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Key Takeaways

  • Multi tenant AI SaaS is mostly security and ops.

  • Start with shared collections plus strict tenant filtering.

  • Store binaries in S3, store metadata and workflow state in MongoDB.

  • Use managed identity and least privilege IAM.

If you are turning an AI workflow into a SaaS product, the hardest parts are usually tenancy, security, and operations. This post outlines a practical multi tenant architecture for AI enabled apps built on AWS with MongoDB.

Tenancy models

Common models:

  • Database per tenant

  • Collection per tenant

  • Shared collections with tenant field

In practice, most teams start with shared collections plus strong tenant filtering, then move up the isolation ladder for high compliance customers.

MongoDB reference:

Key AWS building blocks

  • Identity: Amazon Cognito

  • API: API Gateway plus Lambda or container services

  • Storage: S3 with tenant scoped prefixes

  • Networking: VPC, private subnets, and private endpoints

References:

Data isolation and access control

Rules:

  • Enforce tenant ID in every query server side

  • Use row level authorization checks

  • Use least privilege IAM roles

Security references:

Workflow state and jobs

AI systems need durable job state:

  • queued, running, needs review, complete, failed

MongoDB is a good fit for this state plus metadata. For compute, you can use:

  • Step Functions for orchestration

  • SQS for work queues

References:

zion services overview: https://lidvizion.ai/

FAQs

Q: Do we need separate databases for every tenant? Not always. Separate databases increase isolation but also increase operational cost. Choose based on compliance requirements.

Q: How do we handle per tenant rate limits? Implement throttling at the API layer and enforce quotas in your job scheduler.

Q: How do we onboard enterprise customers with stricter requirements? Offer a higher isolation tier, for example dedicated database and private networking.

Internal reference:

  • Lid Vi

Q: What should we link to internally? A: Link to relevant solution pages like Computer Vision or Document Intelligence, and only link to published blog URLs on the main domain. Avoid staging links.

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