Requesty
Observability

Open any request, down to the token.

Cost, tokens, latency, cache and finish reason on every call through the gateway. Nothing to instrument, no SDK to install.

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recorded at the gatewaylive dashboardssession replay
logs / req_8c14f0a2
200 · 4.28 s
200team: paymentsfeature: invoice-extract14:22:09 UTC
modelanthropic/claude-opus-5
policypolicy/prod-chainfallback 2 of 3
tokens18,402 in / 1,286 out
cached13,910 tokens75.6% of input
ttft612 ms
total4.28 s
cost$0.0391after cache
finishstop
Setup

One integration, not two

Most teams run a gateway and bolt an observability SDK beside it. The gateway already sees every request, so the telemetry comes from where the traffic already is.

  • No SDK in your appNothing to version, nothing to upgrade
  • Any languageThe record is made at the gateway, not in a client
  • IncludedSame account, same dashboard, no second bill
before
2 integrations
Your app
Observability SDK
Gateway or provider SDKs
AI providers
two vendors, two bills, one more SDK to keep current
after
1 integration
Your app
Requesty
AI providers
the record is made where the traffic already is
Cost analytics

See where the money goes

Spend over time by origin, model, team, user or key, and by any metadata you attach. The same view that shows a cost spike shows which route caused it.

analytics / cost by origin
last 8 weeks
Total$4,909.55
AI Agents$3,134.36 63.9%
Claude Code$1,707.17 34.8%
Open WebUI$51.37 1%
Claude Cowork$16.65 0.3%
originmodelteamuserkeyyour metadata
Sessions

Replay a whole session

Requests are stitched back into the session they came from. You see where a run turned expensive or went wrong, not one row at a time.

  • Turn by turnPrompt, response, finish reason
  • Every tool callMost of an agent run is tool calls. They are all here.
sessions / sess_4b81d0
3 of 8 turns are tool calls
01usersummarise this contract
02assistantrequested 2 tools
03toolfetch_document()
04toolextract_clauses()
05assistantsummary returned
06userwhat is the termination clause?
07toolsearch_document()
08assistantanswer returned
tokens 8,075session cost $0.024tool calls 3duration 3m 04s
Performance

Latency you can act on

Time to first token is what the person waiting feels. The dashboard splits it from total latency per model and per provider, so a regression points at a route.

performance / time to first token
open data, Apr 2026
Azure
593 ms960 ms
Vertex (Gemini)
1.29 s3.05 s
Vertex (Claude)
1.44 s8.03 s
Anthropic
2.14 s5.87 s
xAI
3.27 s5.68 s
What to do with it

Numbers are only useful if you can act on them

Spend sits next to the caps that contain it. Latency sits next to the policies that route around it.

See the next request you send

Change the base URL, and the dashboard fills in on its own.

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