DEV Community: Ajay Kulkarni

Why a 40-Year-Old Database Is Still Winning in the AI Era

Ajay Kulkarni — Tue, 01 Sep 2026 17:00:25 +0000

There are still some fundamental truths that won't change. LLMs are making it dramatically easier to build software, so yes, we're going to have more applications, more agents, more workloads, and more data. But here's one thing that won't change: developers want to build on trusted foundations. They don't want to stitch together fragile, Rube Goldberg architectures. They want infrastructure they know, they trust, and they can bet their business on, for the next decade. That's why Postgres continues to win, even as AI reshapes the world.

Despite turning 40 years old this year, and having a name only a parent could love, Postgres is still the database developers love most. It's reliable. It's extensible. It's open. And over four decades it has grown into an incredible platform, spanning time-series, geospatial, vector, analytics, and much more, all backed by the largest developer ecosystem in databases.

The irony is that developers don't want innovation in their database. They want innovation everywhere else. They want their database to be dependable. Predictable. Boring.

So we asked the people who know Postgres best: with agents and LLMs reshaping the stack at breakneck speed, why is a 40-year-old technology like Postgres still dominating?

Mike Freedman, Co-Founder and CTO of Tiger Data

Postgres scales because it was built on first principles: transactional correctness, query planning that actually works, semantics you can depend on, and a robust ecosystem of open source extensions. The teams seeing 200x performance improvements aren't jumping to new systems. They're understanding their workloads deeply and letting Postgres do what it was built to do. That doesn't change with LLMs. If anything, it matters more.

Postgres actually made a bet 15 years ago for growth and experimentation with its extension model. This fits massively into the concept of open-source development and experimentation: Let a 1000 flowers bloom. Allow people to customize for use cases, and allow open-source ecosystems thrive (including with "coopetition") rather than restricting decisions through a foundation/core developers. The core database which moves a bit slower, more deliberatively, safety and correctness above all. And then the extension framework, which allows rapid experimentation and innovation.

Chris Preimesberger, Freelance Journalist for The New Stack and SDxCentral

PostgreSQL has always been a keeper. This is largely because its architecture has proven remarkably adaptable to each new wave of computing, from client-server applications, to cloud-native platforms, and now AI-driven workloads. While newer databases often optimize for a single use case, PostgreSQL is astute: it has evolved into a highly extensible platform that supports transactional processing, analytics, geospatial data, vector search, and AI applications without forcing enterprises to abandon existing investments. Its open source governance model has encouraged a vast ecosystem of contributors, tools, and cloud services, giving enterprises confidence that they won't be locked into a single vendor. As agents and LLMs accelerate software development, PostgreSQL benefits from being the database developers already know, trust, and can integrate into modern architectures with minimal friction. While so much else is changing around it, PostgreSQL's combination of stability, flexibility, and trustiness has made it the default foundation upon which much of the new AI-powered stack is being built.

Stephen O'Grady, Principal Analyst & Cofounder of RedMonk

No single project characteristic is responsible for it. But PostgreSQL advocates and vendors typically point to the advantages of its broader community, which again is enabled by its liberal license. Multi-entity open source means more commercial options, which large enterprises favor because competition limits vendors' leverage with respect to pricing and it limits their risk to vendor behavior and changes. It can also mean broader project support and faster innovation, as evidenced by the speed at which PostgreSQL has been able to adapt to emerging market demands by adding the ability to handle new workloads from JSON to vector.

Carl Olofson, Independent Analyst (former Research Vice President at IDC)

Relational database systems like PostgreSQL are not competing with agents and LLMs, but rather cooperating. LLMs by themselves are great if all the data you want to access in your AI system is unstructured (text files, email, harvested collaborative system content, etc.), but if you want useful answers to questions involving the structured data that drive applications, and by extension, the enterprise, you must include databases that deliver current and consistent deterministic response with guaranteed 100% accuracy. SQL has been the preferred access language for decades, and the most commonly encountered SQL database system is PostgreSQL, largely because it is fairly easy to manage and most importantly, it's open source. Old does not necessarily mean outdated, and new does not mean better.

Paul Gillin, Enterprise Editor at SiliconANGLE

In an industry that considers "old" a dirty word, Postgres demonstrates that age can also mean reliability, familiarity and adaptability. In fact, AI has amplified the need for databases that adapt to multiple data types and can function as an operational store, metadata store, document store, search substrate and support vector embeddings. Postgres has adapted well to changing needs thanks to its flexible architecture and large, enthusiastic developer community. While it still lags Oracle, MySQL and Microsoft SQL Server in overall popularity, it is a true open-source alternative that appears to have the momentum to be a safe and reliable choice for many years to come.

