Weaviate suits teams that want embeddings generated inside the database, a Query Agent for plain-English questions and strong multi-tenancy, all in one open source platform. Qdrant suits engineers who want a focused retrieval engine with native hybrid search and filtering, running anywhere from managed cloud to edge devices. The more you want the database to do for you, the more the first one fits.
Most vector database decisions boil down to how much work you want the database to take off your plate. Some teams want to hand it raw text and images and let it handle the rest. Others want a sharp, fast retrieval engine and prefer to keep embedding and agent logic in their own code. These two vendors sit on opposite sides of that line, and once you know which side you're on, the choice gets much easier.
To save repeating names, I'll call the first one the AI database and the second one the search engine, because that's how each describes its own core job.
How much should your database do for you?
The AI database pitches itself as vector search, RAG and agentic AI in one open source platform. It generates vectors from text and images itself, so there's no external embedding pipeline to build, and its Query Agent turns natural-language questions into database queries.
The search engine is narrower on purpose. It's a Rust-based vector search engine aimed at retrieval at scale, with metadata filtering, native hybrid search and multivectors. It does offer help with embeddings through its FastEmbed library and an Inference API, and it supports external providers, but it doesn't try to be the whole stack.
Neither stance is better in the abstract. A small product team with no ML engineer may love having embeddings and natural-language querying built in. A platform team that already runs its own embedding models may see those extras as things it won't use.
Search quality: filters, hybrid queries and tenants
Filtering
The search engine's headline claim is about filtering. The vendor says filters are applied during HNSW traversal, giving one-stage filtering rather than a separate step before or after the vector search. The AI database also stresses filtered vector search, under the name PathSeer, so both take filtering seriously. They just describe it differently.
Hybrid search
If you need keyword-style matching next to semantic search, the search engine is more explicit about it. Its docs describe native dense and sparse hybrid search, with sparse methods including SPLADE++ and miniCOIL. The AI database's pages put their emphasis elsewhere, on filtering and multi-tenancy.
Multi-tenancy
For anyone building a SaaS product where each customer needs its own slice of data, the AI database makes a strong claim: it says it can hold a very large number of tenants in a single cluster. The search engine documents multitenancy too but doesn't cite a tenant count. If tenancy is central to your product, that's a point in the AI database's favour, though it's a vendor claim worth checking against your own load.
Legacy code, edge devices and what's changing
This is the section I'd read twice before committing. The AI database's docs say its GraphQL API is deprecated and is scheduled to be switched off on its cloud service. Plenty of older tutorials and sample apps were written against that API, so if you're starting from one, plan the migration to its current client libraries from the outset rather than discovering it later.
The search engine, meanwhile, is pushing past the server. Its Edge option, currently in beta, is aimed at on-device use, and the AI database's pages don't describe anything similar. If you're building search that has to run on a device without a network round trip, that's a real differentiator.
Put simply: one vendor is retiring an old interface, the other is opening a new front. Both are signs of active products, but they affect you in different ways depending on where your code starts.
Where and how each one runs
The AI database is open source and deployment-agnostic, with its own cloud service where a free tier is mentioned for evaluation. The search engine offers Managed Cloud, Hybrid Cloud on your own Kubernetes, the Edge beta and a waitlist for a serverless option. It also notes HIPAA compliance among its security credentials, which may matter if you handle health data.
For agent builders, both have something to offer. The AI database has its Query Agent; the search engine documents agentic tools, MCP servers and Agent Skills. If you're comparing agent frameworks to sit on top of either, our guide to the best AI agent frameworks is a useful next stop.
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The comparison grid
| Area | Weaviate | Qdrant |
|---|---|---|
| Positioning | Broader AI database: vector database, Query Agent and embeddings | Focused vector search engine for retrieval at scale |
| Embeddings | Built-in vector generation from text and images | FastEmbed library and an Inference API, with external provider support |
| Natural-language querying | Query Agent turns questions into database queries | Agentic tools, MCP servers and Agent Skills documented |
| Filtering and hybrid search | Filtered vector search (PathSeer) and multi-tenancy | Native dense and sparse hybrid search, plus one-stage filtering |
| Deployment | Open source, deployment-agnostic, cloud service with a free tier for evaluation | Managed Cloud, Hybrid Cloud, Edge (beta) and a serverless waitlist |
| Engine | Not described on its pages | Rust powered, with HIPAA compliance noted |
| Tenancy | Claims a very large tenant count per cluster | Multitenancy documented, no count cited |
Verdict for product teams and for engineers
Pick the AI database if you want embedding generation and a Query Agent built into one open source platform, especially for a multi-tenant product where each customer's data stays separate. Pick the search engine if you want a lean retrieval engine with native hybrid search, one-stage filtering and the freedom to deploy anywhere from edge devices to managed cloud. If you're inheriting old code built on the AI database's GraphQL API, factor in the migration either way.
Still weighing the wider field? Our round-up of the best vector databases goes beyond these two.
Other vector stores that might fit better
Pinecone
Best for: Teams wanting a managed knowledge platform for AI agents with no infrastructure to run, and a start-for-free sign-up.
Chroma
Best for: Developers prototyping AI apps on open source search infrastructure described as fast, serverless and scalable; its banner says it's joining Replit.
pgvector
Best for: An open source vector similarity search extension that runs inside an existing Postgres database.
Do you make a vector database or AI search tool? Tool makers can ask to be considered for a featured place on this page.
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Frequently asked questions
Is Weaviate or Qdrant open source?
The AI database describes itself as open source and deployment-agnostic. The search engine offers managed, hybrid and edge options; check its repository terms if self-hosting matters to you.
Which one creates embeddings for me?
The AI database generates vectors from text and images itself. The search engine offers its FastEmbed library and an Inference API, and works with external providers.
Is the Weaviate GraphQL API going away?
Its docs say the GraphQL API is deprecated and scheduled to be switched off on its cloud service, so older tutorials using it will need migrating.
Which is better for hybrid search?
The search engine documents native dense and sparse hybrid search with one-stage filtering. The AI database's pages focus more on filtered search and multi-tenancy.
Can either run on a device?
The search engine has an Edge option in beta for on-device use. The AI database's pages don't describe an equivalent.
