Pick a replacement vector database by your reason for leaving. If you already run Postgres or a search engine, add vectors there instead of adopting a new database; if you want open source you control, self-host a dedicated engine; and if you'd rather not run infrastructure at all, buy managed retrieval.
On 26 Oct 2026, Weaviate announced that its GraphQL API is deprecated. Weaviate Cloud switches it off on all clusters on 1 May in 2027, and clusters that haven't used GraphQL in 30 days lose it earlier, on 1 Dec 2026 according to the post. If your queries are written in GraphQL, a rewrite is coming either way.
That notice is why many developers are planning the move, alongside two older frustrations: the always-free cloud cluster is capped, and the cloud bill depends on index and compression choices you have to make up front. This is a migration brief for that moment, with eight options in no particular order and grouped by which reason for leaving each one answers.
The rewrite you'll do whichever engine you pick
Here's what's easy to miss in the rush. Moving the data is the small job. The big job is your application code: every query, filter and named-vector setup has to be rewritten against a new client, whichever engine you choose. Leaving doesn't remove the GraphQL work. It swaps one rewrite for another.
The other side of that is good news. Weaviate's own post says nothing breaks on the announcement day and gives until the 1 May Cloud cut-off. That's enough time to test two candidates on a copy of your data before you commit, and to compare them with a GraphQL-free rewrite on Weaviate itself.
My advice before you move a single collection
Don't let a deprecation notice make a database decision for you. Rewrite your ten most common queries against the new Weaviate client and against your top two alternatives, then time each one on a copy of your data. Whichever rewrite feels least painful, with results you trust, is your answer.
If your team already runs Postgres or a search cluster, start there, because one fewer system to operate is worth a lot. Only adopt a new dedicated engine if your test shows you need it.
Matching each option to your reason for leaving
You'd rather not run another database
pgvector adds vector similarity search to Postgres, so queries stay in SQL. OpenSearch and Elasticsearch both put vectors inside a search stack, which suits teams already running one, and Elastic's version can be deployed on premises or in air-gapped networks. SingleStore keeps vectors beside transactional and analytic data in one SQL database.
You want open source or a lighter free start
Qdrant is an open source engine written in Rust you can run yourself, so there's no free-tier cluster cap to hit. Chroma is the lighter start for prototypes, running locally or on its cloud; its homepage announces the project is joining Replit, so keep an eye on what that means for the roadmap.
You want a different design altogether
Vespa is a search-first platform for teams whose Weaviate use is mostly hybrid retrieval and ranking at scale. Vectara goes the other way, a managed retrieval and agent platform for teams who'd rather buy retrieval than operate a vector database.
For a wider field, our guide to the best vector databases covers more names, and if you're new to the category, what a vector database does explains the basics.
Eight Weaviate replacements, in no particular order
Chroma
Best for: Prototypes and small builds needing vector, full-text, regex and metadata search on Apache 2.0 code, run locally or on a serverless cloud.
Elasticsearch
Best for: Vector and hybrid search from the makers of BM25 text search, deployable on any cloud, on premises or in air-gapped networks.
OpenSearch
Best for: An Apache 2.0, community-driven search and analytics suite on Apache Lucene, with a vector engine for machine learning and generative AI apps.
pgvector
Best for: Adding vector similarity search to the Postgres you already run, so there's no separate database to operate.
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Qdrant
Best for: Engineers wanting an open source vector similarity search engine written in Rust, with managed cloud, hybrid cloud and an Edge beta on offer.
SingleStore
Best for: Apps that want vectors in a real-time database handling transactions and analytics in one engine, with live reads and writes.
Vectara
Best for: Enterprises that'd rather have a platform turn multimodal data into managed AI agents and RAG context, with an on-premises option.
Vespa
Best for: Search, recommendation, personalisation and retrieval in one platform spanning vector database and tensors, run as a managed cloud or on AWS.
The eight at a glance
| Tool | Kind of engine | Answers which reason |
|---|---|---|
| Chroma | Open source, local or cloud | A light start: run locally or begin on its cloud |
| Elasticsearch | Search database with vector search | Teams already on the Elastic stack, including on premises |
| OpenSearch | Open source search suite | Adding vectors to a search stack you already run |
| pgvector | Postgres extension | No new database: vectors stay in SQL on Postgres |
| Qdrant | Open source vector engine | Self-hosting with no free-cluster ceiling |
| SingleStore | Real-time SQL database | Vectors beside transactional and analytic data |
| Vectara | Managed retrieval platform | Buying managed retrieval instead of running a database |
| Vespa | Search and retrieval platform | Search-first teams with hybrid retrieval at the core |
Questions for your migration test
Run these on a copy of your data, not on a slide deck. Release notes matter here as well: Weaviate 1.40, released on 30 Oct 2026, changes vector configuration defaults, and a medium-severity security fix shipped in v1.39.3 in October. Upgrades on any engine deserve a read, not just a run.
- How do filters translate? Metadata filters are where rewrites usually take longest, so port your hardest one first.
- What replaces named vectors? If you store several vectors per object, check how each candidate models that before you move anything.
- Who runs it at night? Self-hosting gives you control and hands you the upgrades, backups and on-call pager.
- What drives the bill? Ask how dimensions, object counts, replicas and index type affect the managed option, and see the vendor's page for the details.
- Where does data live? Data protection requirements vary by country and sector, so check with your regulator rather than relying on a vendor summary.
- Can you swap again later? Keep search calls behind your own thin service layer, so the next move is a smaller job.
Do you make a vector database or retrieval platform? Tool makers can ask to be considered for a featured place on this list.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
Is Weaviate shutting down GraphQL?
Weaviate announced on 26 Oct 2026 that its GraphQL API is deprecated. Its post says Weaviate Cloud switches it off on all clusters on 1 May in 2027, with earlier switch-off for clusters that haven't used it in 30 days.
Is the GraphQL change a reason to leave Weaviate?
No. You can rewrite your queries for Weaviate's newer client and stay. Leaving is worth it only if another tool also fixes a second problem, such as running fewer systems.
Can I use Postgres as a vector database?
Yes, with an extension that adds vector similarity search. It's a good fit if your team already runs Postgres and wants queries to stay in SQL.
How hard is it to migrate from one vector database to another?
Exporting objects and vectors is usually the easy part. Rewriting queries, filters and index settings in your application takes most of the time, so test on a copy first.
Should I self-host a vector database?
Self-hosting removes free-tier caps and gives you control, but you take on upgrades, backups and security fixes. A managed service takes that work in exchange for less control.
What should I test before choosing a Weaviate replacement?
Your most common queries, your hardest filter, multi-vector objects if you use them, recall on your own data, and how the managed option is billed.
