# GenAI APIs & Backend Integration Architect and harden LLMs inside your stack: model routing, auth, fallback, cost controls, and observability. GenAI APIs wired into your backend. Canonical: https://www.metaborong.com/services/ai/genai-apis-backend-integration Service: ai/genai-apis-backend-integration ## Overview GenAI APIs and backend integration is the engineering of the production LLM layer inside an existing product — model routing, auth, rate limits, fallback paths, cost controls, and observability. The work starts where most LLM features fail: a single provider, no cost visibility, no per-tenant isolation, no eval harness. We engineer LLM integration for products that need AI without losing what works. Senior engineers own the build, India + global delivery. ## What is it? GenAI APIs and backend integration is an engineering engagement that hardens the production LLM layer inside an existing product - gateway, model routing, rate limits, fallback, observability, and evaluation. Builds typically ship in four to ten weeks. Senior engineers own the work end-to-end, delivered from India with global reach. ## What we deliver - Production LLM gateway routing across OpenAI, Anthropic, and open-weights providers - Per-tenant rate limits, cost ceilings, and audit logging enforced at the gateway - Streaming-aware integration in your product with fallback and retry paths - Observability - latency, error rate, cost, drift - wired into your existing dashboards - Eval harness covering your highest-traffic prompts and workflows - Runbook for incident response, model deprecation, and provider switching ## How we work 1. **Architecture and audit** We review the existing LLM surface - provider choices, prompt code paths, error handling, cost trajectory, tenant isolation. Failure modes are catalogued: provider outages, model deprecation, rate-limit cascades, cost spikes, prompt injection. The architecture spec for the gateway, routing, and observability layer comes out of this phase, scoped to your stack and compliance posture. 2. **Gateway and routing** We build the LLM gateway - a thin layer in your stack that handles auth, routing across providers, retries, fallbacks, and rate limits. Per-tenant ceilings are enforced at the gateway, not in application code. Streaming, structured outputs, and tool calling work uniformly across providers so application code does not branch per model. 3. **Observability and evals** Latency, error rate, cost, and drift land in your existing observability stack - Datadog, Sentry, or whatever you already operate. The evaluation harness covers your highest-traffic prompts and workflows, runs in CI, and gates production deploys. Cost trends are tracked per tenant and per workflow so finance gets live visibility, not surprise invoices. 4. **Rollout and handover** The gateway rolls out behind a feature flag, with traffic shifted incrementally from the legacy path. We close the engagement with documentation, a runbook covering model deprecation and provider switching, and three weeks of co-maintenance. Existing AI features keep shipping throughout; the integration work happens around them, not as a stop-the-world rewrite. ## Tech stack OpenAI (Models), Anthropic (Models), Hugging Face (Open-weights), Vercel AI SDK (Streaming), Datadog (Observability), Sentry (Error tracking), PostgreSQL (Audit logs), Redis (Rate limits) ## When this fits ### Fits when - You have a product already shipping LLM features and the cost or reliability is breaking down. - You need fallback across providers because uptime, latency, or pricing is hitting your roadmap. - Your team needs per-tenant cost and rate-limit visibility before scaling traffic up further. ### Does not fit when - You do not have an existing product yet - start with a build engagement, not an integration one. - You want a brand-new copilot or agent - that is a different leaf with its own scoped engagement. - You expect the integration to fix poor model selection or untuned prompts on its own - it will not. ## FAQ ### Why route across multiple model providers in the first place? Single-provider products fail in three ways - outages, deprecation, and cost. Routing across OpenAI, Anthropic, and open-weights gives you a fallback path when a provider has an incident, a migration path when a model is deprecated, and pricing leverage as the market shifts. The gateway makes provider choice an operational lever, not a code change. ### How do you handle prompt injection and abuse at the gateway? Input validation, output schema enforcement, per-tenant rate limits, and structured tool boundaries. The gateway logs every request with tenant attribution, so abuse patterns surface in observability. We do not promise to defeat every novel injection technique - we engineer the layers that close the most common attack surfaces and instrument the rest for review. ### Do you handle compliance - SOC 2, GDPR, India DPDP? We engineer for the compliance posture your product already operates under. PII handling, tenant isolation, audit logging, and data-residency choices are architecture decisions, not afterthoughts. Where data must not leave a region, the gateway enforces that at routing. Where consent is required, the application surfaces it and the gateway enforces it. ### Can you integrate without rewriting our existing AI features? Yes. The gateway lands behind a feature flag and traffic shifts incrementally from the legacy path. Application code changes are small - typically a different client import. Existing features keep shipping throughout. We do not do stop-the-world rewrites unless the buyer asks for one and the timeline supports it.