October 2025 Summaries

Tiger Data's recent endeavor focused on developing an AI-native application called Eon, a Slack-native assistant designed to convert institutional knowledge into instant answers by leveraging conversational memory, context, and reliability. The project, which became widely used within the company in just six weeks, involved tackling three primary challenges: building memory that understands time using TimescaleDB, providing context without noise through Model Context Protocol (MCP) servers for platforms like GitHub and Linear, and ensuring reliability with features like durable event processing and automatic retries. The team open-sourced the components developed, including tiger-agents-for-work for Slack agent frameworks, and MCP servers for GitHub, Linear, and documentation search, allowing others to replicate or innovate upon their work. This initiative highlights the importance of durable event handling and focused tools for AI agents, showcasing the potential of using Postgres infrastructure for AI applications and marking the beginning of a broader journey into developing agentic capabilities.

Orca Protocol, a leading decentralized exchange (DEX) on Solana, processes over $500 million in daily trading volume by leveraging advanced technological infrastructure to optimize price discovery and minimize slippage across various token types like blue chips and stablecoins. To meet the demands of high-frequency trading and large-volume swaps, Orca utilizes a Concentrated Liquidity Market Maker (CLMM) and employs TimescaleDB on AWS to efficiently manage real-time blockchain indexing, complex financial calculations, and sub-second analytics, ensuring performance without compromise. The transition to Tiger Data's PostgreSQL platform has dramatically improved query performance, with response times reduced to 300-500ms, facilitated by advanced features like time-based partitioning and continuous aggregates. This infrastructure supports Orca's goals of providing institutional-grade performance while maintaining the decentralized ethos of the DeFi ecosystem, positioning it as a pivotal component of Solana's trading landscape and allowing for scalable growth in the future.

Fluid Storage is a next-generation storage architecture designed to provide a forkable, ephemeral, and durable infrastructure, particularly for agent-driven environments. This innovative system operates as a distributed block layer, offering true elasticity, synchronous replication, and zero-copy forks, making it compatible with databases like PostgreSQL and others. Fluid Storage emerged as a solution to address the limitations of existing elastic storage systems, such as Amazon EBS, which often result in slow scaling and inefficiencies due to over-provisioning. It unifies elasticity, iteration, and durability within its architecture, allowing for instant forks and snapshots without incurring downtime or excessive costs. The system's multi-tenant design ensures high utilization and cost efficiency by billing based on actual consumption rather than allocated space. Fluid Storage's architecture comprises a distributed key-value block store, a storage proxy layer, and a user-space storage device driver, together providing seamless performance, fast recovery, and the ability to scale fluidly to meet the dynamic needs of modern agentic workloads. Tiger Cloud's free-tier databases now run on Fluid Storage, allowing developers to experience this fluid infrastructure firsthand, with opportunities for early-access partnerships for those developing agentic or infrastructure platforms.

Agentic Postgres introduces a novel approach to database management through the use of zero-copy forks, allowing developers to safely test and deploy AI-generated database migrations and optimizations without impacting production data. Leveraging Fluid Storage technology, these forks enable fast, isolated, and cost-effective testing environments that share the same physical data as the production database, efficiently managing storage by only separating modified data. This system empowers developers to test AI-generated migrations against real production data, catch potential incidents before they occur, and optimize performance queries by experimenting with actual data conditions. The framework supports various forking strategies to suit different testing needs, from real-time data analysis to reproducing historical events, enhancing continuous integration and deployment pipelines. By enabling developers to test and validate changes safely and quickly, Agentic Postgres aims to make deploying updates on Fridays less daunting, ultimately increasing productivity and reducing the anxiety associated with database management.

pg_textsearch is a new PostgreSQL extension designed to enhance AI-native applications by providing a modern BM25 ranking system, combining vector and keyword search capabilities within a single database. This extension addresses the limitations of Postgres' native full-text search by introducing improvements like inverse document frequency weighting, term frequency saturation, and length normalization to ensure high-quality search results. It is particularly beneficial for systems such as Retrieval-Augmented Generation (RAG) and chat agents that rely on precise and contextually relevant information retrieval. The extension integrates seamlessly with Postgres and provides a hybrid search approach that combines the conceptual similarity of vector search with the precision of keyword matching, enhancing the performance and relevance of search results for AI applications. The preview release focuses on a memtable layer for fast in-memory operations, with future plans to incorporate disk-based segments and advanced query optimizations.

The blog post discusses the Tiger MCP server, a tool designed to enhance AI's ability to design and manage PostgreSQL databases by providing structured knowledge and guidance based on 35 years of Postgres expertise. Unlike other MCP servers that act as simple API wrappers, the Tiger MCP server leverages detailed prompt templates and versioned documentation to equip AI with the necessary context and best practices for creating robust, maintainable, and efficient database schemas. This approach addresses common pitfalls in AI-generated SQL, such as inconsistent ID types and inefficient indexing, by automatically applying proven design patterns and modern PostgreSQL features. The server enables AI to perform tasks like schema design with minimal manual intervention, ensuring that databases are built with scalability and performance in mind. The post highlights the importance of using the right context and tools to transform AI into subject matter experts, particularly in complex fields like Postgres, where traditional documentation fails to meet AI's needs.

Agentic Postgres introduces a free plan designed to facilitate AI development on Postgres by removing financial barriers and focusing on experimentation. The plan, known as the Tiger Free Plan, allows developers and AI agents to build and test their ideas without the constraints often imposed by traditional free database tiers, such as limited features and trial expirations. With up to 750 MB of storage per service and the ability to fork databases, run real-time analytics, and use built-in time-series and columnar features, the plan is tailored to support modern AI workflows. The free plan encourages seamless transition to production, as all plans use the same APIs, and it is available without requiring credit card information. Although it is optimized for experimentation rather than production, users can easily upgrade to a paid plan if their projects outgrow the free tier. Existing customers can integrate the free services into their accounts for testing and side projects, ensuring that developers can explore, learn, and build effectively with minimal friction.

Agentic Postgres is a newly launched database designed specifically for AI agents, aiming to revolutionize the way developers interact with databases by reducing management complexity and enhancing performance. Developed by Tiger after a decade of expertise in building high-performance systems, Agentic Postgres introduces a new MCP server that enables agents to efficiently interact and understand database operations, and incorporates advanced features like native full-text and semantic search through enhanced Postgres extensions. The database also includes a copy-on-write block storage layer that allows for instant, efficient database forking, facilitating safe experimentation and parallel operations. In addition, Agentic Postgres is powered by Fluid Storage, offering a scalable and elastic storage solution that operates like a local disk, ensuring high throughput and flexibility. The platform is designed to elevate developers by enabling them to focus on creative and impactful work while agents handle routine tasks, and it offers a new CLI and free tier to encourage immediate adoption and experimentation.