Multi-Tenant AI Isolation
Each SaaS tenant requires isolated AI context — one tenant's knowledge base, conversation history, and fine-tuned behaviors must never bleed into another tenant's AI experience.
Amazon Bedrock Consulting
AWS Bedrock for SaaS Products
We help SaaS companies integrate Amazon Bedrock as a product feature — AI assistants, content generation, and intelligent search that your customers pay for, with per-tenant data isolation and usage-based cost controls.
- A use case is scoped against real data.
- The model runs on Amazon Bedrock.
- A knowledge path retrieves from your sources.
- Guardrails apply policy and filters before output leaves.
- A named owner reviews anything that reaches customers or records.
- The feature ships inside the existing application.
Key Challenges We Solve
AI Feature Cost Control
Bedrock token costs at SaaS scale can be unpredictable. Without per-tenant usage tracking and token budgets, AI feature costs can eliminate margins on lower-tier plans.
Productizing AI Features
Embedding Bedrock into a SaaS product requires API abstraction, rate limiting, usage metering for billing, and model version management that is transparent to end users.
Latency for Real-Time AI
SaaS users expect AI responses in under 2 seconds. Bedrock streaming responses, prompt caching, and architecture optimization are required to meet UX expectations.
Our Approach
Per-Tenant Knowledge Base Architecture
Bedrock Knowledge Bases with per-tenant S3 prefixes and IAM role isolation — each tenant's documents, embeddings, and retrieval context are fully separated using tenant-scoped credentials.
AI Usage Metering Pipeline
Lambda middleware that intercepts Bedrock API calls, logs token usage per tenant to DynamoDB, and enforces usage limits — enabling AI feature tiers and overage billing.
Bedrock AgentCore for SaaS Workflows
Bedrock AgentCore connected to your SaaS APIs via tool servers and Action Groups — enabling AI assistants that can take actions in your product (create records, run reports, send notifications) on behalf of users.
Frequently Asked Questions
How do you prevent one tenant's data from appearing in another tenant's AI responses?
Each tenant gets an isolated Bedrock Knowledge Base backed by a tenant-scoped S3 prefix. IAM role assumption at request time ensures the retrieval step can only access that tenant's documents. Conversation history is stored in DynamoDB with tenant-partition-key isolation. No cross-tenant data sharing is architecturally possible.
How do we charge customers for AI feature usage?
We instrument Bedrock API calls to capture input and output token counts per tenant. This data feeds a metering service that enables usage-based billing (tokens consumed), tier-based limits (10,000 tokens/month on Starter), or AI add-on SKUs. Token costs are typically $0.003-$0.015 per 1,000 tokens depending on model choice.
Which Bedrock model should a SaaS product use?
For general SaaS AI features, Claude Haiku offers the best cost-to-capability ratio for high-volume, real-time interactions. Claude Sonnet suits complex reasoning and document analysis. Use Nova Micro for the lowest-latency classification and extraction tasks. Model selection should be driven by your specific use case and latency/cost requirements.
Ready to Get Started?
Talk to our AWS experts about aws bedrock for saas products.