Helicone
LLM observability via drop-in proxy
Overview
Helicone provides LLM observability via a one-line proxy URL change — drop-in instrumentation for OpenAI, Anthropic and any OpenAI-compatible API. Logs requests, tracks costs, enables caching, exposes prompt-engineering analytics. Open-source core with a hosted SaaS tier; popular with seed-stage AI startups.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 19 May 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
Use cases
What you can produce with Helicone
- Add full request logging to an existing OpenAI or Anthropic integration by changing one line — the API base URL — with no SDK to install.
- Track LLM spend broken down by user, feature or environment using custom property tags on each request.
- Enable response caching on repeated prompts to cut token costs and latency without touching application logic.
- Set per-user rate limits at the proxy layer to stop a single account from burning your API budget.
- Compare prompt versions side by side with logged production traffic to see how a prompt change affects cost, latency and output.
- Self-host the entire open-source observability stack for data-residency or compliance requirements.
- Monitor error rates and latency across every LLM provider you use from a single dashboard.
ASEAN Perspective
Helicone in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
Helicone is a developer-focused observability platform for LLM apps, providing request logging, tracing, cost and latency tracking, caching, and prompt management with a one-line proxy or async integration. It is open-source and self-hostable, with a generous free tier, making it an easy, low-friction add to any LLM stack across providers. It suits engineering teams that want visibility into spend and behaviour without heavy instrumentation.
Caveats: the proxy-based integration adds a network hop some teams prefer to avoid (the async route mitigates this), and deeper evaluation/experimentation features are less mature than dedicated eval platforms. It is purely a builder tool. Good docs and SDKs. Cloud or self-hosted, globally usable from ASEAN; self-hosting helps with any data-residency needs.
What people say
Helicone's story changed materially in March 2026: the YC W23 open-source LLM observability company was acquired by Mintlify, and the hosted product is now officially in maintenance mode — security patches, bug fixes and new-model support continue, but Mintlify has confirmed no new features, integrations or roadmap. By acquisition time Helicone had processed over 14 trillion tokens across roughly 16,000 organisations, and Mintlify says it is helping customers migrate. Any adoption decision today has to start from that fact.
What made Helicone popular is still true of the product as it stands. Users consistently describe it as the lowest-friction path to LLM cost and usage visibility: one line of code — swapping your API base URL to route through Helicone's proxy — instantly gets you request logging, per-user and per-feature cost tracking, latency and error dashboards, caching and rate limiting across OpenAI, Anthropic and 100+ models. Developers on Reddit and Hacker News repeatedly praised exactly this instant-setup quality, and the open-source core (5,800+ GitHub stars) meant self-hosting was always an option.
The criticisms predate the acquisition and remain: Helicone is request-level observability, not deep agent tracing. Teams debugging complex multi-step agent workflows found it shallow compared to trace-first tools, and the proxy architecture itself — a hop in your critical path — gave some production teams pause. Post-acquisition, the dominant conversation in developer communities is migration, with comparison guides proliferating.
Who does it still fit? Self-hosters comfortable running the open-source stack and teams needing quick, provider-agnostic cost tracking on a project with a short horizon. For new long-lived production deployments, the maintenance-mode status makes Helicone hard to recommend over actively developed alternatives — which is a genuine loss, because the product earned its reputation.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including Helicone's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Helicone unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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