Complete pricing guide for Weaviate. Compare all plans, analyze costs, and find the perfect tier for your needs.
Not sure if free is enough? See our Free vs Paid comparison →
Still deciding? Read our full verdict on whether Weaviate is worth it →
Pricing sourced from Weaviate · Last verified March 2026
Detailed feature comparison coming soon. Visit Weaviate's website for complete plan details.
View Full Features →Weaviate supports multi-node replication with configurable consistency levels (ONE, QUORUM, ALL) for both reads and writes. The RAFT-based consensus protocol handles leader election and data synchronization across nodes. Built-in backup functionality supports S3, GCS, and filesystem targets. Weaviate Cloud provides managed high-availability with automatic failover and 99.9% uptime SLA.
Yes, Weaviate is fully open-source (BSD-3 license) and designed for self-hosting via Docker or Kubernetes. The official Helm chart supports production Kubernetes deployments with configurable replicas, resource limits, and persistent storage. Weaviate Embedded runs in-process for development and testing. Self-hosted deployments require managing dependencies like the vectorizer modules and configuring HNSW index parameters for optimal performance.
For self-hosted deployments, the main cost driver is memory — HNSW indexes must fit in RAM for optimal query performance. Use product quantization (PQ) to compress vectors and reduce memory requirements by up to 90%. On Weaviate Cloud, costs are based on storage units and compute tiers. Optimize by choosing appropriate vector dimensions, using tenant-based data isolation to avoid over-provisioning, and configuring async indexing for write-heavy workloads.
Weaviate's open-source nature significantly reduces migration risk — you can always run it yourself. The schema-first data model and module-dependent vectorization create some coupling. Mitigate by generating and storing embeddings externally rather than relying on Weaviate's vectorizer modules, using the REST API directly rather than module-specific features, and maintaining export routines via the objects API for data portability.
AI builders and operators use Weaviate to streamline their workflow.
Try Weaviate Now →Open-source Python framework for orchestrating role-playing, autonomous AI agents that collaborate as a 'crew' to complete complex tasks.
Compare Pricing →Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.
Compare Pricing →LangGraph is LangChain's open-source framework for building stateful, durable, multi-agent workflows in Python and JavaScript with graph-based control flow.
Compare Pricing →SDK for integrating cutting-edge LLM technology into applications, with support for building AI agents and connecting model capabilities into existing app workflows.
Compare Pricing →Fully managed vector database for RAG and AI search with serverless storage, hybrid sparse-dense indexes, integrated embedding and rerank models, and managed retrieval workflows.
Compare Pricing →