Weaviate specialists for your dedicated team.

Where Weaviate fits in a long-term product team.

Built-in hybrid search 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 takes more upfront design and produces a data model that can be queried meaningfully as well as searched, which matters once retrieval needs to respect relationships.

What your dedicated team does with Weaviate.

Being open source means self-hosting is available, which matters where data can't go to a managed service. It also means running it, including resource planning, upgrades and backups, is your responsibility.

Choosing between managed and self-hosted on data-residency grounds is a scoping question, as set out in how the monthly fee is built.

Common Weaviate use cases.

• Hybrid retrieval without assembling it Vector and keyword fusion built in and tunable.
• 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.

Adding Weaviate skills to your team.

Weaviate work sits with the AI engineers on your team, with the hosting decision made on data-residency grounds.

At Azendo, Weaviate work sits with a dedicated AI engineering team. You can add Weaviate capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.