Build vs. Buy

License Retrieval Infrastructure
Instead of Building It

Teams that build RAG in-house spend 6–12 months and hundreds of thousands of dollars before reaching production quality. Thalamus gets you there in weeks.

See the Numbers ↓

The Hidden Cost of Building RAG In-House

Every AI team starts the same way: "We'll just build a quick RAG pipeline." Then reality hits.

1

Ingestion breaks on scanned documents with complex layouts.

2

Retrieval quality degrades as the document set grows past demo scale.

3

Hallucinations appear because the retrieval layer is returning noisy, incomplete context.

4

Engineers who should be building product features are debugging vector DB configurations.

5

Six months in, the team is maintaining infrastructure, not shipping value.

"RAG looks simple in a tutorial. It is an engineering discipline in production."

The Numbers Behind Build vs. Buy

2-Year Total Cost of Ownership

$485K
average amount Thalamus saves customers compared to an in-house offshore build
~66%
lower total cost of ownership
In-House Offshore Build
In-House Onshore Build
Up-front build cost
Up-front fully loaded AI engineering team
~4.5 FTE, primarily a blend of AI Architect, Machine Learning Engineers, Backend Engineers, and Product Support roles
Monthly cost of fully loaded AI engineering team
$35,000
$87,500
Months to build
4
4
Total up-front build cost
$140,000
$350,000
Support and maintenance
Ongoing headcount for in-house build
~1.5 FTE, primarily a blend of Machine Learning, Backend, and Site Reliability Engineers
Associated monthly cost
$9,000
$22,500
Total 2-year support and maintenance cost
$216,000
$540,000
New feature evolution
Full-time engineering headcount dedicated to research, development, and implementation
~2.5 FTE, primarily a blend of Machine Learning and Backend Engineers plus Product Support roles
Associated monthly cost
$16,000
$40,000
Total 2-year new feature evolution cost
$384,000
$960,000
Total 2-year cost of ownership
$740,000
$1,850,000

Usage-based costs (LLM, vector DB, OCR) excluded - identical for both paths.

Get to Production 6 Months Faster

Six months is the difference between leading your market and playing catch-up.

Production Ready
Thalamus
Day 0: Production ready

Go live immediately with a hardened retrieval layer - no dedicated build cycle or stabilization period.

In-House Build
4–6 months to production
Upfront build time ~4 months
Hardening to production +2 months

Teams spend months debugging ingestion, retrieval, and observability before they can safely expose answers to users.

Based on historical averages for Thalamus onboardings and production-grade, custom RAG builds.

What Your Thalamus License Covers

This isn't just hosting. It's a continuously improving retrieval platform.

Document parsing, embedding, retrieval, and answer generation infrastructure
Monitoring, incident response, and routine updates
Citation quality upgrades and source attribution improvements
Richer ingestion for tables, charts, and complex document formats
Hybrid dense and sparse retrieval improvements
Hallucination and compliance guardrails
New LLM and model evaluations as they become available
Optimization for emerging model families

These platform upgrades let your engineering team focus on what matters: analytics, workflow automation, risk scoring, and the client-facing features that differentiate your product.

Stop Building Infrastructure. Start Shipping Product.

Talk to us about your architecture and timeline.
We'll give you an honest assessment of whether Thalamus is the right fit.

Request a Demo