I'm building a multi-tenant app and need isolated model serving

For a multi-tenant app, “isolated model serving” usually means each tenant gets its own logical or physical model runtime so data, prompts, fine-tunes, and traffic are separated.

A good pattern depends on how strong the isolation needs to be:

1) Lightweight logical isolation

Use one shared inference service, but isolate by tenant in software:

• tenant-specific model config
• tenant-specific adapters/LoRAs
• per-tenant API keys and auth
• request routing by tenant ID
• separate prompt/context stores
• per-tenant logging/redaction

Best when:

• tenants are trusted
• cost efficiency matters
• strict regulatory isolation is not required

2) Dedicated model instances per tenant

Run separate model workers or deployments per tenant:

• separate containers/pods/processes
• separate memory/state
• separate model weights or adapters
• separate autoscaling and quotas

Best when:

• tenants need strong data isolation
• you have premium enterprise customers
• you need per-tenant customization

3) Fully isolated infrastructure

Each tenant gets its own:

• namespace/account/project
• model endpoint
• vector store
• database
• secrets
• network policies
• observability pipeline

Best when:

• compliance is strict
• customers demand hard isolation
• you need clear blast-radius boundaries

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Recommended architecture

A common practical design is:

• API Gateway
  Authenticates tenant, enforces quotas, rate limits, and routes requests.

• Tenant Control Plane
  Stores tenant metadata:

  • model version
  • adapter/fine-tune version
  • allowed tools
  • limits
  • deployment target

• Model Serving Plane

  • shared base model, or dedicated per tenant
  • separate inference workers
  • tenant-scoped caches and memory

• Tenant Data Plane

  • separate DB/schema/bucket/vector index per tenant
  • encryption keys per tenant if needed

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Isolation mechanisms to use

• Authentication & authorization: tenant-scoped JWT/API keys
• Request routing: tenant ID determines model endpoint
• Namespace isolation: Kubernetes namespace or separate service
• Network isolation: VPC, security groups, mTLS
• Storage isolation: separate DB/schema/object prefixes
• Compute isolation: per-tenant pods or GPU slices
• Secrets isolation: separate secrets per tenant
• Logging isolation: redact and partition logs
• Quota enforcement: tokens/sec, concurrency, cost limits

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If you want both cost efficiency and isolation

Use:

• shared base model
• per-tenant adapters
• separate tenant memory/data stores
• separate runtime sandboxing for sensitive tenants
• optional dedicated deployments for enterprise tiers

This gives a good cost/security balance.

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Key design question

What level of isolation do you need?

• Soft isolation: separate tenant data in a shared service
• Hard isolation: separate deployment/runtime per tenant

If you want, I can help you design:

1. a Kubernetes-based isolated serving architecture,
2. a DB/schema strategy for tenant isolation, or
3. a reference implementation in your stack.