What it does well

Helicone is the open-source LLM observability platform that logs every prompt, completion, token, and latency metric from your AI app. Drop in their proxy, and you get a full dashboard, cost tracking, user analytics, and prompt playground. Free tier: 100K requests/mo; Pro $20/mo for 1M requests. In 2026, it is a serious option for anyone in this category. We tested it for two weeks on real work — here is the honest take on what is great, what is broken, and whether it is worth your money.

Who it is for: Engineering teams shipping LLM-powered features who need to know what is working, what is expensive, and what is breaking in production.

Key features

One-line proxy Change your OpenAI/Anthropic base URL to Helicone — no SDK required

Change your OpenAI/Anthropic base URL to Helicone — no SDK required, all models supported.

Full request logs See every prompt

See every prompt, completion, latency, token count, cost, and error in a searchable dashboard.

User analytics Track per-user cost

Track per-user cost, usage patterns, and identify power users and abuse.

Prompt playground Test prompt variations against

Test prompt variations against real production logs and replay them to compare outputs.

Custom properties Tag requests with custom metadata (user_id

Tag requests with custom metadata (user_id, feature, environment) and slice the dashboard by any dimension.

Self-host option Open-source under Apache 2.0 —

Open-source under Apache 2.0 — deploy on your own infrastructure for compliance or cost.

The honest take

✓ What works

One-line integration (literally change the base URL) makes the time-to-first-insight minutes, not days
Open-source and self-hostable — no vendor lock-in, your data lives where you put it
Cost tracking per user/feature is the killer feature for any team optimizing a paid AI product
Generous free tier (100K requests) covers most early-stage startups indefinitely
Works with every LLM provider (OpenAI, Anthropic, Gemini, Mistral, self-hosted) through the same proxy

✗ What does not

Self-hosting requires Kubernetes or Docker Compose — not a 5-minute setup like managed
Pricing is request-based, not token-based — long prompts/completions are charged the same as short ones
Advanced features (alerts, A/B testing) are still maturing compared to Langfuse
No built-in evaluation framework (you will pair it with Langfuse or Braintrust for LLM evals)
Dashboard can feel overwhelming for very small teams with <100 requests/day

Verdict

Helicone is the easiest way to get visibility into your LLM application in 2026. If you are shipping an AI product and you do not know your per-user cost, your error rate, or your slowest prompt, you are flying blind. Helicone fixes that in 10 minutes. The open-source license and self-host option are real differentiators if you have compliance requirements. For most teams, the managed version is the right starting point — it costs less than your first OpenAI bill but pays for itself the first time you catch a runaway loop or a prompt regression.

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