The Lakebase Vision Is Right. Who Will Build It First?

Databricks deserves credit for clearly articulating this direction. But the harder question remains: when will it actually be ready?

Today’s Databrick’s Lakebase architecture relies on fragile pipelines and multiple data copies. It’s optimized for human-scale data engineering and batch processing, not for AI agents generating thousands of transactions per second while demanding real-time analytics. Achieving strong consistency, low latency and massive concurrency requires a fundamentally different architecture.

That’s why we believe the real race isn’t about who has the best marketing manifesto, but who can deliver a system that actually powers AI at scale.

What Databricks got right

The Lakebase concept addresses real pain points that anyone building modern data systems recognizes immediately:

• Data silos are killing innovation. Companies are drowning in zero value-add ETL pipelines, data copies and integration complexity. Your AI models need real-time access to both operational data and historical analytics — and architectural friction overwhelms progress.
• AI changes everything about data consumption patterns. Human users access data predictably. AI agents don't. They might spin up thousands of concurrent analytical queries for seconds, then disappear. They generate massive transaction volumes while simultaneously requiring complex analytical insights for decision-making. Traditional systems weren't designed for this.
• Open table formats are inevitable. Delta Lake and Iceberg aren't just storage efficiency wins — they're strategic freedom. When your data lives in vendor-neutral formats, you can apply the best compute engine for each workload without being locked into architectural decisions you made years ago.
• Friction between OLTP and OLAP systems is too high. Every time you move data between systems, you're paying taxes in latency, storage costs and operational complexity. The platforms that eliminate these taxes will have fundamental advantages.

Databricks deserves credit for connecting these dots and painting a clear picture of where the industry needs to go. The question is whether anyone can actually build what they're describing.

What AI scale actually requires

After working with companies deploying AI systems at scale, we've learned that the requirements go far beyond what traditional data platforms can deliver:

Real transactional scale for AI workloads

AI-driven systems generate and consume data at breathtaking rates. Recommendation engines, autonomous agents, real-time fraud detection and trading systems may simultaneously ingest millions of events and execute complex analytical queries. Lakebase supports high-concurrency writes — but true AI-scale needs seamless consistency as well.

Data consistency that AI can trust

AI agents must rely on accurate state. Lakehouse architectures that rely on eventual consistency introduce unacceptable risk. Lakebase offers ACID transactions and data sync to Delta Lake, but the entire ecosystem from ingestion to analytics must maintain strict consistency if automated decisions are to be trusted.

True zero-friction data movement

The lakehouse promise sounds compelling: one copy of data, accessible by all workloads. But look at how it actually works. Data originates in fleets of isolated OLTP databases then is ingested into lake storage, then compute engines like Databricks and Snowflake pull that data into their processing layers. That's not zero-copy — that's multiple discrete storage engines with extra network hops. Lakebase promises to manage the CDC between Delta and Postgres, but behind the scenes, there is still a collection of fragile traditional CDC pipelines.

Efficient updates at lake scale

Data lakes have traditionally been append only. While Delta Lake supports deletes and merges, performance and cost degrade at scale. Lakebase enables transactional writes and merges explicitly, and the underlying system handles branching and storage snapshots intelligently. But merging branches, historical corrections or compliance-driven deletes still introduce complexity at petabyte-scale deployments.

The Postgres option

Postgres faces its own AI-scale challenges that require significant community investment to solve:

• Connection scaling for AI agents (thousands of simultaneous clients, each with memory overhead).
• Distributed write throughput without relying solely on WAL and single-node constraints.
• Global consistency across nodes without sacrificing ACID semantics or relying on DIY sharding. 

These ideas are solvable — but require massive community investment in features like distributed write coordination, lightweight branching and hyper-concurrency support.

Architecture that actually delivers

While others are still theorizing, SingleStore has built a full-featured data platform tough enough to handle transactional and analytic workloads — ready for the large scale and complexity of AI-powered applications.

• Native open format integration that queries Iceberg and Delta tables directly from object storage, with intelligent caching that keeps frequently accessed data in memory for millisecond response times.
• Unified transactional and analytical processing that maintains strong consistency across both operational updates and complex analytical queries, without the architectural compromises that create dangerous gaps for automated decision-making.
• Massively parallel architecture designed to handle thousands of concurrent AI agents generating transactions while running analytical queries across petabytes of data — without the network bottlenecks that plague compute-storage separation.
• Efficient large-scale data lifecycle management that automatically manages data across high-performance operational storage and cost-effective lake storage based on access patterns, while supporting complex updates, deletes and merges across the entire dataset without performance penalties.

The key insight is that AI-scale demands require architecture designed specifically for these unified workloads, not retrofitted systems trying to bridge different architectural paradigms through integration layers.

Reality check: Vision vs. execution

Databricks envisions the minimal viable blueprint for AI-scale data infrastructure. But vision is only part of the equation. The true frontier is execution at real-world scale, and measurement under pressure.

If you’re an enterprise architect or startup founder aiming to build or choose a platform that works at AI scale, look closely at Lakebase today. But look even closer at how it performs when AI agents are rewriting, rebranching and demanding sub-10 ms responses at scale.

The vision is right and the race is on to build the architecture that actually delivers.

Want to learn more about databases? We are going to drop in-depth content around how databases work and the optimization choices behind them. And in the meantime, you can explore and start free with SingleStore.