Conclusion

Different vantage points, but the same conclusion keeps surfacing: Postgres isn't winning the AI era in spite of its age, it's winning because of what that age represents. Decades of battle-tested correctness, a governance model that spreads trust across a whole ecosystem rather than one vendor, and an extension architecture that lets new capabilities (vector search, real-time analytics, AI workloads) bolt onto a stable core instead of requiring a new database altogether.

Agents and LLMs may be changing how fast software gets built, but they haven't changed what developers need it to run on.

🚀 Introducing Agentic Postgres: The First & Free Database Built for Agents

Ajay Kulkarni — Tue, 21 Oct 2025 15:15:26 +0000

Agents are the New Developer

80% of Claude Code was written by AI. More than a quarter of all new code at Google was generated by AI one year ago. It’s safe to say that in the next 12 months, the majority of all new code will be written by AI.

Agents don’t behave like humans. They behave in new ways. Software development tools need to evolve. Agents need a new kind of database made for how they work.

But what would a database for agents look like?

At Tiger, we’ve obsessed over databases for the past 10 years. We’ve built high-performance systems for time-series data, scaled Postgres across millions of workloads, and served thousands of customers and hundreds of thousands of developers around the world.

So when agents arrived, we felt it immediately. In our bones. This new era of computing would need its own kind of data infrastructure. One that still delivered power without complexity, but built for a new type of user.

What Agents Actually Need

Agents work differently than humans. They need:

MCPs, not UIs – they call functions, not click buttons
Native search – find the right data instantly
Fast forks and teardown – spin up experiments without the overhead
Efficient pricing – pay for what you use
Built-in knowledge – best practices that come with the database

What We Built

1. An MCP Server That Actually Understands Postgres

We built an MCP server that doesn't just connect to the database—it knows how to use it well. We took 10+ years of Postgres experience and turned it into built-in prompts. Agents get tools for schema design, query optimization, and migrations, plus they can search Postgres docs on the fly.

> I want to create a personal assistant app. Please create a free 
> service on Tiger. Then using Postgres best practices, describe 
> the schema you would create.

2. Search Built Into the Database

pgvectorscale: We improved our vector search extension. Better indexing throughput, better recall, lower latency than pgvector.

pg_textsearch: Our newest extension. It implements BM25 for proper ranked keyword search, built for hybrid AI apps. Right now it uses an in-memory structure for speed—disk-based segments with compression are coming.

No need to bolt on external search. It's all in Postgres.

3. Instant Database Forks

We built a copy-on-write storage layer that makes databases instantly forkable. Full production data, isolated environment, seconds to create. No data duplication, no cost duplication. You only pay for what changes.

Great for testing, benchmarking, or running migrations in parallel without touching prod.

> Create a fork of my database, test 3 different indexes 
> for performance, delete the fork, and report findings.

4. New CLI and a Free Tier

Three commands to get started:

# Install the Tiger CLI and MCP
$ curl -fsSL https://cli.tigerdata.com | sh
$ tiger auth login
$ tiger mcp install

Then either tell your agent to create a free service, or run tiger create service yourself.

Fluid Storage

This all runs on Fluid Storage—our new distributed block store. It's built on local NVMe with a storage proxy that handles copy-on-write volumes.

What you get:

Instant forks and snapshots
Automatic scaling, no downtime
Over 100K IOPS and 1 GB/s per volume

It looks like a local disk to Postgres but scales like cloud storage. Every free service runs on it.

Try It Today

$ curl -fsSL https://cli.tigerdata.com | sh

Built for agents. Designed for developers.

🐯 🚀 Timescale is now TigerData: Building the Modern PostgreSQL for the Analytical and Agentic Era

Ajay Kulkarni — Wed, 18 Jun 2025 16:09:24 +0000

TL;DR: Eight years ago, we launched Timescale to bring time-series to PostgreSQL. Our mission was simple: help developers building time-series applications.

Since then, we have built a thriving business: 2,000 customers, mid 8-digit ARR (>100% growth year over year), $180 million raised from top investors.

We serve companies who are building real-time analytical products and large-scale AI workloads like: Mistral, HuggingFace, Nvidia, Toyota, Tesla, NASA, JP Morgan Chase, Schneider Electric, Palo Alto Networks, and Caterpillar. These are companies building developer tools, industrial dashboards, crypto exchanges, AI-native games, financial RAG applications, and more.

We’ve quietly evolved from a time-series database into the modern PostgreSQL for today’s and tomorrow’s computing, built for performance, scale, and the agentic future. So we’re changing our name: from Timescale to TigerData. Not to change who we are, but to reflect who we’ve become. TigerData is bold, fast, and built to power the next era of software.

