Loading tool details…
Compare listed plans and check current offer terms with Weaviate
The catalog lists this free option; confirm its current limits and eligibility with Weaviate
💡 Pro tip: Start with the free tier to test if Weaviate fits your workflow before upgrading to a paid plan.
per month
per month
per month
This guide does not establish that Weaviate has an active coupon, education discount, or seasonal promotion. Confirm current offers on the vendor's website before paying.
If Weaviate's pricing doesn't fit your budget, consider these ai database alternatives:
Leading multi-agent framework and platform for building, deploying, and monitoring collaborative AI agent teams.
Free tier available
✓ Free plan available
Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.
Free tier available
✓ Free plan available
LangGraph is LangChain’s framework for reliable agents with low-level control, deployment, observability, evaluation, sandboxes and enterprise LangSmith services.
Free tier available
✓ Free plan available
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.
Start with the free tier and upgrade when you need more features
Get Started with Weaviate →Listed prices may change. Confirm current prices and offer terms with the vendor.