A Helicone alternative — for the traffic Helicone can't see

What Helicone does well

Helicone (helicone.ai, Y Combinator W23) is an observability-first proxy for LLM endpoints. It drops in as a base-URL change on your OpenAI / Anthropic / Azure SDK and gives you, in exchange, four things:

• Structured request logs — every prompt, every completion, token counts, latency, user-id and custom-property tags.
• Cost and usage dashboards — per-user, per-model, per-application spend with filtering by time range and tag.
• Caching — exact-match and semantic caching on prompts, configurable TTL, cache-hit rate on the dashboard.
• Prompt management — versioned prompt templates, playground, A/B testing, webhook alerts on cost anomalies.

All four operate on one kind of traffic: LLM inference calls with OpenAI-compatible request/response schemas. If your agent's concern is "where did my OpenAI budget go this week", Helicone is likely already doing the right job. Keybrake is not trying to take that seat.

Why Helicone can't see your Stripe traffic

Helicone's architecture is built around the OpenAI request/response shape. Cost accounting reads token counts from the response. Caching hashes the prompt. The dashboard's schema assumes model, prompt_tokens, completion_tokens. None of that maps to a POST /v1/charges to Stripe, where the cost is parsed from the amount field of a charge object, the "prompt" doesn't exist, and there is no reasonable notion of caching a payment. Helicone could, in principle, add Stripe support — but it would be a categorically different product surface. The Helicone roadmap, correctly, has stayed focused on LLM observability.

Observability vs governance: an important split

Helicone is observability-first — the core promise is "see what your LLM is doing". It has caps and alerts, but they're layered on top of the logging primitive; the product's center of gravity is the dashboard.

Keybrake is governance-first — the core promise is "stop your agent from doing the wrong thing on Stripe". Observability (audit log, parsed-cost reporting) is a by-product, not the headline. That difference shows up in the UX: Helicone's home screen is a chart; Keybrake's home screen is a policy editor with a kill-switch.

When the incident is "my agent burned $4,000 on Stripe in 15 minutes", the governance-first stance is the one you want. Observability after the fact is useful but not sufficient.

Keybrake vs Helicone — side by side

When Helicone is still the right answer

• Your #1 concern is LLM cost reporting. "Which user's prompts cost the most last week?" — Helicone's dashboards answer that faster than anything else out there.
• You need prompt versioning and A/B testing. Keybrake has no opinion on your prompts; Helicone ships a genuine product surface for it.
• You want caching. Keybrake deliberately doesn't cache. Helicone's semantic cache is a real win on repeat-prompt workloads.

Running both: what the dual-proxy setup looks like

A production agent typically ends up with two base URLs in its config: Helicone in front of OpenAI/Anthropic, Keybrake in front of Stripe/Twilio/Resend. Both carry a shared x-agent-run-id header. Both logs are queryable; a per-run join tells you "run run_abc spent $1.20 on GPT-4o tokens and $247 on Stripe charges". Neither proxy knows about the other. Each does the observability-or-governance job for its category.

agent
 ├─ OpenAI-base-url → https://oai.helicone.ai/v1         (Helicone auth header)
 └─ Stripe-base-url → https://proxy.keybrake.com/stripe  (vault_key_…)
                      x-agent-run-id flows through both

Concrete next step

If you already use Helicone for LLMs, adding Keybrake is a two-line config change: swap api.stripe.com for proxy.keybrake.com/stripe, and the Stripe secret for a Keybrake vault_key_…. Attach a daily USD cap. If the agent also hits Twilio and Resend, repeat.

Further reading

• Helicone vs Keybrake (side-by-side) — same compare, table-first.
• What belongs in an AI agent audit trail — the schema we write per call, and why those fields (parsed cost, policy result, run ID) are the right shape for incident triage.
• Agent kill-switches — 4 patterns with measured stop-latency — revoke vs network-block vs flag vs HITL, per vendor.
• Stripe restricted keys — 10-control coverage matrix — what Stripe's native feature does and doesn't cover.