Developers Thought We Were Crazy

When we started 8 years ago, SQL databases were “old fashioned.” NoSQL was the future. Hadoop, MongoDB, Cassandra, InfluxDB – these were the new, exciting NoSQL databases. PostgreSQL was old and boring.

That’s when we launched Timescale: a time-series database on PostgreSQL. Developers thought we were crazy. PostgreSQL didn’t scale. PostgreSQL wasn’t fast. Time-series needed a NoSQL database. Or so they said.

“While I appreciate PostgreSQL every day, am I the only one who thinks this is a rather bad idea?” – top HackerNews comment on our launch (link)

But we believed in PostgreSQL. We knew that boring could be awesome, especially with databases. And frankly, we were selfish: PostgreSQL was the only database that we wanted to use.

*Today, PostgreSQL has won. *

There are no more “SQL vs. NoSQL” debates. MongoDB, Cassandra, InfluxDB, and other NoSQL databases are seen as technical dead ends. Snowflake and Databricks are acquiring PostgreSQL companies. No one talks about Hadoop. The Lakehouse has won.

*Today, agentic workloads are here. *

Agents need a fast database. We see this in our customer base: private equity firms and hedge funds using agents to help understand market movements (“How did the market respond to Apple WWDC 2025?”); industrial equipment manufacturers building chat interfaces on top of internal manuals to help field technicians; developer platforms storing agentic interactions into history tables for greater transparency and trust; and so on.

What Started as a Heretical Idea Is Now a Thriving Business

We have also changed. We met in September 1997, during our first week at MIT. We soon became friends, roommates, even marathon training partners (Boston 1998).

That friendship became the foundation for an entrepreneurial journey that has surpassed even our boldest imaginations.

What started as a heretical idea is now a thriving business:

2,000 customers
Mid 8-digit ARR, growing >100% y/y
200 people in 25 countries
$180 million raised from top investors
60%+ gross margins
Cloud usage is up 5x in the last 18 months, based on paid customers alone.

And that’s only the paid side of the story. Our open-source community is 10x-20x larger. (Based on telemetry, it’s 10x, but we estimate that at least half of all deployments have telemetry turned off.)

TimescaleDB is everywhere. It’s included in PostgreSQL offerings around the world: from Azure, Alibaba, and Huawei to Supabase, DigitalOcean, and Fly.io. You’ll also find it on Databricks Neon, Snowflake Crunchy Bridge, OVHCloud, Render, Vultr, Linode, Aiven, and more.

We Are TigerData

Today, we are more than a time-series database. We are powering developer tools, SaaS applications, AI-native games, financial RAG applications, and more. The majority of workloads on our Cloud product aren’t time-series. Companies are running entire applications on us. CTOs would say to us, “You keep talking about how you are the best time-series database, but I see you as the best PostgreSQL.”

So we are now “TigerData" We offer the fastest PostgreSQL. Speed without sacrifice.

Our cloud offering is “Tiger Cloud.” Our logo stays the same: the tiger, looking forward, focused and fast. Some things do not change. Our open source time-series PostgreSQL extension remains TimescaleDB. Our vector extension is still pgvectorscale.

Why “Tiger”? The tiger has been our mascot since 2017, symbolizing the speed, power, and precision we strive for in our database. Over time, it’s become a core part of our culture: from weekly “Tiger Time” All Hands and monthly “State of the Tiger” business reviews, to welcoming new teammates as “tiger cubs” to the “jungle.” As we reflected on our products, performance, and community, we realized: we aren’t just Timescale. We’re Tiger. Today, we’re making that official.

This is not a reinvention: it’s a reflection of how we already serve our customers today.

Polymarket uses TigerData to track their price history. During the last election Polymarket ramped up 4x when trade volumes were extra high, to power over $3.7 billion dollars worth of trades.

Linktree uses TigerData for their premium analytics product, saving $17K per month on 12.6 TB from compression savings. They also compressed their time to launch, going from 2 weeks to 2 days for shipping analytical features.

Titan America uses TigerData’s compression and continuous aggregates to reduce costs and increase visibility into their facilities for manufacturing cement, ready-mixed concrete, and related materials.

Lucid Motors uses TigerData for real-time telemetry and autonomous driving analytics.

The Financial Times runs time-sensitive analytics and semantic search.

Come Join Us

Tiger is the Fastest PostgreSQL. The operational database platform built for transactional, analytical, and agentic workloads. The only database platform that provides Speed without Sacrifice.

This is not a rebrand, but a recommitment to our customers, to our developers, and to our core mission.

If this mission resonates with you, come join us. Give us product feedback. Spread the word. Wear the swag. Join the team.

It’s Go Time. 🐯🚀