Case Study: RAG Deployment on AWS for a SaaS Company
Published on February 26, 2026
A mid-size B2B SaaS company was burning $41,000/month in agent salaries answering the same 400 questions every single day.
Their knowledge base had 9,200+ documents. Their support team of 23 people averaged 14 minutes per ticket. And 38% of those documents were duplicates, contradictions, or outdated version history — including a pricing page from 2021 that quoted $19/month for a plan that now costs $79/month.
Impact: After RAG deployment — ticket resolution dropped to 91 seconds. Agent headcount requirement for tier-1 dropped by 61%.
We deployed a production RAG system on AWS in 11 weeks. Here is exactly how we did it — and the two decisions that almost derailed the entire project. This is a real Braincuber AI deployment, not a demo.
The Problem Was Not the AI Model
Every SaaS founder we talk to assumes the hard part of RAG is picking the right LLM. Wrong. The hard part is the data layer nobody audits before go-live.
This client had 9,200 documents across Confluence, Zendesk, Google Drive, and a legacy SharePoint instance that had not been touched since 2019. When we ran their raw corpus through an initial embedding pass, the RAG system was confidently citing a pricing page from 2021 that quoted $19/month for a plan that now costs $79/month.
(Yes, their sales team had already sent 47 of those wrong answers to active leads in a pilot test. We caught it before it went live — barely.)
Garbage in, garbage out applies 10x harder in RAG than it ever did in traditional search.
Why "Just Use OpenAI" Was the Wrong Call
Their CTO’s first instinct was to call the OpenAI API directly. We pushed back hard. Here is why that was a bad idea for a SaaS company handling multi-tenant B2B data:
The OpenAI Problem for B2B SaaS
Data sovereignty: Every API call sends tenant data to a third-party endpoint. Their enterprise customers in the EU were under GDPR contracts that explicitly prohibited this.
Cost ceiling: At 180,000 queries/month, OpenAI API costs would have run $7,400–$9,200/month with zero cost ceiling. One viral product launch and that number spikes without warning.
No control over model versioning
The day OpenAI deprecates a model, your carefully tuned prompts break.
We moved everything inside AWS. The entire inference pipeline stays within a single VPC. No tenant data crosses an external endpoint. Their enterprise contracts stayed intact.
The Exact AWS Stack We Deployed
No vague "cloud-native architecture" talk. Here is the actual production stack:
Ingestion Pipeline
1. Ingest + Extract
Documents land in Amazon S3 (versioned, lifecycle-managed). AWS Lambda trigger fires on every upload. Text extraction via Amazon Textract for PDFs and scanned docs.
2. Chunk + Embed
Chunking at 512 tokens with 64-token overlap using LangChain’s RecursiveCharacterTextSplitter. Embeddings generated via Amazon Titan Embeddings V2.
3. Store
Vectors stored in Amazon Aurora PostgreSQL with the pgvector extension. $0 idle cost. Scales with existing RDS infrastructure.
Query Pipeline
1. Receive
User query hits Amazon API Gateway then Lambda.
2. Retrieve
Query embedded via Titan. Top-7 chunks retrieved from pgvector.
3. Generate
Retrieved context + query sent to Claude 3 Sonnet via Amazon Bedrock.
4. Respond
Response returned in under 650ms (p95).
Multi-Tenancy Architecture
How it works: Each tenant’s documents tagged with a tenant_id metadata field in pgvector. Every query filtered at retrieval time: WHERE tenant_id = :current_tenant. JWT tokens passed through API Gateway validate tenant identity before any vector lookup fires.
Infrastructure as Code
Full deployment via Terraform — reproducible, version-controlled, auditable. AWS CloudFormation for the Bedrock Knowledge Base component. ECS Fargate for the API layer, behind an Application Load Balancer.
The Two Decisions That Determined 80% of the Outcome
Chunking Strategy Killed the First Pilot
The first deployment used a flat 1,024-token chunk size — the default in most LangChain tutorials. Answer accuracy in internal testing came back at 61%. Unacceptable.
The problem: their product documentation mixed conceptual explanations with step-by-step procedures in the same sections. A 1,024-token chunk would grab half a concept explanation and half an unrelated troubleshooting procedure, and the model would blend them into a hallucinated answer.
The Fix That Changed Everything
What we did: Dropped to 512 tokens, added 64-token overlap, and implemented a parent-child chunking strategy where small retrieval chunks link back to larger context windows.
Answer accuracy jumped from 61% to 89% on RAGAS evaluation
That 28-point accuracy swing came entirely from chunk size — not from changing the model.
Vector Database Selection Was Not a Religious Debate
We evaluated three options: Amazon OpenSearch Serverless, Aurora PostgreSQL with pgvector, and a self-managed Pinecone instance.
OpenSearch Serverless
Billed $700–$1,100/month in OCU charges before a single query ran — purely for index capacity.
Pinecone
Required data leaving the VPC. Killed the compliance requirement immediately.
