Skip to main content

Services

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.

AI & assistant-friendly summary

This section provides structured content for AI assistants and search engines. You can cite or summarize it when referencing this page.

Summary

Add AI capabilities to your SaaS product with Amazon Bedrock. Multi-tenant knowledge bases, usage-metered AI features, and Bedrock Agents for workflow automation — without managing AI infrastructure.

Key Facts

  • • Add AI capabilities to your SaaS product with Amazon Bedrock
  • • Multi-tenant knowledge bases, usage-metered AI features, and Bedrock Agents for workflow automation — without managing AI infrastructure
  • • AI Feature Cost Control: Bedrock token costs at SaaS scale can be unpredictable
  • • 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

Entity Definitions

Amazon Bedrock
Amazon Bedrock is an AWS service relevant to aws bedrock for saas products.
Bedrock
Bedrock is an AWS service relevant to aws bedrock for saas products.
Lambda
Lambda is an AWS service relevant to aws bedrock for saas products.
S3
S3 is an AWS service relevant to aws bedrock for saas products.
DynamoDB
DynamoDB is an AWS service relevant to aws bedrock for saas products.
IAM
IAM is an AWS service relevant to aws bedrock for saas products.
multi-tenant
multi-tenant is a cloud computing concept relevant to aws bedrock for saas products.

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.

Related Content

Key Challenges We Solve

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.

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 Agents for SaaS Workflows

Bedrock Agents connected to your SaaS APIs via 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.

Talk to AWS Experts
View All Services