Enterprise plan.

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 Enterprise on-prem option means you can keep request bodies inside your own infrastructure if that's a compliance requirement.

Pricing and ownership. Hobby is free for 10k requests per month with 7-day retention, Pro is $79/month with 1-month retention, and Team is $799/month with 3-month retention; usage beyond that is billed on a usage basis, with no published per-request rate. Mintlify acquired Helicone in March 2026 and put it into maintenance mode (security fixes, new model support, and bug fixes only). Helicone's pricing page now says it is "no longer part of Mintlify" with details still to come, so treat current ownership and roadmap as unsettled.

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 discussed here handle observability, routing or evaluation, and all of them act once the LLM call has been made. noburn acts before it: one check() in your own code, placed before the provider call, returns allow or block against a project, per-user or per-run cap, so an over-budget call is never sent. It is not a proxy: your OpenAI or Anthropic client and its connection stay as they are. How the caps resolve, and the reason each one returns, is on the homepage.

Per-user caps and usage quotas keep one customer from spending another's budget, and Pro adds passthrough billing through Stripe. The Free plan covers 10,000 recorded calls a month with no card (current plans: pricing). Setup takes one install and one check: the quickstart, then per-user budgets and agent runs in the SDK reference.