Enterprise RAG: Build vs Buy – A Technical Decision Guide

The pitch for RAG is compelling: connect your documents, ask questions in plain English, get accurate answers with citations. No wonder engineering teams everywhere are spinning up LangChain proof-of-concepts.

But there's a gap between a demo that works on ten PDFs and a system that handles your actual enterprise documents, with all their formatting quirks, permission requirements, and scale demands. This guide is for technical leaders trying to make an honest assessment: should you build this yourself, or buy a platform?

We'll walk through what building actually involves, where the hidden costs lie, and how to think about the decision for your specific situation.

What we're actually talking about

RAG (Retrieval-Augmented Generation) is an architecture pattern, not a product. When someone asks a question, the system retrieves relevant content from your documents, then uses a language model to generate an answer grounded in that content. Done well, you get accurate, verifiable answers instead of the hallucinations that plague vanilla LLM deployments.

A complete enterprise RAG system needs several components working together:

• Document ingestion – Getting content out of PDFs, Word docs, spreadsheets, presentations, and whatever else lives in your systems
• Chunking and processing – Breaking documents into pieces the right size for retrieval, while preserving context
• Embedding generation – Converting text into numerical vectors that capture semantic meaning
• Vector storage – A database optimised for similarity search across those vectors
• Retrieval logic – Finding the most relevant chunks for a given query, often with re-ranking
• Generation – Using an LLM to synthesise an answer from the retrieved content
• Citation tracking – Linking claims back to source documents so users can verify

Each of these is a solved problem in isolation. The challenge is getting them to work together reliably, at scale, with your specific documents and requirements.

The DIY stack: what building looks like

If you're building a prototype, you'll likely reach for some combination of these tools:

The typical architecture

Document processing: Unstructured, LlamaParse, or similar libraries for extracting text from various file formats. You might also roll your own with PyPDF, python-docx, and friends.

Embeddings: OpenAI's embedding models are the common choice. Cohere and open-source alternatives like BGE or E5 are options if you want to avoid API dependencies.

Vector database: Pinecone, Weaviate, Qdrant, Milvus, or pgvector if you want to stay in PostgreSQL. Each has different trade-offs around scale, cost, and operational complexity.

Orchestration: LangChain or LlamaIndex to wire everything together. These frameworks handle the retrieval-generation pipeline and provide abstractions for common patterns.

LLM layer: API calls to GPT, Claude, or Gemini. Or self-hosted models like Qwen if you need to keep data on-premises.

Getting a basic version working with this stack isn't hard. A competent engineer can have something running in a few days. That's both the appeal and the trap.

What the tutorials don't tell you

The tutorials show you how to build RAG over a folder of clean PDFs. Here's what they skip:

Document parsing is harder than it looks. Your actual documents aren't clean markdown. They're scanned PDFs with OCR artefacts, spreadsheets with merged cells, presentations with diagrams, Word docs with tracked changes. Headers and footers get mixed into the content. Tables become incomprehensible when flattened to text. The parsing library that worked perfectly on your test files fails silently on half your production documents.

Chunking requires domain knowledge. The default chunking strategies (split every 500 tokens with 50-token overlap) work reasonably well for blog posts. They fall apart for contracts, where a single clause might span multiple pages and reference definitions from elsewhere. Or technical documentation where context depends on the section hierarchy. You'll spend weeks tuning chunk sizes and overlap strategies for your specific content.

Permissions are a nightmare. If Sarah can't see a document in SharePoint, she shouldn't find it through your search system either. Implementing permission inheritance means syncing access controls from every source system, handling group memberships, dealing with document-level and folder-level permissions, and keeping it all in sync as permissions change. Most DIY systems either ignore this entirely or implement it poorly.

Citations need to be precise. "This came from Document X" isn't good enough for enterprise use. Users need page numbers, or better yet, highlights showing exactly which passage supports each claim. Building accurate citation tracking, especially when the LLM paraphrases or combines information from multiple sources, is surprisingly difficult.

It never stops needing work. Documents change. New file types appear. Users find edge cases. Embedding models improve. You'll need someone maintaining this system indefinitely.

The real costs of building

Let's break down what you're actually signing up for.

Engineering time

The initial build takes longer than you think. Teams consistently underestimate by a factor of three to five, because the first version that works in development reveals all the edge cases you didn't anticipate.

Then there's iteration. Your first chunking strategy will be wrong. Your first retrieval approach will miss obvious results. Users will report issues you never considered. Each iteration cycle costs engineering time that could go elsewhere.

And there's opportunity cost. The senior engineers working on your RAG system aren't working on your core product. For most companies, search infrastructure isn't the thing that differentiates you in the market.

Infrastructure

Vector databases at scale aren't free. Pinecone's pricing climbs quickly with document volume. Self-hosted alternatives need compute and operational expertise.

Embedding generation needs compute too: either API costs (which add up with large document sets) or GPU infrastructure for self-hosted models.

LLM API costs are the obvious one, but they're harder to predict than you'd expect. Retrieval quality affects how much context you need to send. Poor retrieval means more tokens per query.

Hidden costs

Security review. Any system touching enterprise documents needs security assessment. Homegrown systems take longer to review because there's no existing documentation or compliance history.

User feedback loops. You'll need ways to collect feedback, identify poor results, and improve. This is engineering work that doesn't ship features.

