AI & CLOUD RELIABILITY
Uptime, engineered for services and AI alike.
SLOs, error budgets, and incident response that cover cloud services and AI inference together, from engineers who carry the pager. Reliability becomes a promise you keep, not a number you hope for.
SLO: API & Inference Availability (30d)
99.99%
ON TRACK
Error Budget
7.2% remaining
0%100%
Burn Rate (past 1h)1.8x
Error Budget Consumption (30d)Incident
May 4May 11May 18May 25Today
Incidents (30d)
3
MTTR
28m
The Problem
Reliability tends to be reactive and defined at 'good enough'. With AI in the path, it's worse: inference latency, model drift, and token limits add new failure modes most monitoring never sees. Outages cost trust and revenue every time.
What We Do
- SLOs and error budgets for services and AI endpoints
- Observability across metrics, logs and traces, including inference latency and quality
- Incident response and on-call that actually works
- Reliability baked into delivery, not bolted on after
How It Works
- 1Define SLOsSet targets and error budgets, for APIs and inference alike
- 2InstrumentCollect signals across the stack, from load balancers to model outputs
- 3DetectSpot early, reduce noise
- 4Respond & learnFix fast, learn faster, for every system you run
Outcomes
- 99.99% uptime targets tied to error budgets
- Faster incident recovery (lower MTTR)
- AI and cloud reliability measured the same way
Reliability is what keeps a shipped system shipped. how our forward-deployed engineers take AI from pilot to production
Keep production up, on purpose.
Talk to an engineer