Calling an AI API in a demo is straightforward. Doing it in a system that handles real traffic, with a budget, uptime requirements, and a team reviewing your pull requests, is a different problem entirely.
This event is built for engineers who have already integrated an AI API and are now dealing with the consequences: inconsistent latency, unpredictable costs, brittle prompt logic, and no clear way to monitor what the model is actually doing in production.
The core problems addressed
We examine observability first, because you cannot improve what you cannot measure. That means logging completions without leaking sensitive data, tracking latency percentiles across providers, and building dashboards that surface model drift before users notice it.
Cost governance in multi-tenant systems
Cost allocation becomes genuinely complex when multiple teams or customers share a single API key. We cover per-tenant token accounting, budget alerts, and the architectural decisions that make cost rollback possible without a rewrite.
Fallback routing, provider redundancy, and caching strategies for deterministic prompts are covered in the second half of the event. These are not theoretical patterns; each one is demonstrated with a working implementation and a discussion of its failure modes.
- Observability and completion logging
- Latency profiling across providers
- Per-tenant cost accounting
- Fallback routing and provider redundancy
- Prompt versioning and regression testing