Weaviate.
Weaviate is an open-source vector database with a GraphQL interface, built-in hybrid search combining vector and keyword retrieval, optional modules that generate embeddings, and both self-hosted and managed deployment.
Where Weaviate fits on a long engagement.
Hybrid search being built in rather than assembled is Weaviate's strongest practical feature. Combining semantic similarity with keyword matching usually outperforms either alone, and having the fusion implemented and tunable removes work that teams otherwise do badly.
The schema is more structured than most vector stores require: classes, properties and cross-references are declared. That is more upfront design and it produces a data model that can be queried meaningfully rather than only searched, which matters once retrieval needs to respect relationships.
What an assigned team does with Weaviate.
Being open source means self-hosting is genuinely available, which matters where data cannot go to a managed service. It also means operating it — resource planning, upgrades, backups — is yours.
Deciding between managed and self-hosted on data-residency grounds rather than convenience is a scoping question, and what it covers is set out in how the monthly fee is built.
What we use Weaviate for.
- Hybrid retrieval without assembling it Vector and keyword fusion built in and tunable rather than hand-rolled.
- Relationships in the retrieval model Cross-references so retrieval can respect structure, not only similarity.
- Self-hosted where data cannot leave Full control of the deployment, with the operational load that implies.
How Weaviate capacity is assigned.
Weaviate work is assigned inside AI capacity, with the hosting decision made on data-residency grounds rather than convenience.
Tell us what your roadmap needs Weaviate for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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