Documentation and onboarding. Other engineers will need to understand and maintain the system. Internal documentation is work.

On-call burden. When it breaks (and it will) someone needs to fix it. Production ML systems have failure modes that are different from typical web applications.

When building makes sense

Building isn't always wrong. It makes sense when:

You have highly specific requirements that no platform supports. Maybe you need to integrate with a proprietary document format, or your retrieval needs are genuinely unusual. If you've evaluated the options and they truly can't do what you need, building is your path.

RAG is core intellectual property. If your company's competitive advantage is specifically in how you process and retrieve information, owning that system end-to-end might matter strategically.

You have a team with deep ML/NLP expertise and bandwidth. If you already employ engineers who've built similar systems, the learning curve is lower. But be honest about whether they have bandwidth and whether this is the best use of their skills.

You're in an experimental or research context. Academic research, internal R&D, or early-stage exploration, where learning is the goal, benefits from hands-on building.

Even in these cases, starting with a platform and customising might still be faster than building from scratch.

When buying makes sense

Buying makes sense when:

Time-to-value matters. If you need working search in weeks not months, you're unlikely to build something production-ready that quickly. Platforms have solved the hardest problems already.

Your documents are complex. Mixed formats, tables, scanned documents, complex layouts: this is where DIY solutions struggle most. Platforms have invested heavily in document parsing because they've heard the complaints.

Security and compliance are requirements. Enterprise features like SSO integration, audit logging, permission inheritance, and data residency options take significant effort to build. Platforms have these because enterprise customers demand them.

Your team should focus elsewhere. For most companies, search infrastructure isn't a differentiator. It's plumbing. If your engineers could be building features that directly serve your customers or grow your business, that's probably where they should be.

You want to avoid the maintenance burden. Buying doesn't just save initial build time. It shifts ongoing maintenance to someone whose job it is to keep the system working.

What to evaluate in a platform

If you decide to buy, here's what matters:

Document ingestion quality

This is where platforms differentiate most. Can it handle your actual documents? Not the clean PDFs from the demo, but your real contracts with nested tables, your scanned receipts, your spreadsheets with merged cells.

Does it preserve structure? A system that flattens everything to plain text will struggle with documents where layout carries meaning.

Ask for a trial with your own data. Vendors who are confident in their parsing will welcome this; those who aren't will want to stick to demo datasets.

Citation accuracy

Can users verify answers? This is non-negotiable for enterprise use. People need to trust the system, and trust comes from being able to check sources.

Page-level citations beat document-level citations. Bounding boxes (highlighting the exact passage) are even better. Test whether the citations actually point to relevant content, not just vaguely related sections.

Security and deployment

Where does your data go? Some platforms process everything in their cloud. Others offer VPC deployment or on-premises options. For regulated industries or sensitive data, this might be the deciding factor.

How are permissions handled? Does the platform inherit access controls from your source systems? Can you configure role-based access? What happens when someone's permissions change?

Model flexibility

Are you locked into a specific LLM, or can you choose? The model landscape is evolving rapidly. Being tied to GPT-3.5 when GPT-5 ships is a risk. Better platforms are model-agnostic, letting you swap in new models as they improve.

Can you bring your own models? For some organisations, running inference on self-hosted models is a requirement. Check whether that's supported.

Total cost of ownership

What's included in the licensing fee versus what's extra? Some platforms charge per document, per query, per user, or some combination. Model out your expected usage.

Are there hidden infrastructure costs? If the platform runs in your cloud, what compute does it need?

Compare honestly against the build option. Include engineering time at your actual fully-loaded cost, not just salaries.

A decision framework

Here's a simplified way to think about the choice:

Lean towards building if:

• You have specific requirements no platform meets
• RAG is core to your competitive advantage
• You have deep ML expertise with bandwidth
• Learning is more important than shipping quickly

Lean towards buying if:

• You need production-ready search in weeks, not months
• Your documents are complex or varied in format
• Security, compliance, and permissions matter
• Your engineering team has higher-value work to do

Questions to ask yourself:

• What's our realistic timeline for a production deployment?
• Who will maintain this system in two years?
• What's the opportunity cost of the engineering time?
• How complex are our actual documents?
• What are our security and compliance requirements?

Questions to ask vendors:

• Can we trial this with our own data?
• How does permission inheritance work?
• What deployment options do you offer?
• How are citations generated and verified?
• What's included in the pricing versus additional?

The bottom line

Building your own RAG system is possible. The tools are better than ever, and the tutorials make it look straightforward. But there's a significant gap between a demo and a production system that handles enterprise requirements.

The question isn't whether you can build it. It's whether you should. For most organisations, the answer is that your engineering time is better spent elsewhere, and the ongoing maintenance burden of a custom system isn't worth the flexibility.

That said, there are legitimate reasons to build. If you have genuinely unique requirements, deep in-house expertise, and strategic reasons to own this infrastructure, building can be the right choice.

Whatever you decide, make the decision with clear eyes about what each path actually involves. The worst outcome is six months into a build realising you should have bought, or locked into a platform that doesn't actually meet your needs.

If you're evaluating options, explore how Conductor approaches RAG, or talk to us about your specific requirements. We're happy to discuss whether we're a good fit, even if the answer is that building makes more sense for your situation.