4 ways to add AI search to your documentation (and what each actually costs)

"Add AI search to our docs" sounds like a weekend project. Depending on which approach you take, it's anywhere from 30 minutes to a month of engineering. Here's an honest breakdown of every real option, what each costs in time and money, and when each makes sense.

Note: "AI search" means different things. This post covers semantic search with generated answers — a user asks a question in natural language and gets an answer grounded in your docs, not just a list of links. If you want keyword search, Algolia is fine and you don't need this guide.

Option 1: Build a RAG pipeline yourself

The full stack: crawl your docs → chunk text → generate embeddings → store in a vector database → build a query API → wire it to an LLM → add a chat UI → automate re-indexing on deploys. Each step is independently well-documented. Together they're a real project.

What you're building:

Crawler: Cheerio or Playwright for JS-rendered sites. Chunking: 300–500 token chunks with 50-token overlap. Embeddings: OpenAI's text-embedding-3-small at $0.02/1M tokens. Vector store: Supabase pgvector (free tier available) or Pinecone. Query layer: embed the question, cosine similarity search, pass top-K chunks to GPT-4o/Claude with a grounding prompt. UI: a chat widget on your docs site.

Ongoing: Re-indexing pipeline (trigger on docs deploy), monitoring for retrieval quality, handling embedding model deprecations.

When to choose this: You're building AI search as a product feature, you need full data control (on-prem, compliance), or your retrieval requirements are specialized enough that a generic solution won't work well.

When not to: Your goal is "AI agents should stop hallucinating about our API." That doesn't require a custom RAG pipeline. It requires your docs to be retrievable.

Option 2: Your docs platform's built-in AI

Mintlify, GitBook, and ReadMe all have AI chat built in or in beta. If your docs already live on one of these platforms, this is the lowest-friction option for adding a chat widget to your docs site.

What you get: A "Chat with docs" widget on your site, auto-indexed when you publish, no setup. Mintlify and GitBook also now auto-generate MCP servers for each docs site, which means AI agents in Claude/Cursor can query your docs too.

Trade-offs: You're locked to the platform. Customization is limited — you can't tune the retrieval logic, change the chunking strategy, or control what the model says. If you want to white-label it or embed it outside your docs site, that may not be possible.

When to choose this: You're already on one of these platforms and want something live this afternoon. The platform handles the hard parts and the quality is usually good for standard docs use cases.

Option 3: A dedicated AI docs search tool

Tools like Kapa.ai and Inkeep are purpose-built for enterprise docs search. They handle the full pipeline — ingestion, indexing, the chat widget, analytics on what users are asking. Integrations with Slack, Discord, Zendesk, and MCP are typically included.

What you get: Production-grade retrieval quality, analytics (see what questions users ask most, what gets answered well vs. not), multi-source ingestion (docs + Notion + GitHub + support tickets), and support from a team whose whole job is making this work.

Trade-offs: The cost is enterprise-oriented — budget for $500–2,000/month. That's justified if you're at scale and docs search is a serious investment. For a 5-person startup, it's usually too much before you've validated the use case.

When to choose this: You have significant docs traffic, a support burden you're actively trying to reduce, and the budget. Companies like Cloudflare, MongoDB, and Sentry use tools in this category. It's a real product, not a weekend project.

Option 4: A hosted indexing service (MCP-first)

This is a different angle entirely. Instead of adding AI search to your docs website, you make your docs queryable by AI agents from wherever they're working — Claude Desktop, Cursor, Windsurf.

AgentReady does this: submit your docs URL, it crawls and indexes your content, and exposes it as an MCP tool. When a developer using Claude asks about your API, Claude calls ask_site("yourdomain.com", "how do I authenticate?") and gets an answer from your actual docs with a citation. No widget on your site. No SDK to install. It works from inside the developer's existing AI tool.

What you get: Your docs are queryable from any MCP client without the user doing anything beyond a one-time MCP server setup. Answers are cited. Content is current as of last index. Free to try.

Trade-offs: No chat widget on your site — this is AI agent access, not end-user search. If you want a widget your customers can use on your docs homepage, this isn't it. It's for the use case where developers ask Claude about your API in their IDE.

When to choose this: Your users are developers who use AI coding assistants. You want Claude to stop hallucinating about your API. You want something live in 60 seconds without infrastructure.

The honest comparison

Most teams don't need to pick just one. The platform built-in gives you a widget on your site. AgentReady makes your docs queryable from Claude and Cursor. They're complementary — same docs, different interfaces, different audiences.