The Real Cost of Knowledge: Why Most AI Engineering Platforms Over-Engineer RAG

When AWS published their post Deploy Amazon Bedrock Knowledge Bases Using Terraform for RAG-Based Generative AI Applications, it offered a beautifully structured architecture: document ingestion, embeddings, vector search, and automated retrieval through Bedrock Knowledge Bases.

It’s a strong example of technical design.

But for most organizations, it’s more infrastructure than insight.

The Managed Complexity Trap

Every company entering AI transformation hits the same wall.

They begin with reference architectures built for hyperscale, not for discovery.

Then the pattern unfolds:

• •OpenSearch Serverless minimums run $100–150 a month before the first query.
• •Managed pipelines multiply IAM roles, data movements, and service integrations.
• •Teams spend weeks building connectors before they get a single meaningful answer.

By the time the system is ready, the business question that started it has already changed.

That’s the irony of the knowledge revolution: many organizations are building “intelligent systems” that haven’t yet produced any usable intelligence.

The Lean Alternative: The OutcomeOps Model

The real goal isn’t to automate knowledge.

It’s to make institutional knowledge accessible, traceable, and continuously refined.

Here’s the architecture pattern that achieves that outcome with almost zero operational drag:

Team Knowledge Sources (Docs, Wikis, Repos, Reports)
          ↓
Ingestion Lambda → S3 (raw content)
          ↓
Embedding Model (Bedrock Titan v2)
          ↓
Vector Store (DynamoDB)
          ↓
Query Lambda → LLM (Claude via Bedrock)

No OpenSearch.

No managed Knowledge Base.

Just a lightweight flow that any organization can deploy, understand, and operate for a few dollars a month.

Technical Note on Vector Search

Yes, DynamoDB isn’t a vector database that’s intentional.

For moderate scales, we store normalized embeddings as numeric arrays in DynamoDB and compute cosine similarity inside the Lambda itself. Each query performs a full table scan, loads embeddings into memory, and sorts results by similarity score.

This approach scales linearly:

• •Around 25,000–30,000 embeddings, queries complete in roughly 100–200 ms.
• •Around 50,000–100,000, latency increases linearly, typically reaching 500–800 ms.

Beyond that threshold, we’d transition to OpenSearch Serverless, Aurora pgvector, or another purpose-built vector index.

The trade-off is intentional: simpler operations, no external dependencies, predictable cost, and complete transparency into how results are calculated. For most internal knowledge systems, those advantages outweigh the microseconds saved by specialized engines.

How It Works

• •Ingestion Lambda collects organizational content documents, notes, design artifacts, marketing briefs and uploads it to object storage.
• •Embedding Model converts each item into a vector representation that captures meaning, not just keywords.
• •Vector Store (DynamoDB) holds those embeddings with metadata for source and context.
• •Query Lambda embeds a user question, computes similarity, and retrieves the most relevant context.
• •Answer Lambda calls a large language model through Bedrock to produce a grounded, source-cited response.

The result: a transparent, low-cost intelligence layer that adapts as your content and usage patterns evolve.

Why It Works

Because the objective isn’t automation it’s adaptation.

Every query highlights what’s missing or unclear.

Each new document expands the system’s context.

Over time, this feedback refines retrieval accuracy without retraining any model.

It’s not the software that improves.

It’s the organization that becomes more aligned and context-aware.

When to Scale Up

DynamoDB comfortably supports tens of thousands of data points.

Only when you reach very large document sets or strict latency requirements should you move to OpenSearch Serverless or another vector engine.

That’s the OutcomeOps principle:

Don’t scale until you have feedback. Validate the outcome first. Then scale the pattern.

Why It Matters

Every department engineering, marketing, HR, operations is now asking the same question:

“How do we turn what we already know into something we can actually use?”

The answer isn’t to deploy the biggest platform; it’s to design the simplest system that improves with use.

The simpler the loop, the faster the organization adapts.

The OutcomeOps model treats intelligence as an emergent property of iteration knowledge that becomes more accessible and actionable each time people engage with it.

Closing Thought

AWS’s Bedrock Knowledge Base architecture is a solid enterprise reference, but not every team needs an aircraft carrier.

Most just need a speedboat that can change direction quickly and refine its map as it goes.

• •Start light.
• •Prove value.
• •Then expand with intent.
• •That’s how organizations move from static documentation to operational understanding - from automation to adaptation.

That’s OutcomeOps.