Tiger Data has unveiled a managed PostgreSQL database service tailored specifically for AI agents, asserting that traditional database architectures are ill-equipped for a future dominated by autonomous software agents. The New York-based company, operating under the name Timescale Inc., has launched the Ghost service to meet the increasing demand for infrastructure that facilitates large-scale experimentation by coding and workflow agents. This service is now generally available.
With a decade of experience, Tiger Data has transitioned from developing a PostgreSQL extension for time-series data to establishing a robust database platform utilized by over 3,000 customers, as noted by co-founder and Chief Executive Ajay Kulkarni. Ghost is fundamentally built on PostgreSQL but reimagines the provisioning and consumption of databases by AI systems.
“The idea is that all new applications are being built with coding agents,” Kulkarni explained. “We believe the agent is the future packaging of software. Instead of an application, users want outcomes.” However, this shift towards agent-driven development introduces new infrastructure requirements, as agents often experiment, create temporary environments, and test various approaches.
“They don’t always do it safely,” Kulkarni added. “The database layer can get really expensive and dangerous if you let your agents experiment and go wild.” To mitigate these risks, Ghost enables agents to spin up unlimited databases with a feature called “fast forking,” allowing users to create exact, independent copies of datasets in mere minutes or even seconds, circumventing lengthy copying processes. Tiger Data adopts a pricing model based on active compute usage rather than per-database licensing.
“Databases are free,” Kulkarni stated. “You could have one database per agent or one database per task. You can create it in seconds.”
Copy-on-write storage
The Ghost service leverages Tiger Data’s proprietary Fluid Storage technology, a copy-on-write storage layer that permits multiple database instances to share underlying data blocks. Users are only charged for data that changes, rather than for data copies. Kulkarni likened this architecture to pointers that reference common storage until modifications occur. “If one database starts diverging and writes new blocks to disk, it only has to keep track of the new blocks,” he explained.
Tiger Data positions Ghost as an evolution of PostgreSQL, aimed at modernizing it for agents rather than replacing it. The company underscores its compatibility with the broader PostgreSQL ecosystem, supporting popular extensions such as TimescaleDB, pgvector, and PostGIS.
Early adopters have begun utilizing Ghost in unexpected ways, according to Kulkarni. “It’s kind of serving as a scratch pad for agents,” he remarked. He recounted an instance where the platform was employed to analyze operational data, with an AI assistant creating a temporary database, loading a dataset, conducting analysis, and subsequently discarding the environment.
Despite the emergence of new AI-centric data infrastructures like vector databases, Kulkarni remains confident that PostgreSQL will continue to be central to AI development. “I would be surprised if the database for agents is not Postgres,” he asserted. “Postgres is reliable, it’s flexible, it’s extensible. It also has a huge ecosystem and community.”
Tiger Data currently employs 200 individuals across 25 countries and has successfully raised 0 million in funding.
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