The operational side of running AI systems in production: deployment patterns, cost management, scaling decisions, and the observability stack you need to keep things working after launch.
What you will find here
- Deployment patterns — containerisation, model serving, batching strategies, and blue-green deployment for embedding pipelines.
- Scaling trade-offs — when to scale vertically versus horizontally, index partitioning, and read replica patterns for vector stores.
- Cost management — embedding costs, inference costs, storage costs, and where to apply caching to reduce each.
- Observability — structured logging, distributed tracing, and the metrics that matter most for AI workloads.
- Operational incidents — common failure modes, runbooks for index corruption, and recovering from bad deploys.
Articles here assume you already have a working system and are focused on making it reliable, affordable, and easy to operate.