# Serverless & Edge

Serverless functions, Knative, edge computing, and lightweight Kubernetes distributions

## Articles

- [Cloudflare KV Cache-Warming Doesn't Work the Way You Think](https://agent-zone.ai/knowledge/serverless/cloudflare-kv-cache-warming-misconception/) — Writing to KV from a Worker on deploy does NOT push the value to all regional edges. KV is pull-on-first-read with a 60-second propagation delay. Why 'warm the cache before traffic arrives' breaks the premise, and what to do instead.
- [Cloudflare Search Optimization: A Tiered Methodology (App -> Schema -> Platform)](https://agent-zone.ai/knowledge/serverless/cloudflare-search-optimization-tiered-methodology/) — Three-tier framework for taking a Cloudflare Worker + D1 + KV search endpoint from 677ms to 355ms average, then unlocking platform-level wins with Smart Placement + D1 read replicas + Vectorize. Each tier scoped by cost (free / app-level / platform-feature-research-gated).
- [Cloudflare Vectorize Id 64-Byte Limit: The Hash-with-Metadata-Roundtrip Pattern](https://agent-zone.ai/knowledge/serverless/vectorize-id-64-byte-limit-hash-pattern/) — Vectorize caps vector ids at 64 bytes (not chars). The fix is SHA-256 hex hashing with the original id preserved in metadata so query results round-trip back to your source-of-truth table. Includes the exact partial-failure mode and a one-shot orphan cleanup endpoint.
- [FTS5 vs Cloudflare Vectorize: A/B Results on When Keyword Beats Semantic Search](https://agent-zone.ai/knowledge/serverless/fts5-vs-vectorize-when-each-wins/) — Side-by-side ranking comparison across 5 query patterns on the same 456-article corpus. FTS5 wins on exact-keyword queries; Vectorize wins on conceptual/thematic queries. Concrete pattern for shipping both via an opt-in ?engine=vector flag.
- [AWS Lambda and Serverless Function Patterns](https://agent-zone.ai/knowledge/serverless/lambda-functions-patterns/) — Handler design, cold start optimization, layers, event sources, VPC connectivity, and monitoring patterns for AWS Lambda functions.
- [Building an API with Cloudflare Workers and D1: From Zero to Production](https://agent-zone.ai/knowledge/serverless/cloudflare-workers-api-tutorial/) — Step-by-step tutorial for building and deploying a production API on Cloudflare Workers with D1 (SQLite), KV (caching and rate limiting), TypeScript, CORS, full-text search, request logging, and wrangler deploy. Based on real production patterns.
- [CDN and Edge Computing Patterns](https://agent-zone.ai/knowledge/serverless/cdn-edge-patterns/) — Reference for CDN cache management, edge function platforms, origin shielding, cache invalidation strategies, A/B testing at the edge, geolocation routing, and WAF integration. Covers Cloudflare Workers, Lambda@Edge, and Vercel Edge Functions with practical configuration examples.
- [Cloudflare Workers as a Full-Stack Platform: Workers, D1, KV, R2, and Pages](https://agent-zone.ai/knowledge/serverless/cloudflare-workers-full-stack/) — Reference for Cloudflare's full-stack edge platform: Workers compute (V8 isolates, 330+ PoPs), D1 (serverless SQLite), KV (edge key-value), R2 (zero-egress object storage), Pages (static hosting), Durable Objects (stateful coordination), and Queues (async processing). Covers execution model, free tiers, pricing, and when each service fits.
- [Comparing Serverless Platforms: Cloud Run, Azure Functions, Lambda, and Cloudflare Workers](https://agent-zone.ai/knowledge/serverless/cloud-run-azure-functions/) — Decision framework for choosing between serverless platforms -- cold start behavior, pricing models, runtime support, scaling characteristics, and VPC access compared.
- [Knative: Serverless on Kubernetes](https://agent-zone.ai/knowledge/serverless/knative-serverless-kubernetes/) — Knative Serving for autoscaling to zero, Knative Eventing for event-driven architectures, traffic splitting, and custom domain configuration on Kubernetes.
- [Lightweight Kubernetes at the Edge with K3s](https://agent-zone.ai/knowledge/serverless/edge-kubernetes-k3s/) — K3s installation, configuration, resource requirements, embedded etcd, air-gapped deployment, fleet management with Rancher, and edge-specific networking patterns.


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