Weaviate Development

An open-source vector database that bundles hybrid keyword and vector search, and multi-tenancy, for retrieval systems that want the database to do more.

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What Weaviate is

Weaviate is an open-source vector database. It stores objects with their vectors and properties, and offers hybrid search that blends BM25 keyword scoring with vector similarity in a single query.

It supports multi-tenancy, where each tenant's data is isolated within one collection, and optional modules that can generate embeddings for you. It runs self-hosted or as a managed cloud service.

Suitability

And when it is not. Choosing the wrong tool costs more than any implementation detail.

Good fit for

Retrieval needing hybrid keyword and semantic ranking without assembling two systems
Multi-tenant products where many customers each have their own searchable data
Teams that want the database to handle embedding generation through its modules
Self-hosted or managed deployments, chosen per client constraint

Consider alternatives for

Small corpora inside an existing PostgreSQL system, where pgvector is simpler
Teams wanting the smallest possible operational footprint and a single transactional store
Cases where embedding generation must be tightly controlled in your own pipeline and not delegated to the database

In practice

Hybrid search for knowledge bases

Search that respects exact terms such as product codes and names while still matching on meaning.

Multi-tenant retrieval

Giving each customer isolated searchable content inside a shared system, with tenant boundaries enforced by the database.

Self-hosted retrieval

Deploying inside a client's own infrastructure when a hosted vector service is ruled out.

Considerations

Practical notes from running this in production.

01
Weaviate is a capability we take on per engagement, not part of our standing production stack. We will say so up front and verify the approach on a sample of your data.
02
Letting the database generate embeddings is convenient but couples your data to a model choice. Many teams prefer to generate embeddings in their own pipeline so they can change model deliberately.
03
Hybrid search has a weighting between keyword and vector scores. Tune it against real queries, since the best balance differs between content types.
04
As with any self-hosted stateful service, backups, upgrades and capacity planning are yours to run unless you use the managed offering.

FAQ

Ask us directly

When is Weaviate a better choice than pgvector?

When the collection is large, when you want built-in hybrid search and multi-tenancy without building them yourself, or when you want vector workload separated from your transactional database. For a moderate corpus inside an existing Postgres system, pgvector is usually simpler.

How does Weaviate compare with Elasticsearch for hybrid search?

Elasticsearch has a long track record for keyword relevance and analytics and now supports vectors too. Weaviate was built around vectors with keyword search added. If your system is already on Elasticsearch, extending it may be less work than adding a new database; if retrieval is the core of the product, a vector-first engine may fit better. We decide from your queries.

Do you have Weaviate production experience?

We treat it as a capability taken on per engagement and do not claim delivered Weaviate projects. Our primary retrieval stack is PostgreSQL with pgvector and Pinecone. If Weaviate is the right fit for you, we will scope a proof of concept first.

How long does a Weaviate development project take?

It depends on scope. A focused first release is usually weeks rather than months, while larger platforms are delivered in phases. We give a written estimate after a short discovery conversation and agree milestones before work starts.

How is Weaviate development work priced?

We work either to a fixed scope with agreed milestones or on a time-and-materials basis, whichever suits how well the requirements are known. You receive the estimate and its assumptions in writing before committing.

By industry

Where we apply Weaviate. Tiles marked "Guide" open a dedicated write-up; the rest open the industry page.

Let’s talk about your Weaviate project

Tell us what you are trying to build or fix. We will come back with scope, approach and an honest view on cost and timeline.

What Weaviate is

Weaviate is an open-source vector database. It stores objects with their vectors and properties, and offers hybrid search that blends BM25 keyword scoring with vector similarity in a single query.

It supports multi-tenancy, where each tenant's data is isolated within one collection, and optional modules that can generate embeddings for you. It runs self-hosted or as a managed cloud service.

Weaviate Development

What Weaviate is