It simplifies building RAG systems by abstracting complex infrastructure like vector databases and embedding pipelines into a managed service. With MCP support and free embedding, engineers can give AI agents access to fresh, proprietary data without high costs or manual plumbing.
Today, we’re excited to announce a few developer experience improvements to Cloudflare AI Search to make it easy to manage a search solution out of the box. Previously, you had to stitch together components of the Cloudflare primitives (Workers AI, AI Gateway, Vectorize, R2, Browser Run) but now, AI Search can do this automatically, and better. Our goal is to give your agents their own search engine, where they can easily find data to provide better answers for themselves and their humans.
We’re also sharing an early preview of pricing for customers of AI Search so you can learn how this scales. We modeled pricing in a way that makes it predictable and scalable: embedding and reranking are free when you use the default models, so no need to worry about predicting token count.
In AI Search, users can now:
/search and /mcp endpoint that can search through multiple instances or websites at once without authentication, so you can share easily with your customers./search and /mcp endpoints (e.g., search.example.com/mcp). You can also add Cloudflare Access to create private search instances.Finally, we will also share examples of how AI Search is used across our own platform including Cloudflare.com, our Developer Docs, with EmDash, in Cloudflare Dev Stack MCP — and even the blog post you’re reading right now (try cmd+K).
One of the ways we use AI Search is in our new Cloudflare Dev Stack MCP, which you can try today in our AI Playground. It gives coding agents current, cited docs from across the Cloudflare developer ecosystem, so they build on the latest features and fixes instead of stale training data.
Here's how we built it using the features available today in AI Search:
We created one AI Search instance per Cloudflare-owned surface: Docs, Blog, API Docs, Community, Astro, Vite, Vitest, Hono, Replicate, OpenNext. (Each of these is Cloudflare-owned.)
They span different domains, but, because Cloudflare owns the website data, AI Search is able to treat them as a single set and ingest them all the same way. Point AI Search at a site, or set of sites, and it handles crawling, ingestion, embedding, and retrieval. Creating an instance is a single command, and for a site without a sitemap you add --parse-type discover to find pages by following links (powered by /crawl from Browser Run):
Now the interesting part: answering a single query across all 10 instances. There are two ways to do it.
Option A: in a Worker (what we did for Cloudflare Stack MCP)
We bound the namespace to a Worker to create a remote MCP server and made one multi-instance call across all 10 instances. We took this path because we're adding the stack search into Cloudflare's MCP server, so it ships as a tool alongside the Cloudflare tools agents already connect to.
The binding, in wrangler.jsonc:
Then a single tool makes one call that fans out across the instances you name:
Option B: flip on public endpoints (no code)
If you'd rather not write a Worker at all, enable public URLs on the namespace. You immediately get /search and /mcp endpoints that query every instance, with no auth and nothing to deploy.
Reach for the Worker when you're folding search into an existing app or MCP server, as we are. Or reach for the public endpoint when you just want a shareable search endpoint in one click.
Public endpoints come with a default public URL, but you can put your own custom domain over them to brand the endpoint (e.g., search.example.com/mcp).
If the search should be private, add Cloudflare Access in front of the domain. The endpoint now requires a login, so only authorized people (or agents) can query it.
With the Cloudflare Dev Stack MCP Server, you can ask about any tool, or describe an app you want to build, and you'll get back current, cited answers on how best to build it on the Cloudflare stack.
The AI Playground is worth checking out, but the real magic is wiring the MCP into your coding agent, so the stack's current docs are one tool call away. That replaces the usual fallback (web search then fetching full pages), which is slow, token-heavy, and often lands on the wrong or stale source. To use with your agent of choice, drop the Dev Stack MCP URL into your MCP configuration. For example:
We build with AI Search the same way our customers would: Cloudflare Blog's search already runs on it, and today Developer Docs and Cloudflare.com join it. All of it uses hybrid search, semantic and keyword together in one query, so it handles both open-ended "what does this do" questions and exact lookups of names or keywords. We recently rebuilt the Blog on EmDash, our new open-source CMS, and our new
EmDash AI Search integration is what powers that search now. You can also add it to your own EmDash site and get the same search over your content out of the box.
AI Search is powered by Browser Run /crawl in the background, but goes a step further to identify itself with its own bot identity: Cloudflare-AI-Search. Just like Browser Run, it follows robots.txt, identifies itself with an immutable, public user agent, and will respect whatever bot controls a site has in place.
AI Search is currently free while in beta, and billing is not yet enabled; we'll email you with plenty of notice before it starts. As we move toward general availability, here's a preview of pricing across ingestion, storage, and queries, plus embedding and reranking (preview prices are subject to change before billing begins):
† A single pool of 5M ingestion tokens per month, covering any file type currently supported (e.g., text, images). ‡ A single pool of 2,000 queries per month, shared across both query types.
Our goal is to provide pricing you can predict, starting with the models your search leans on. Embedding turns your text into the vectors that search matches on, and reranking reorders results so the most relevant come first. Both run free with AI Search defaults or when using select models from the Workers AI catalog, so the models behind indexing and every search are not a cost you have to worry about. Answer generation and query rewriting are optional steps that run on a model you choose, billed as Workers AI usage, or you can use AI Gateway credits with any model/provider.
Here's a sample monthly bill on the Workers Paid plan for creating a new AI Search instance for a 20,000-document data source (about 20M tokens of text) plus 1,000 images (assume about 1,000 tokens each), with 30,000 semantic queries a month using the default AI Search embedding and reranking model. Ingestion is chunked with roughly 10% overlap, which shows up as the × 1.1 below:
Images count toward base ingestion and also incur the image add-on cost. Storage assumes about 10 KB per document and 1 MB per image. Indexing is largely a one-time cost, so later months are mostly queries, closer to $21.
AI Search is available to enable and use today. Point it at your site, turn on hybrid search for both semantic and keyword matching, and you have a search engine for your own data, ready for your agents. Spin one up with one command:
From there, query it, wire it into an agent over /mcp, or put a custom domain on a public /search endpoint to share it with your users. Check out the AI Search docs for more information.
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