Comparison
Helicone vs Keybrake
Helicone and Keybrake look adjacent ("proxy, logs, cost dashboard") but aim at different halves of the agent stack. Helicone observes LLM traffic; Keybrake enforces policy on SaaS-API traffic. The head-to-head for 2026.
Quick verdict
- Choose Helicone if: your primary problem is "I need to see what my LLM calls are costing and returning" — Helicone's caching, prompt management, and per-user dashboards are the category-leading answer.
- Choose Keybrake if: your primary problem is "I need to stop my agent from overspending on Stripe / Twilio / Resend" — policy enforcement, USD caps, endpoint and customer allowlists, mid-run revoke, parsed-cost audit.
- Choose both if: your agent does inference AND money-moving tool calls. Run Helicone in front of OpenAI, Keybrake in front of Stripe/Twilio/Resend, join logs on
x-agent-run-id.
Side by side
| Helicone | Keybrake | |
|---|---|---|
| Category | AI observability proxy | SaaS-API governance proxy |
| Stance | Observability-first (chart-driven) | Governance-first (policy-driven) |
| Vendors | OpenAI, Anthropic, Azure OpenAI, 40+ LLMs | Stripe, Twilio, Resend (+ roadmap) |
| Cost source | Token counts × price table | Parsed from vendor response |
| Pre-flight enforcement | Rate limits, alerts | Daily USD cap, endpoint allowlist, customer scope, param rules — enforced before forwarding |
| Mid-run revoke | Disable key; next request 401s | Flip vault_key to revoked; median < 5s on next request |
| Caching | Exact-match + semantic | None (SaaS calls are mutating) |
| Prompt management | Yes — versioning, playground, A/B | N/A |
| Audit log shape | Prompt / completion / tokens / cost / user / tags | Vendor / endpoint / params / parsed cost / policy result / run ID |
| Hosting | Cloud + self-host (OSS) | Cloud (self-host on roadmap) |
| Starting price | Generous free tier | Free 1k req/mo; Team $99/mo; Scale custom |
Detailed differences
Different centre of gravity
Helicone's home screen is a dashboard. Keybrake's home screen is a policy editor with a kill-switch. That difference is not cosmetic — it reflects what each tool is optimising for. Helicone surfaces patterns in past traffic; Keybrake prevents certain traffic from happening at all. For money-moving APIs, "prevent" is the primitive you need; for LLM inference, "surface and summarise" is usually enough.
Different cost-accounting shape
Helicone's per-request cost is tokens × model-price. Keybrake's is vendor.response.amount (Stripe), vendor.response.price (Twilio), or a flat rate (Resend). Helicone's schema has no room for "what customer was charged" — Keybrake's does, because that's how agent-to-SaaS incidents are actually triaged.
Different audit-trail consumer
Helicone's audit is consumed by the engineer reconciling OpenAI spend or debugging a prompt regression. Keybrake's audit is consumed by the ops-risk engineer — or, increasingly, a compliance reviewer asking "which customer did the agent charge under which policy on 2026-04-15?" The rows are shaped for that second question.
Running both
Agent config carries two base URLs and two keys. Both receive x-agent-run-id. Log joins on that column post-hoc. See the longer positioning piece for the diagram.
Try Keybrake
Helicone stays the right answer for LLM observability. Keybrake handles the other half — Stripe, Twilio, Resend. Free tier covers 1,000 proxied requests/month.