Weaviate

What is Weaviate?

Weaviate is a vector database that stores data objects together with their vectors, so applications can combine similarity search with structured filtering in a single query. Developers use it for semantic and hybrid search, retrieval-augmented generation, recommendations and AI agents. You can self-manage the open-source database or use Weaviate Cloud, which offers shared and dedicated deployments with replication, role-based access control, multi-tenancy, vector compression and backups. Built-in AI services run inside the cloud: Weaviate Embeddings hosts embedding models billed per million tokens, and the Query Agent turns natural-language questions into database operations. Cloud clusters run on AWS, with GCP and Azure on higher plans, and the company states SOC 2 Type II and HIPAA compliance for qualifying tiers. Weaviate Cloud has an always-free sandbox-style plan at $0/mo, a pay-as-you-go Flex plan that starts at $45/mo, and a prepaid Premium plan sold through sales.

Use cases

• Run semantic search over documents
• Build hybrid keyword and vector search
• Power retrieval for RAG apps
• Build recommendation features
• Isolate customer data with multi-tenancy
• Query data in natural language with an agent
• Store embeddings from hosted models

Who it's for

• AI engineers
• Backend developers
• Data teams
• Enterprise platform teams

Why use Weaviate

• Hybrid search and filtering in one query reduces the need for a separate search engine next to the vector store.
• A free plan and a $45/mo Flex entry point let you start small before committing to a contract.
• You choose between self-managing open source and a managed cloud with uptime SLAs.
• Hosted embeddings and the Query Agent run next to your data, so fewer external services are needed.
• Multi-tenancy and RBAC support SaaS products that keep customer data separate.

Weaviate features

• Vector and structured filtering in one query
• Hybrid search combining keyword and vector
• Multi-tenancy for per-customer data
• Vector compression and flexible index types
• Replication and highly available clusters
• Role-based access control
• Weaviate Embeddings hosted models
• Query Agent for natural-language questions
• Backups with 7 to 45 days retention on paid plans
• SSO and SAML on Premium
• HIPAA compliance and PrivateLink on dedicated Premium

Weaviate integrations

• AWS
• Snowflake Arctic Embed

How to get started with Weaviate

1. Sign up for Weaviate Cloud, or install the open-source database yourself.
2. Create a cluster and a collection, and choose a vector index and compression setting.
3. Load your objects with vectors, or let Weaviate Embeddings generate them.
4. Query with vector, keyword or hybrid search, adding structured filters, or ask questions through the Query Agent.
5. Add replication, backups and a larger plan as the workload moves to production.

Weaviate limitations

• Free plan allows 100,000 objects, 1 GB memory, 10 GB disk and 1 collection
• Free plan is limited to 3 tenants and one cluster per user
• Free plan has no backup retention or replication
• Data transfer is free only during a promotional period and charges may follow
• Premium pricing requires contacting sales

Weaviate pricing

Yes, Weaviate has a free plan. Paid plans start at $45 per month, according to the official pricing page on 24 Oct 2026.

Weaviate plans

• Free

  $0/mo

  • 100,000 objects
  • 1 GB memory and 10 GB disk
  • 1 collection, up to 3 tenants
  • Basic support

• Flex

  Starts at $45/mo

  Pay-as-you-go, monthly

  • Shared cloud with replication
  • RBAC
  • 99.5% uptime
  • 7-day backup retention
  • Standard support

• Premium

  Custom

  Prepaid contract, contact sales

  • Shared or dedicated deployment
  • Up to 99.95% uptime
  • SSO and SAML
  • Enterprise support

Plans as listed on the official pricing page on 24 Oct 2026. Prices can change and may vary by country or exclude tax. Check the official pricing page.

Weaviate alternatives

The main Weaviate alternatives listed on Debuly are Pinecone, Qdrant, Chroma, pgvector, Baseplate and Zilliz Cloud. The table compares pricing and platforms; order is A–Z.

1. Ba

   Baseplate

   Backend for LLM apps: a hybrid database for data and embeddings, search, an app builder and API endpoints

   Developer platformsAutomation and agents
   FreemiumVisit Baseplate (official site)
2. 

   Chroma

   Open-source search database for AI apps, with vector, full-text, regex and metadata search and a serverless cloud option.

   Developer platformsResearch and searchAutomation and agents
   Open sourceVisit Chroma (official site)
3. 

   pgvector

   Open-source PostgreSQL extension that adds vector columns, similarity search and HNSW and IVFFlat indexes to your existing database.

   Developer platformsCoding
   Open sourceVisit pgvector (official site)
4. 

   Pinecone

   Managed vector database for AI search and RAG, with dense, sparse and full-text indexes, inference and an Assistant API.

   Developer platformsResearch and search
   FreemiumVisit Pinecone (official site)
5. 

   Qdrant

   An open-source vector database and search engine for AI workloads, available self-hosted or as Qdrant Cloud with a free tier.

   Developer platformsResearch and searchAutomation and agents
   FreemiumVisit Qdrant (official site)
6. 

   Zilliz Cloud

   Fully managed vector database from the team behind Milvus, with free, serverless and dedicated clusters plus on-demand lake-scale compute.

   Developer platformsResearch and search
   FreemiumVisit Zilliz Cloud (official site)

Weaviate FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from docs.weaviate.io · fetched 24 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

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