Weaviate 1.39 Release

Weaviate 1.39 Release
Weaviate 1.39 promotes the Boost API and MMR diversity selection to GA, previews 4-bit Rotational Quantization, and ships an experimental Search REST API.

Weaviate 1.39 promotes the Boost API and MMR diversity selection to GA, previews 4-bit Rotational Quantization, and ships an experimental Search REST API.

When a Weaviate query is slow, the first question is where the time went. Query profiling returns a per-stage, per-shard timing breakdown, making query performance issues visible.

This release brings the HFresh disk-based vector index and the built-in MCP Server to general availability, rebuilds cluster-wide async replication to run from a single scheduler (on by default), and adds two previews: the Boost API and Nested Object Filtering.

Server-side batching, retries, the blobHash data type, and multimodal ingestion — what to use when, with code.

Use Weaviate's built-in MCP server to give Claude Code, Cursor, and VS Code hybrid search over your codebase and docs. No glue code.

Tokenization makes or breaks hybrid search. See how Weaviate's accent folding, custom stopwords, and /v1/tokenize endpoint power multilingual BM25.

This release introduces the built-in MCP Server, Extensible Tokenizers, Diversity Search (MMR), and Query Profiling as previews, along with Incremental Backups, Gemini audio support for multi2vec-google, and the new BlobHash property type.

This release introduces HFresh vector index (Preview), and brings Server-side Batching, Object TTL, Async Replication Improvements, Drop Inverted Indices, and Backup Restoration Cancellation to general availability.

This release introduces Object Time-to-Live (TTL), zstd compression support, flat index RQ quantization, multimodal support with Weaviate Embeddings, runtime configurable OIDC certificates and much more.

1.34 introduces flat index support with RQ quantization, server-side batching improvements, new client libraries, Contextual AI integration and much more.

Hands-on patterns: Design pattern for gen-AI enterprise applications, with Arize AI.

Learn how Weaviate's native multi-tenancy architecture delivers scalable vector search with one shard per tenant, dynamic resource management, and true data isolation.