How I'd build API Gateway AI
I'd reach for Next.js on the frontend, FastAPI in the backend with Postgres for the data layer, Stripe for billing, and Caddy as the actual gateway proxy. I'm estimating 560 hours to reach production, which breaks down roughly as: auth and multi-tenant isolation (40 hours), billing infrastructure (35 hours), the core gateway logic (60 hours), the AI rate-limiting engine (80 hours), deployment and observability (50 hours), and buffer for integration testing and customer onboarding flows (50 hours). That's roughly 14 weeks of focused work, or two months if you need me part-time.
Day-by-day plan
- Week 1: Provision Postgres schema for tenants, users, API keys, and usage tracking. Set up Next.js frontend scaffold with Clerk for auth. Wire JWT validation on the FastAPI backend.
- Week 1-2: Integrate Stripe Billing Platform. Implement usage metering via meter events. Tier-gate features: free tier (100k requests/month), $149 tier (1M requests + priority support), $299 tier (unlimited + multi-region).
- Week 2-3: Build the core gateway proxy in Caddy, fork it to accept custom middleware. Write the request inspection layer that captures headers, payload size, origin IP, and rate-pattern metadata without logging the full body (privacy-first).
- Week 3-4: Ship the AI rate-limiting engine. Use Claude API to score incoming requests against learned abuse patterns. Cache the model output for 5 minutes to avoid inference on every request. Add a feedback loop where users can label false positives to retrain the classifier.
- Week 4: Add comprehensive logging to Postgres. Write Prometheus exporters for request volume, latency, and blocked requests. Set up Grafana dashboards visible to both operators and customers.
- Week 5: Build customer onboarding with a walk-through video, quickstart guide, and example curl calls. Deploy to AWS Lightsail with Terraform. Set up 30-day on-call rotation for the first month.
What's hard about this build
The hardest part is proving the AI actually reduces false positives without real production data yet. We'll start with a heuristic-based baseline (IP reputation, payload size anomalies, request frequency) and use Claude API to score against that baseline, but the feedback loop that makes this valuable takes weeks to build signal. Second is request latency. Adding AI inference to the hot path introduces tail latency we can't afford. I'd mitigate this by batching inference requests and accepting a 200ms decision window for blocking, but that's a trade-off. Third is privacy and compliance. Inspecting and logging API traffic triggers HIPAA, PCI, and GDPR concerns even for metadata. I'd implement request sampling (log 1-in-10 requests after filtering out sensitive patterns) and clearly document what we retain. Finally, integration testing is messy because real API patterns vary wildly. We'd need at least 2-3 paying beta customers running traffic through production before we can validate the AI actually works.
What's fast because of AI
Claude's code generation cuts scaffolding from a week to a day. I'd generate the FastAPI CRUD endpoints, the Postgres migrations, and the Stripe webhook handlers all at once, then iterate on the business logic. Test case enumeration is the other big win: I can ask Claude to enumerate edge cases for a multi-tenant billing system (free-tier downgrade, refunds, overage handling) and generate pytest fixtures for all of them, saving maybe 40 hours. UI copywriting and onboarding flow is surprisingly fast too. Rather than workshop product copy with a marketing hire, I'd draft the signup form labels, error messages, and email templates with Claude and iterate once with a user. Debugging is the last one: when a customer reports a rate-limiting false positive, Claude helps me trace the request pattern and enumerate which heuristics fired, compressing a 2-hour debugging session into 30 minutes.
How I'd hand it off
I'd record a Loom walkthrough of the entire deployment pipeline and the Stripe test environment. I'd leave a runbook in Linear with clear SOP steps for customer onboarding, incident response (false-positive review), and monthly billing reconciliation. I'd stay on-call for 30 days post-launch, with a defined escalation path: customers hit a Slack channel for support, you route critical issues to me, I resolve them same-day or loop in your team if it's structural. All credentials go into a shared 1Password vault with docs on which service each one is for. At the 30-day mark, we'd do a handoff meeting where I walk you through the code layout and the observability setup, then you take ownership.