How to add AI-powered search to your website or app

By the end of this guide you will have AI site search running on your own content: a search box that understands what a visitor means, not only the words they typed, and returns ranked results from your pages, products or documents. The building blocks are an embedding model that turns text into vectors, a search database that stores and queries them, and often a reranker that reorders the top results.

You can assemble these yourself or use a platform that bundles them. This guide shows the order of work and compares eight options from our Developer platforms category, so you can match the tool to your content volume, hosting preferences and budget.

Key takeaways

• Semantic search finds results by meaning. Hybrid search combines it with keyword matching, which usually helps for product codes, names and exact phrases.
• The main decision is build or bundle: pick a vector database plus an embedding model, or a search platform that handles ingestion, retrieval and answers together.
• Free tiers are real but small. Pinecone's Starter plan stops at 2 GB of storage, and Weaviate's free plan allows 100,000 objects.
• Test relevance with real queries before launch, and re-index whenever your content changes.

What you need before you start

• Content to search. Pages, product records, help articles or documents, in a form you can export as text with titles, URLs and metadata.
• A sample of real queries. Ten to thirty is enough: search logs, support tickets or questions you expect. You will use them to judge relevance.
• A developer. Most options here are APIs or libraries. Plan to write an indexing script and a small search endpoint.
• Accounts and keys. One for the search database and one for the embedding model, unless your platform includes both.
• Time. A prototype takes an afternoon. Tuning and a safe launch take longer.
• Privacy review. Check what customer or personal data you will send to a third-party service, and who may see the results.

Which kind of AI tool fits this job

A managed vector database. You bring the embeddings and query them through an API. Pinecone offers dense, sparse and full-text indexes, namespaces for separating tenants, and hosted embedding and reranking models. Weaviate stores objects with their vectors, supports hybrid search combining keyword and vector, and offers multi-tenancy. Both have a free plan.

An open-source database you can run yourself. Qdrant is an open-source vector database and search engine with hybrid search, payload filtering and a managed Qdrant Cloud. Chroma is an Apache 2.0 licensed search database with vector, sparse, full-text and regex search and clients for Python, TypeScript and Rust. These suit teams that want to start on a laptop or their own servers.

Embedding and reranking models. Search quality depends heavily on the model that creates vectors. Voyage AI provides embedding models, rerankers and a multimodal endpoint, billed by usage. Cohere offers Embed and Rerank models by API, with Rerank billed per search.

A search platform that bundles the pieces. Azure AI Search is a managed service on Microsoft Azure with full-text, vector and hybrid search, a semantic ranker and a free tier. Vectara ingests company data, runs hybrid retrieval with reranking and can generate cited answers, which suits question-answering search more than a plain results list.

Step 1: Decide what visitors should find

Start with the experience, because it decides how much machinery you need.

1. Decide whether you want a results list, direct answers with sources, or both.
2. List the content types that should be searchable and the metadata that matters, such as category, language, date or customer.
3. Decide which content must stay hidden for some users, such as private documents.
4. Write your target queries and what a good first result looks like for each.

Check it worked by having someone else read your query list and confirm each has a clear expected result.

Step 2: Choose a retrieval approach and a tool

Match the tool to your constraints rather than to feature lists.

1. Choose semantic, keyword or hybrid. Hybrid is listed by Pinecone, Weaviate, Qdrant, Chroma, Azure AI Search and Vectara.
2. Choose hosting: fully managed, self-hosted, or inside your cloud account. Qdrant and Chroma can run on your own infrastructure, and Azure AI Search is a managed service on Azure.
3. Compare free limits to your content size. Pinecone's Starter allows 2 GB of storage, Weaviate's free plan allows 100,000 objects, and Azure AI Search's Free tier has 50 MB of storage and up to 3 indexes.
4. Check that the tool supports per-customer separation if you serve many tenants. Pinecone offers namespaces, and Weaviate and Qdrant list multi-tenancy.

Check it worked by confirming your content size fits the free tier you plan to prototype on.

Step 3: Index your content for AI site search

You end this step with your content stored as vectors alongside the text and metadata.

1. Export your content with stable IDs, titles, URLs and metadata.
2. Split long documents into passages. Search returns the closest-matching chunk, so each chunk should make sense on its own.
3. Create embeddings with the model you chose. Voyage AI lists adjustable output dimensions, and its models are billed by usage. Pinecone's inference service hosts embedding models if you want one provider.
4. Write the chunks, vectors and metadata to your database in batches.
5. Keep the same embedding model for queries and documents. Mixing models gives poor results.

Check it worked by counting the stored records and confirming the number matches your export.

Step 4: Build the search endpoint

Your website should call your own server, not the database directly, so keys stay private.

1. Create a server route that accepts a query string and optional filters.
2. Embed the query with the same model, then search the database. For hybrid search, use the tool's hybrid query option instead of two separate calls.
3. Apply metadata filters, for example language, product line or the signed-in customer's access rights.
4. Return titles, snippets and URLs, with a limit on result count.

Check it worked by running ten of your sample queries and seeing sensible results in the top three.

Step 5: Add reranking and test relevance

A reranker reorders the top results using a model that reads the query and each candidate together.

1. Retrieve a wider set of candidates, then rerank them. Cohere lists Rerank models billed per search of up to 100 documents, and Voyage AI lists rerankers.
2. Score results against your query list. Note which queries fail and why: missing content, bad chunks or wrong filters.
3. Adjust one thing at a time, such as chunk size, the number of candidates or hybrid weighting.
4. Re-run the full query list after each change so you notice regressions.

