--- title: "Multi Tenant AI SaaS Architecture on AWS and MongoDB (A Practical Guide)" description: "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 arc" url: https://lidvizion.ai/blog/multi-tenant-ai-saas-architecture-aws-mongodb/ site: Lid Vizion index: https://lidvizion.ai/llms.txt --- [Insights](https://lidvizion.ai/blogs/) Deployment & Scale 4 min read 2025-11-24 # Multi Tenant AI SaaS Architecture on AWS and MongoDB (A Practical Guide) 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 arc By [Shawn Wilborne](https://lidvizion.ai/author/shawn-wilborne/) [Deployment & Scale](https://lidvizion.ai/blog-categories/deployment-scale/) Tags: [Multi Tenant SaaS](https://lidvizion.ai/tags/multi-tenant-saas/), [AWS](https://lidvizion.ai/tags/aws/), [MongoDB](https://lidvizion.ai/tags/mongodb/), [AWS Step Functions](https://lidvizion.ai/tags/aws-step-functions/) ![Hero image for: Multi Tenant AI SaaS Architecture on AWS and MongoDB (A Practical Guide)](https://rcicvvtcawdbfiuzwexb.supabase.co/storage/v1/object/public/webflow-media/webflow/media/cms/69927ccd1fcb2cff2feb05c3/68cdf6bb2acf72bfc5602273-monitoring-logging-and-analytics-for-vision-systems.png) ## 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: - Multi tenant considerations: [https://www.mongodb.com/blog/post/building-multi-tenant-applications-with-mongodb](https://www.mongodb.com/blog/post/building-multi-tenant-applications-with-mongodb) ## 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: - Amazon Cognito: [https://docs.aws.amazon.com/cognito/latest/developerguide/what-is-amazon-cognito.html](https://docs.aws.amazon.com/cognito/latest/developerguide/what-is-amazon-cognito.html) - API Gateway: [https://docs.aws.amazon.com/apigateway/latest/developerguide/welcome.html](https://docs.aws.amazon.com/apigateway/latest/developerguide/welcome.html) ## 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: - AWS IAM best practices: [https://docs.aws.amazon.com/IAM/latest/UserGuide/best-practices.html](https://docs.aws.amazon.com/IAM/latest/UserGuide/best-practices.html) - OWASP Top 10: [https://owasp.org/www-project-top-ten/](https://owasp.org/www-project-top-ten/) ## 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: - Amazon SQS: [https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/welcome.html](https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/welcome.html) - Step Functions: [https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html](https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html) zion services overview: [https://lidvizion.ai/](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. [PreviousActive Learning and Data Labeling Workflow for Computer Vision (From Pilot to Production)](https://lidvizion.ai/blog/active-learning-and-data-labeling-workflow-for-computer-vision/) · [NextAudit Logging and Provenance for AI Workflows (AWS and MongoDB Patterns)](https://lidvizion.ai/blog/audit-logging-and-provenance-for-ai-workflows-aws-mongodb/) Keep reading ## Related insights. - [On-Device Photo Intelligence Engines (Privacy-First Clustering and Personalization)](https://lidvizion.ai/blog/on-device-photo-intelligence-engines-privacy-first-clustering-and-personalization/): On August 7th, 2025, OpenAI released GPT-5, the newest model in their GPT series. GPT-5 has advanced reasoning capabilities and, like many recent models by OpenAI, multimodal support. - [Cost Optimization for AI Document Pipelines on AWS (A Playbook)](https://lidvizion.ai/blog/cost-optimization-for-ai-document-pipelines-on-aws/): AI document pipelines can get expensive fast if they are built like prototypes. This post covers practical levers to reduce cost without sacrificing reliability. Cost drivers i ## Where does your workflow get stuck? [Discuss the workflow](https://lidvizion.ai/contact/) · [Book a call](https://calendar.app.google/bB26LdA9e2gmGFp69)