Scaling & Production Deployment
Taking AI from prototype to production systems serving real users at scale
Session Topics
Prototype to Production
The gap between a Colab notebook and a production service — what changes at scale and why most prototypes never survive it.
Infrastructure Options
API-based services (OpenAI, Anthropic) vs self-hosted open-source models — cost models, latency, privacy, and control trade-offs.
Scaling Strategies
Horizontal scaling, caching (semantic caching, prompt caching), batching, async processing, and load balancing for AI workloads.
Cost Optimisation
Token budgeting, model tiering, caching strategies, and batch inference — cutting AI costs by 60–90% without sacrificing quality.
Observability & SLAs
Latency p50/p95/p99, error rates, token throughput, and setting realistic SLAs for AI APIs that are non-deterministic.
Scale Your Hiring AI to Production
Design a complete production deployment strategy for your hiring AI system, scaled to 1 million users and 100K requests per day. Make real decisions on infrastructure, deployment, monitoring, and organisation.
A production deployment design document with architecture, cost estimates, scaling strategy, and monitoring plan.
Key Takeaways
- Production AI fails in ways prototypes don't — plan for failure from day one
- Semantic caching is the highest-ROI optimisation for most AI APIs
- Self-hosting open-source models is cost-effective above ~10M tokens/day
- Observability is not a luxury — it is the only way to improve what you can't see