Check it worked by comparing the share of queries with a good first result before and after reranking.

Step 6: Build the search interface

Visitors judge search by what they see.

1. Add a search box with a clear submit button and a sensible empty state.
2. Show titles, short snippets and the matching category.
3. Add filters that map to your metadata.
4. If you generate answers, show the sources next to them. Vectara lists cited, grounded answers and hallucination detection, but you should still review answers for accuracy.
5. Handle slow responses with a loading state and a timeout.

Check it worked by trying the interface on a phone and with a keyboard only.

Step 7: Keep the index fresh and monitor usage

Search quality decays when content changes and the index does not.

1. Re-index when a page is created, edited or deleted. Remove deleted content so it cannot appear.
2. Log queries that return no results or that visitors immediately retry.
3. Watch usage against plan limits. Pinecone's Builder plan blocks usage beyond its limits rather than billing it, and Chroma Cloud pauses service when usage limits are exceeded.
4. Review your sample queries every few weeks and add new ones from the logs.

Check it worked by editing a page, waiting for the update to run and finding the new text in search.

Tools for this job compared

Tool	Good fit for	Pricing	Stated limits
Pinecone	Managed semantic, full-text and hybrid search via web and API	Freemium, paid from $20/mo (checked 24 Oct 2026)	Starter: AWS us-east-1, 5 indexes, 2 GB storage; Builder blocks usage beyond its limits
Weaviate	Hybrid search with multi-tenancy, managed or self-hosted	Freemium, paid from $45/mo (checked 24 Oct 2026)	Free plan: 100,000 objects, 3 tenants, no replication or backup retention
Qdrant	Open-source vector search, self-hosted or in the cloud	Freemium	Free tier is a single node with no high availability, backups or SLA
Chroma	Open-source hybrid search with a serverless cloud option	Open source, paid from $250/mo (checked 24 Oct 2026)	Cloud regions are AWS us-east-1 and GCP europe-west1; hitting usage limits pauses the service
Cohere	Embeddings and reranking by API	Freemium	Trial keys are rate limited and not allowed for commercial use
Voyage AI	Embedding models and rerankers via API and Python	Freemium, paid from $0.02, usage-based (checked 24 Oct 2026)	Free tokens do not apply to Batch API usage; older models get no free tokens
Azure AI Search	Search service for Azure-based apps	Freemium	Free tier: 50 MB, 3 indexes, no semantic ranker; exact prices only in the Azure calculator
Vectara	Hybrid retrieval with grounded, cited answers	Free trial, paid from $100/mo (checked 24 Oct 2026)	30-day trial with 10,000 credits; paid plans have a minimum monthly bundle

Prices and plan limits change, so confirm the current terms on each vendor's site before you commit.

Common mistakes to avoid

• Using semantic search alone. Vector search can miss exact product codes, names and quoted phrases. Fix it by using hybrid search where the tool offers it, and test those queries specifically.
• Chunking carelessly. Chunks that cut off mid-thought return confusing snippets. Fix it by splitting at headings and paragraphs, and by storing the page title and URL with every chunk.
• Skipping access control. Search can reveal private content if filters are missed. Fix it by applying permission filters on the server for every query and, for multi-customer apps, separating data by namespace or tenant.
• Changing the embedding model without re-indexing. Vectors from different models are not comparable. Fix it by re-embedding the whole index whenever you switch models.
• Launching without a query test set. Without examples you cannot tell if a change helped. Fix it by keeping a fixed list of real queries and re-running it after every change.

How we put this together

This is an evidence-based guide built from each tool's listing in our directory, which is compiled from the vendor's own site and pricing page. It is not a review based on using the tools. We don't rank tools, and nobody paid to be included. Some outbound links may be affiliate links; see our disclosure.

Frequently asked questions

Is there a free way to add AI search to my website?

Yes, for small sites. Pinecone, Weaviate, Qdrant and Azure AI Search list a free tier or plan, Chroma Cloud's Starter plan has no monthly fee but bills usage after $5 in free credits, and Chroma and Qdrant are open source. Voyage AI gives every account free tokens. Check each limit against your content size, since free plans cap storage, objects or indexes.

What is the difference between semantic search and hybrid search?

Semantic search compares the meaning of a query with your content using vectors. Hybrid search combines that with keyword matching in one query. Hybrid usually handles exact terms better, which matters for names, codes and quoted phrases.

Do I need a vector database?

Not always. A bundled platform such as Azure AI Search or Vectara handles indexing and retrieval for you, and a small site may need nothing more. A dedicated vector database such as Pinecone, Qdrant or Weaviate gives you more control over embeddings, filters and hosting.

Can I keep my content private?

It depends on where you host. Qdrant and Chroma can run on your own infrastructure, and Vectara lists deployment in your VPC or on-premises. Managed services process your text on the vendor's systems, and embedding APIs receive your text too, so read each vendor's data terms before sending sensitive content.

How long does it take to set up?

It depends on how much content you have and how much of the stack you assemble yourself. A prototype over a small set of pages is the quick part; tuning chunking, filters and reranking against real queries is what takes longer. Plan time for access control and for keeping the index up to date.

Compare more options in our Developer platforms category. If you want chat answers instead of a results list, see how to build a RAG chatbot over your docs.