Loading tool details…
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 · Confirm current pricing and terms with the vendor.
A check mark means listed in that plan’s record. A dash means not listed, rather than confirmed unavailable. Verify current vendor terms.
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 →Leading multi-agent framework and platform for building, deploying, and monitoring collaborative AI agent teams.
Compare Pricing →Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.
Compare Pricing →LangGraph is LangChain’s framework for reliable agents with low-level control, deployment, observability, evaluation, sandboxes and enterprise LangSmith services.
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 →Managed vector database for RAG and AI search with serverless scaling, hybrid retrieval, and an inference layer.
Compare Pricing →Open-source AI application database with vector, full-text, and metadata search — designed to be embeddable, easy to run locally, and now offered as Chroma Cloud with usage-based serverless pricing from $5/month.
Compare Pricing →