Timescale Strengthens and Accelerates AI Development with PostgreSQL-Based Vector Platform

Timescale, the cloud database company, is debuting Timescale Vector, a robust platform for building production AI applications at scale with PostgreSQL. Developed as a response to the ever-evolving nature of AI, Timescale Vector manages relational data, vector embeddings, time-series data, analytics, and event data from a single platform.

As the popularity of generative AI (genAI) applications increases, so does the technology it relies on: vector embeddings. Next-gen AI will require developers to store and query vector embeddings, further necessitating a database that is equipped to do so.

While this seems simple enough, it introduces a complex problem: Implementing a new, less-proven, niche database designed specifically for vector data that inevitably comes with a steep learning curve for developers, according to Timescale.

To address this problem, Timescale Vector is engineered to extend PostgreSQL—a more familiar, more trusted database—to be able to store, query, and manage vector data at scale. Not only does this greatly simplify the AI application stack, lowering its operational burden, it inherits the 30-plus years of battle-testing and reliability associated with PostgreSQL.

“We launched Timescale over six years ago with the idea that we’re more than just a PostgreSQL extension—we’re making PostgreSQL easier, faster, and more cost-effective for developers building data-intensive applications,” said Ajay Kulkarni, CEO and co-founder of Timescale. “The launch of Timescale Vector signifies our commitment to continuing to solve the biggest developer pain points so they can focus on building new AI applications more efficiently on a database foundation that’s fast, reliable and battle-tested.”

Timescale Vector accelerates ANN (Approximate Nearest Neighbor) search on millions of vectors, additionally offering pgvector’s HNSW and ivfflat indexing algorithms. This results in Timescale achieving 243% faster search speed at 99% recall as compared to Weaviate, a specialized vector database, according to the company.

Additionally, Timescale’s solution optimizes time-based vector search, using automated time-based partitioning and indexing for efficient embedding findability. Due to its foundation in PostgreSQL, users can leverage all PostgreSQL data types to store and filter metadata, JOIN vector search results with relational data, and write full SQL relational queries that incorporate vector embeddings.

Timescale Vector is currently available in early access. During its early access period, the solution will be free to use for all new and existing Timescale customers.

To learn more about Timescale Vector, please visit