Enterprise AI SaaS: Multi-tenancy, Billing, MLOps

From Prototype to Enterprise: AI SaaS with Multi-tenancy and MLOps

Imagine your AI service processing hundreds of client requests, but p99 latency spikes to 8 seconds and API costs become critical. This is a typical scenario for a SaaS platform grown out of a prototype. We build architectures that withstand enterprise loads: multi-tenant backend, AI Gateway with rate limiting and semantic cache, and auto-scaling in Kubernetes.

The platform must not be a black box for clients. Each customer sees their own dashboard with metrics, billing, and logs. Integration with AI models goes through a single gateway that records every request, tracks cost per tenant, and routes traffic during provider outages. This approach eliminates surprises at the end of the month.

Our stack uses proven technologies: FastAPI, Next.js, PostgreSQL, Qdrant, AWS EKS. But it's not a pile of solutions—it's an interconnected system with clear SLAs. According to Wikipedia, vector databases (Qdrant/Pinecone) provide efficient embedding search, critical for the RAG pipeline.

Core Architecture Overview

The architecture comprises several layers: an AI Gateway (proxy between app and AI providers), a multi-tenant backend with isolated data, a billing system (Stripe), vector storage for RAG, and monitoring/observability. Each layer is independently scalable and configurable per tenant.

How AI Gateway Solves Cost and Reliability Problems

A custom proxy between the application and AI providers is a key component. Functions: rate limiting per tenant, cost tracking, fallback to a backup provider when OpenAI is unavailable, semantic cache (reduces costs by 20–40%), and logging for analysis.

Fallback: if OpenAI responds with 5xx, the request is directed to Anthropic or Azure OpenAI—without time loss. Semantic cache: repeated queries with the same embedding are searched in cache, cutting p99 latency to 200 ms. Average cache savings are substantial for projects with millions of tokens. For example, at a load of 100,000 requests per day, savings reach $2,500 monthly.

rate_limits:
  tenant_1: 100 rpm
  tenant_2: 500 rpm
cache:
  backend: redis
  ttl: 3600
  similarity_threshold: 0.95
fallback:
  primary: openai
  secondary: anthropic
  timeout: 5s

How Data Isolation Is Ensured in a Multi-Tenant AI SaaS Platform

• Namespace isolation: each tenant uses a separate namespace in Qdrant/Pinecone, data is physically separated.
• Fine-tuned models per enterprise: for large clients, we deploy a separate model with LoRA adapters.
• Data residency: restrict data storage by region (EU only, US only) via tenant configuration.

All configurations (model, parameters, custom prompts) are stored in PostgreSQL with row-level security.

Security and Compliance

We implement encryption at rest and in transit, role-based access control (RBAC), and audit logging. For tenants with compliance requirements (SOC 2, HIPAA), we provide dedicated clusters and data processing agreements. Our infrastructure supports VPC peering and private endpoints.

How to Choose Between Self-Hosted Models and API Providers

Self-hosted LLaMA 3 is 10x cheaper than GPT-4o, making it ideal for high-volume applications. Self-hosted models (e.g., LLaMA 3) can be 10–20 times cheaper with comparable quality, but require more DevOps skills. API providers (GPT-4o) offer lower latency and ease of integration, but at higher cost. The choice depends on cost, data control, and scale requirements. We help determine the optimal strategy. Discuss your platform architecture with our engineer—it takes no more than an hour.

Development Pipeline

1. Weeks 1–4: Core infrastructure—auth (Clerk/Auth0), multi-tenancy, basic AI Gateway, first AI function.
2. Weeks 5–9: Stripe billing, remaining core functions, admin panel, usage analytics.
3. Weeks 10–14: Onboarding flow, documentation, API key management, performance optimization.
4. Weeks 15–18: Security audit, load testing, public launch.

What's Included

• Architecture documentation (HLD with diagrams)
• Repository with codebase (backend, frontend, infrastructure as code)
• CI/CD pipeline (GitHub Actions + ArgoCD)
• Monitoring dashboard (Datadog/Grafana) with alerts
• Deployment and operations manual
• Client team training (2 workshops)
• 1-month warranty support after launch

Scaling and Guarantees

Kubernetes with HPA based on 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%. With over 10 AI SaaS projects delivered and 5+ years in MLOps, we bring proven expertise. We have 5+ years on the market, ensuring reliability and innovation.

Order an audit of your current architecture—we'll identify bottlenecks and propose an optimization plan. Get a consultation on architecture and timelines. We'll assess your project in 1–2 days.

Contact us for a detailed consultation to discuss your project.