CedarDB bets on one database for transactions and analytics

Databases have traditionally been built for one of two jobs: analytical databases help businesses spot trends and answer questions, while operational databases store day-to-day transaction records, like orders and payments. The Munich-based database engine CedarDB supports both, and its approach has gained relevance with the rise of agentic AI, co-founder and CEO Moritz Sichert told TechCrunch.

CedarDB has been in the making for the last decade, but its current momentum stems from enterprise companies whose existing data stacks suited storing years of transactions but struggled to quickly answer analytical questions asked in natural language about that data. The company is a Technical University of Munich spinout based on Umbra, a research project built to beat legacy database engines at processing speed using modern hardware. With five Umbra researchers as co-founders, CedarDB kept that approach, promising faster performance without more CPUs, power or memory, and requiring few changes from customers.

In particular, the team made CedarDB compatible with PostgreSQL, an open-source database system. According to Sichert, that compatibility keeps switching costs low, since users don’t need to rebuild their apps, and it has an added advantage: CedarDB follows the logic LLMs are most familiar with from their training. Co-founder Christian Winter detailed other priorities the team set after leaving academia, “such as reliability, fault tolerance and compatibility with popular tools,” in a blog post.

The team debated whether to launch a cloud offering right away, and in the end chose to launch CedarDB Enterprise as an on-premise offering alongside its free CedarDB Community Edition, both announced in May last year — the same time it announced a €5.3 million seed funding round (about $6 million). The cloud offering was only delayed, not abandoned: after beta testing with users on its waitlist, the company announced limited availability of CedarDB Cloud on Wednesday onstage at TechCrunch Disrupt, where it’s one of 20 startups competing in the Startup Battlefield.

A fully managed, cloud-hosted version will likely help CedarDB expand its user base beyond large enterprises, and better prepare it to compete with Databricks, a major data company that appears to have reached a similar conclusion: at its Data + AI Summit in June, Databricks cited the rise of AI agents as the reason for launching LTAP, a new architecture meant to unify transactions, analytics, streaming and operational data on a single copy of storage in the lake. Sichert interprets Databricks’ move as another sign of demand for what CedarDB does, though it’s too early to tell how disruptive it might be.

The startup plans to serve its first cloud users with its current team of 14 people, but Sichert expects it will need a larger team once it gets more traction — which is when, he said, the company would raise its next round.

Why it matters

CedarDB’s bet reflects a broader shift as AI agents generate both transactional and analytical queries in the same workflow — a demand pattern big vendors like Databricks are now also racing to serve, which could reshape how enterprises architect their data stacks.