Aurora PostgreSQL + pgvector
$0 idle cost. Scaled with existing RDS infrastructure. Handled 450 concurrent vector searches per second without breaking a sweat.
The Cost Verdict
Total infrastructure cost: $2,340/month for 180,000 queries — versus the $7,400–$9,200/month OpenAI API estimate. That is a 40% cost reduction against the alternative.
Results After 90 Days in Production
Numbers only. No adjectives.
| Metric | Before RAG | After RAG (Day 90) |
|---|---|---|
| Avg. tier-1 ticket resolution time | 14 min | 91 seconds |
| Monthly tier-1 agent cost | $41,000 | $16,200 |
| Answer accuracy (RAGAS eval) | N/A | 89% |
| p95 query latency | N/A | 650ms |
| System uptime | N/A | 99.1% |
| Cache hit rate (repeat queries) | N/A | 35% |
Why the 35% Cache Hit Rate Matters More Than You Think
How it works: Repeat queries — same question, same tenant — get served from an ElastiCache Redis layer in front of Bedrock. Those queries cost $0 in Bedrock tokens and return in under 40ms.
At 180,000 monthly queries, that cache alone saves $830/month in model inference costs.
What the Next 6 Months Look Like
The client is now moving to agentic RAG — where the system does not just retrieve and generate, but takes multi-step actions: auto-drafting Zendesk replies, escalating tickets above a confidence threshold of 72%, and triggering Salesforce updates when a support interaction signals churn risk.
We are building this on LangChain + CrewAI agent orchestration, hosted on ECS Fargate, with Amazon Bedrock Guardrails enforcing PII redaction before any response goes out. The full agentic layer is a 6-week build on top of the existing RAG infrastructure.
RAG Is Not the Destination
RAG is the data foundation. Companies that get RAG right in month one ship agents in month four.
Companies that rush RAG for a demo and skip the data audit are still debugging hallucinations in month nine.
How Braincuber Approaches RAG on AWS
We have deployed production RAG systems for SaaS companies, D2C brands, and enterprise clients across the US, UAE, and UK. In every single project, the same three failure modes show up:
Bad data going into embeddings — no pre-ingestion audit, no deduplication, no version control on source documents.
Wrong chunk size picked from a tutorial — not from actual evaluation against real queries.
Multi-tenancy bolted on after launch — which means rearchitecting the entire vector store with live data at 2 AM.
We solve all three before the first embedding runs. If your SaaS company is evaluating RAG on AWS, we will find your biggest data quality gap in the first 30-minute call.
Stop Shipping AI Demos That Embarrass You in Front of Enterprise Clients
Book a free 15-Minute RAG Architecture Audit — we will tell you exactly what breaks first. Do not let a bad chunking strategy cost you 3 months of re-work. Our cloud consulting team has deployed RAG for $4.7M+ ARR SaaS companies.
Frequently Asked Questions
How long does RAG deployment on AWS actually take for a SaaS company?
A production-ready RAG deployment on AWS — including data audit, ingestion pipeline, vector store setup, and API layer — takes 8 to 12 weeks for a SaaS company with an existing document corpus of 5,000–15,000 files. The longest phase is pre-ingestion data cleanup, not the infrastructure build. Teams that skip this add 4–6 weeks of accuracy debugging post-launch.
What does RAG on AWS Bedrock cost per month at SaaS scale?
For a SaaS company running 150,000–200,000 queries/month, expect $1,800–$2,800/month in AWS infrastructure costs using Aurora PostgreSQL with pgvector and Claude 3 Sonnet on Bedrock. That figure drops by 30–40% once a Redis caching layer handles repeat queries. OpenSearch Serverless adds $700–$1,100/month in baseline OCU costs regardless of query volume.
How do you handle multi-tenancy in a RAG system on AWS?
Each tenant’s documents are tagged with a tenant_id metadata field at ingestion time in the vector store. Every retrieval query includes a hard filter on that tenant_id before any semantic search runs. JWT tokens validated at API Gateway enforce tenant identity. This prevents cross-tenant data leakage without requiring separate vector indices per tenant — which would multiply your storage costs 10–40x.
What answer accuracy can a SaaS company realistically expect from AWS RAG?
A well-tuned RAG system on AWS Bedrock — with proper chunking strategy, pre-audited source documents, and evaluated retrieval parameters — consistently reaches 85–91% answer accuracy on RAGAS benchmarks. The single biggest driver of accuracy is chunk size tuning against real user queries, not model selection. Switching models moves accuracy by 3–5 points. Fixing your chunk size moves it by 15–28 points.
Do we need to fine-tune the LLM for our SaaS product domain?
No — and fine-tuning before validating your RAG retrieval accuracy is one of the most expensive mistakes we see. Fine-tuning on AWS SageMaker for a domain-specific model runs $12,000–$40,000 in compute and 6–10 weeks of engineering time. In 90% of SaaS use cases, fixing the retrieval layer — better chunking, metadata filtering, and query rewriting — closes the accuracy gap without touching the base model. Save fine-tuning for cases where retrieval alone genuinely plateaus below 80%.