Weaviate
Vector database with built-in agent and query primitives, cloud or self-hosted
What is Weaviate?
Weaviate is a vector database that consolidates embeddings, ranking, vector search, and agent primitives into one platform, with managed cloud and self-hosted deployment. It exposes GraphQL and REST APIs, supports Python/Go/TS/JS SDKs, and claims a community of 50,000+ AI builders. Bought by teams who want vector search plus first-party agent and query primitives in the same product.
Tools for building, hosting, testing, observing, connecting, and giving memory or computer access to AI agents.
See the full Agent Infrastructure guide to compare more tools, buyer criteria, and related workflows.
Use cases to evaluate
RAG with first-party embedding and query agents
Hybrid search via GraphQL over enterprise data
Agentic AI applications needing built-in reasoning primitives
Multi-tenant SaaS apps that need isolated vector namespaces
Fit to evaluate
AI engineers who want a batteries-included vector platform
Teams using GraphQL elsewhere in the stack
Regulated buyers needing HIPAA + RBAC
Builders prototyping agent workflows on top of vector search
Business fit
Right for you if you like the GraphQL query model and want built-in modules for embeddings and a Query Agent without bolting them on. Skip if you want a minimal vector DB and prefer to assemble your own embedding + reranking stack. Weaviate's per-dimension pricing model is unusual and rewards careful vector size choices.
How to evaluate Weaviate
Use this category when a business wants agents that do work across tools, APIs, browsers, and data sources.
Confirm the exact workflow
Map Weaviate to one concrete workflow first, such as rag with first-party embedding and query agents. Avoid buying before the owner, trigger, output, and success metric are clear.
Check category fit
Compare tool-calling, memory, browser automation, evals, observability, and deployment controls.
Compare practical alternatives
Shortlist Weaviate against Orgo, Browser Use, Browserbase so the decision is based on fit, effort, and workflow ownership rather than brand recognition alone.
Validate cost and rollout effort
14-day free trial. Flex from $45/mo (pay-as-you-go, 99.5% SLA, $0.0139 per 1M vector dimensions, $0.255/GiB storage). Premium from $400/mo (prepaid, up to 99.95% SLA, $0.00975/1M dimensions, $0.2125/GiB storage). Query Agent add-on $30/org with 4,000 requests. Embeddings $0.025-$0.065 per 1M tokens. Also confirm implementation time, support needs, and whether the technical setup matches your team.
Compare Weaviate with alternatives
Use this quick comparison before booking demos or moving data into a new system.
| Primary workflow | RAG with first-party embedding and query agents, Hybrid search via GraphQL over enterprise data |
|---|---|
| Best-fit team | AI engineers who want a batteries-included vector platform, Teams using GraphQL elsewhere in the stack |
| Implementation effort | Technical setup and maintenance profile |
| Pricing check | Free plan + paid plans |
| Closest alternatives | OrgoBrowser UseBrowserbaseHyperbrowser |
Weaviate pricing
| Model | Free plan + paid plans |
|---|---|
| Snapshot | 14-day free trial. Flex from $45/mo (pay-as-you-go, 99.5% SLA, $0.0139 per 1M vector dimensions, $0.255/GiB storage). Premium from $400/mo (prepaid, up to 99.95% SLA, $0.00975/1M dimensions, $0.2125/GiB storage). Query Agent add-on $30/org with 4,000 requests. Embeddings $0.025-$0.065 per 1M tokens. |
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Common questions about Weaviate
What is Weaviate?
Weaviate is a vector database that consolidates embeddings, ranking, vector search, and agent primitives into one platform, with managed cloud and self-hosted deployment. It exposes GraphQL and REST APIs, supports Python/Go/TS/JS SDKs, and claims a community of 50,000+ AI builders. Bought by teams who want vector search plus first-party agent and query primitives in the same product.
What is Weaviate used for?
Common use cases: RAG with first-party embedding and query agents; Hybrid search via GraphQL over enterprise data; Agentic AI applications needing built-in reasoning primitives; Multi-tenant SaaS apps that need isolated vector namespaces.
How much does Weaviate cost?
14-day free trial. Flex from $45/mo (pay-as-you-go, 99.5% SLA, $0.0139 per 1M vector dimensions, $0.255/GiB storage). Premium from $400/mo (prepaid, up to 99.95% SLA, $0.00975/1M dimensions, $0.2125/GiB storage). Query Agent add-on $30/org with 4,000 requests. Embeddings $0.025-$0.065 per 1M tokens.
Who is Weaviate best for?
Weaviate fits AI engineers who want a batteries-included vector platform, Teams using GraphQL elsewhere in the stack, Regulated buyers needing HIPAA + RBAC, Builders prototyping agent workflows on top of vector search. Right for you if you like the GraphQL query model and want built-in modules for embeddings and a Query Agent without bolting them on. Skip if you want a minimal vector DB and prefer to assemble your own embedding + reranking stack. Weaviate's per-dimension pricing model is unusual and rewards careful vector size choices.
What are alternatives to Weaviate?
Common alternatives to Weaviate include Orgo, Browser Use, Browserbase, Hyperbrowser, Steel, Anchor Browser.