How We Build Secure and Customizable AI Platforms for B2B Clients

We design and implement multi-tenant AI infrastructure that handles from 10 to over 1000 B2B clients while maintaining data isolation, performance, and customization flexibility. In 3–5 months, we build the platform from scratch or migrate an existing one — turnkey, with documentation and team training. Typical investment ranges from $50,000 to $150,000.

Common Pain Points in Building an AI SaaS Platform

Data isolation is the primary headache. If one tenant accidentally accesses another's model, it means reputation loss and legal risks. Row-Level Security in PostgreSQL solves this at the DB level but doesn't protect against ML artifact leaks. We use S3 prefixes plus IAM policies for each tenant.

The second block is performance under growth. Shared schema is cheaper, but with 100+ tenants query latency increases. Without proper indexing on tenant_id, queries slow down. We design sharding upfront and use connection pools with tenant-aware routing.

The third is AI customization for each client. Tenants want their own prompts, models, and limits. Without a TenantAwareInferenceService, administration becomes chaos. Request a consultation — we will help you build the right architecture.

Isolation Models and Methods Comparison

For AI workloads, the second option is optimal: Shared DB + Separate Schema for transactions plus separate S3 prefixes for ML models. This provides a balance between cost and flexibility. Infrastructure savings reach up to $10,000 per month for 50+ tenants.

How to Ensure Data Isolation Between Tenants?

We use Row-Level Security in PostgreSQL. Each query is automatically filtered by tenant_id. Example policy:

-- Enable RLS for tenant data isolation
ALTER TABLE predictions ENABLE ROW LEVEL SECURITY;

-- Policy: each tenant sees only their data
CREATE POLICY tenant_isolation ON predictions
    USING (tenant_id = current_setting('app.current_tenant_id')::UUID);

Middleware on FastAPI sets the tenant context for each request (see code below). This guarantees that no query "leaks" between tenants.

# FastAPI middleware to set tenant context
@app.middleware("http")
async def tenant_context_middleware(request: Request, call_next):
    tenant_id = await resolve_tenant(request)
    request.state.tenant_id = tenant_id

    async with db.acquire() as conn:
        await conn.execute(
            f"SET LOCAL app.current_tenant_id = '{tenant_id}'"
        )
        request.state.db_conn = conn
        response = await call_next(request)

    return response

Tenant-Specific AI Configuration

@dataclass
class TenantAIConfig:
    tenant_id: str
    allowed_models: list[str]
    system_prompt_override: str = None
    monthly_token_limit: int = 1_000_000
    concurrent_request_limit: int = 10
    custom_models: list[str] = None
    prediction_log_retention_days: int = 90
    pii_detection_enabled: bool = True
    audit_log_enabled: bool = True

class TenantAwareInferenceService:
    async def predict(self, tenant_id: str, model_name: str,
                       inputs: dict) -> dict:
        config = await self.get_tenant_config(tenant_id)

        if model_name not in config.allowed_models:
            raise PermissionError(f"Model '{model_name}' not allowed")

        if not await self.rate_limiter.check(tenant_id, config.concurrent_request_limit):
            raise RateLimitError("Concurrent request limit exceeded")

        if config.system_prompt_override and 'system' in inputs:
            inputs['system'] = config.system_prompt_override + "\n\n" + inputs['system']

        if config.pii_detection_enabled:
            inputs = await self.pii_detector.redact(inputs)

        result = await self.inference_engine.run(model_name, inputs)

        await self.audit_log.record(tenant_id, model_name, inputs, result)

        return result

What's Included in the Work (Deliverables)

• Documentation: Full architecture docs, API reference, deployment guide, runbook.
• Access: Source code repository, CI/CD pipeline, monitoring dashboards (Grafana + Prometheus).
• Training: 2-day hands-on workshop for your team, plus recorded sessions.
• Support: 3 months of post-launch support (SLA-based), with optional extended maintenance.
• Code artifacts: All configuration files, Dockerfiles, Kubernetes manifests, Terraform scripts.

Work Process and Scope

1. Analytics — audit of current infrastructure, defining isolation and scale requirements.
2. Design — DB schema, API contracts, stack selection (PyTorch, LangChain, PostgreSQL, S3).
3. Implementation — coding, RLS setup, creating TenantAwareInferenceService, integrating LLMs (GPT-4, Claude, LLaMA), fine-tuning, vector DBs (ChromaDB, pgvector), RAG pipelines.
4. Testing — load tests, pentesting for data isolation. We achieve 99.9% uptime and p99 latency <200ms.
5. Deployment — CI/CD, monitoring (Grafana + Prometheus), documentation.
6. Support — SLA, refinements for new requirements.

Our engineers have 5+ years of MLOps experience and 20+ implemented AI platforms. We use proven solutions: PostgreSQL RLS, Kubernetes, vLLM for inference. We guarantee compliance with GDPR and 152-FZ.

class TenantOnboardingService:
    async def provision_tenant(self, signup_data: dict) -> Tenant:
        tenant = await self.db.create_tenant(signup_data)
        await self.db_manager.create_schema(tenant.id)
        await self.db_manager.run_migrations(tenant.id)
        await self.storage.create_tenant_prefix(tenant.id)
        await self.config_store.create_default_config(tenant.id)
        api_key = await self.auth.create_api_key(tenant.id, scope="all")
        await self.email.send_welcome(tenant, api_key)
        return tenant, api_key

Typical Mistakes in Multi-Tenant Implementation

1. Lack of tenant-aware caching — cache from one tenant could serve data to another. Use tenant_id as part of the cache key.
2. Weak isolation at the application level — filtering by tenant_id in code rather than at the DB level risks accidental leaks. Always combine RLS with middleware checks.
3. Wrong choice of multi-tenancy model — for a small number of tenants, shared schema works, but as it grows, latency spikes. Plan for potential migration to separate schema without downtime.

Why Our Architecture Is More Cost-Effective?

Compare: Shared DB + Separate Schema is 3–5 times cheaper than a separate database per tenant when serving 50+ clients. Infrastructure savings reach up to $10,000 per month for 50+ tenants. And performance — p99 latency below 200ms even with 1000 concurrent requests (thanks to connection pooling and per-tenant indexes). Return on investment occurs within 6 months after launch.

Timeline and Cost

Development takes from 3 to 5 months depending on the complexity of AI modules and the number of tenants. The exact cost is determined after an audit — contact us for a consultation and get a preliminary assessment of your project. Typical budget is $50,000–$150,000.