Serverless AI backends built for the edge
Streaming APIs, queues and a hardened Postgres layer on Cloudflare Workers — global latency without a server bill that scales with idle time.
AI features die in production for infrastructure reasons: cold starts, timeouts on streamed responses, unbounded provider spend, and a database that was never given a tenancy model. This service is the plumbing that keeps the feature alive.
What the build includes
- Edge API layer with server-sent streaming and typed request validation.
- Postgres schema with row-level security, migrations and seeded fixtures.
- Queues and scheduled jobs for ingestion, retries and long-running work.
- Provider fallback and per-tenant spend caps with usage metering.
- Origin guards, rate limiting and signed webhook verification on public routes.
- CI/CD, preview deploys, structured logging and uptime monitoring.
Frequently asked
Why build an AI backend on the edge?
Latency and cost. Edge runtimes start in milliseconds, sit close to the user, and bill per request rather than per idle hour — which matters when most of the wall-clock time in an AI request is spent waiting on a model provider.
What are the limits of edge runtimes?
No native binaries, no long-lived processes and a per-request CPU budget. Heavy work moves to queues or a container step. I design around those limits up front instead of discovering them the week before launch.
How is data kept secure?
Postgres row-level security as the primary boundary, server-side keys only, signed webhooks with verified callers, origin checks on public routes and least-privilege service credentials. Nothing sensitive ever reaches the browser bundle.
Do I own the infrastructure?
Yes. Everything lives in your cloud and GitHub accounts from the first commit, deployed by CI, with a runbook and architecture decision records so another engineer can take over without me.
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Want a scoped quote? Email daniyal@agenticcore.tech — I reply within one business day.