AI SaaS Platform Development

AI SaaS Platform Development

AI SaaS is not just a wrapper around OpenAI API. It's a product with multi-tenancy, billing, reliable inference backend, rate limiting, observability, and UX that hides ML complexity from end users. We build the complete stack.

AI SaaS Architecture

AI Gateway Layer: Own proxy between application and AI providers. Functions: per-tenant rate limiting, cost tracking, fallback (if OpenAI unavailable — switch to Anthropic/Azure OpenAI), caching (semantic cache reduces costs 20–40%), logging for analytics.

Multi-Tenancy:

• Isolation: separate vector stores (namespaces in Pinecone/Qdrant), separate fine-tuned models per enterprise tenant
• Configuration per tenant: model choice, parameters, custom prompts, whitelist/blacklist
• Data residency: optional data storage region constraints

Billing & Usage: Stripe for subscription management. Token-based billing (tracking via AI Gateway). Soft/hard limits. Usage dashboard for user. Overage alerts.

Core AI Features: Depends on product type. Typical set: text generation, document QA (RAG), summarization, translation, code generation. Each function — separate endpoint with independent scaling.

Development Pipeline

Weeks 1–4: Core infrastructure: auth (Clerk/Auth0), multi-tenancy, basic AI gateway, first AI feature.

Weeks 5–9: Billing (Stripe). Remaining core features. Admin panel. Usage analytics.

Weeks 10–14: Onboarding flow, documentation, API keys management. Performance optimization.

Weeks 15–18: Security audit, load testing, public launch.

Scaling

Kubernetes with HPA (Horizontal Pod Autoscaler) by CPU/memory and custom metrics (inference queue depth). GPU pods for self-hosted models with node autoscaling. Target metrics: p99 latency <2 sec, uptime 99.9%.