Holds ~17 req/s on short steps (2.7 min), fails at ~29 req/s
- Breaks by
- 28.5 req/s
- Slowed first
- Add to cart
- Latency bound
- p95 ≤ 1.5 s
We run the agreed test and return the measurements, the result of your chosen rule and the limits of what was observed. You and your AI read them with your knowledge of the application.
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Holds ~17 req/s on short steps (2.7 min), fails at ~29 req/s
Why these numbers hold up
Your logs stay useful. A Run adds what the client side saw, under a load you agreed in advance.
Pricing
Every Run shows its exact dollar price before you confirm it and is paid from your Account's balance. Credits never expire.
A new Account starts here with $5 of welcome credit.
After a credit pack is bought or CapacityLab grants credit.
$19 of Run credit for $19
Sales are closed during the beta, so there is nothing to buy yet. Pricing details.
Talk through your workload
Bring a target endpoint, a traffic question, or a Railway bill you do not trust yet.
One controlled loop
The application stays the target. Test infrastructure stays in support.
You attach CapacityLab as a tool in your own AI agent and grant only the scope it needs. The connection runs over MCP (Model Context Protocol), an open protocol for exactly this.
Your agent turns the question into a workload profile: target, endpoints, traffic shape and rate.
Register the exact endpoint that may receive load — staging by default, production only if you decide so.
We send the bounded workload from our generator; no request leaves it without a registered target.
You follow your own infrastructure while the load runs, on your usual dashboards, and can stop the Run.
You get the latency measured at the load generator, the result of your chosen rule and its observation limits. Why the application behaved so is for you and your agent to judge; HTTP measurements alone do not establish an internal root cause.
Traceable by design
Registered targets only
Register the endpoint that may receive load and get the capacity decision together with its evidence.