PromptWake vs Helicone

you want fast, low-friction logging, cost analytics and caching for the LLM calls your own code makes.

you want the record of what your developers asked AI tools and which lines those tools changed.

Helicone's pitch is friction: change one base URL and you have logging, cost tracking and caching for every model call your application makes. That is a genuinely good trade, and for a team shipping an AI feature it is often the fastest useful instrumentation available.

It also defines exactly what Helicone can see, and the boundary is sharper than with SDK-based tools. A proxy observes the traffic sent to it. Your application's calls go through it because you pointed them there. Cursor's calls go to Cursor's backend, Claude Code's go to Anthropic, Copilot's go to GitHub — none of them read your base URL, and none of them can be pointed anywhere.

If the integration step is 'change your base URL', then the coverage is 'everything whose base URL you control'. AI coding tools are not in that set.

A team adopts Helicone, sees a complete dashboard, and reasonably concludes it now has AI visibility. What it has is visibility into the AI its product uses. The AI that writes the product is invisible — and for most engineering organisations in 2026, that is the larger share of the tokens and by far the larger share of the consequences.

You can check your own exposure in a minute: compare the number of developers with a Cursor or Claude Code subscription to the number of users in your Helicone project. If those are different populations, you are measuring one AI estate and running two.

PromptWake captures on the machine rather than in the network path. The coding tools already write their history to disk — JSONL for Claude Code, SQLite for Cursor and OpenCode, per-workspace sessions for Copilot — and a local daemon reads those files and assembles prompt, response and the resulting file diff into one timeline. Nothing is proxied, no keys are handled, and no traffic is intercepted.

The limitation is the mirror image of Helicone's: we see machines where the daemon runs and nothing where it does not. Neither product covers the other's blind spot, which is why teams that need both usually end up with both. If you are choosing only one, choose by which half of your AI usage you cannot currently explain.