The Postgres Race

There’s a database battle happening right now, and most people don’t even know it started.

Modern use cases require extremely fast, extremely reliable operational databases directly connected to data platform intelligence systems.

In 2025, both Snowflake and Databricks acquired and integrated Postgres companies. Databricks acquired Neon for ~$1 billion, quickly introduced an offering called Lakebase within a month, and told the world it’s “the database for the AI era.” Bold claim and perhaps premature.

Across the aisle, Snowflake quietly acquired Crunchy Data — a battle-tested, FedRAMP-compliant Postgres shop with real enterprise credentials — and launched Snowflake Postgres with a very different positioning: “production-ready Postgres.”

That difference in positioning reflects two very different approaches to enterprise readiness—one designed for production at scale, the other still evolving toward it.

What we’re Seeing in Practice

Across evaluations and customer discussions, Snowflake Postgres has shown material advantages in performance, cost efficiency, and scalability over Lakebase.

In observed workloads:

• Performance has been multiple times higher:  2x the performance
• Cost has been meaningfully lower:  half the cost

Additionally, storage capacity is substantially larger with Snowflake Postgres, with fewer near-term scaling constraints: Snowflake Postgres gives you 8x more storage than Lakebase. Lakebase hits a ceiling at just 8 TB.

These differences become more pronounced as workloads approach enterprise scale.

Postgres Versioning Matters

Then there’s the versioning tradeoff that isn’t getting much attention (and it matters).

Snowflake Postgres runs the latest Postgres 18 with high availability and performance in a single offering. Based on current documentation, Lakebase appears to require tradeoffs across configurations—for example, selecting newer Postgres versions with certain limits, or earlier versions to access high availability and scaling features.

Lakebase made Postgres 17 generally available in February 2026, following its initial release in September 2024 —resulting in a meaningful gap for teams looking to adopt new features.  In contrast, Snowflake made Postgres 18 generally available in February 2026.

In practice, Lakebase teams may need to choose between database version and operational capabilities like HA. Snowflake Postgres teams get both.

The Pedigree Problem

Where each product came from matters enormously when you’re betting real workloads on a database.

Crunchy Data — the foundation of Snowflake Postgres — was already serving customers like UPS and federal government agencies before Snowflake’s acquisition. That’s years of hardening, security validation, and enterprise-grade operational maturity. Baked in.

Neon — the basis for Lakebase — was a promising startup that, in the same week Databricks was closing its $1 billion acquisition, experienced two publicly documented outages, totaling approximately 5.5 hours during which customers were unable to create or start databases. Databricks rebranded, integrated, and introduced Lakebase as an enterprise offering within roughly 30 days of closing the acquisition.

From our perspective, one of these foundations arrived with years of enterprise hardening. The other arrived with a $1 billion price tag and a postmortem blog post.

The Requirements that Matter at 2 AM

I’ve been in enterprise data long enough to know that the features that matter most are the ones your team is scrambling for at 2 AM when something breaks. That’s where platforms separate themselves.

Let’s start with reliability.

Snowflake Postgres offers a published 99.95% uptime SLA with high availability and built-in backup and recovery. Based on publicly available information, we have not seen a clearly documented SLA for Databricks Lakebase at the time of writing.

Security follows a similar pattern.

Snowflake Postgres provides PrivateLink, VPC connectivity, and customer-managed encryption keys as part of a standard enterprise deployment model. In contrast, comparable capabilities in Lakebase appear to be configuration-dependent, in preview, or still evolving based on current documentation.

Then there’s replication and integration.

Lakebase uses a different architecture that does not follow standard Postgres replication patterns. As a result, teams may need to take alternative approaches when integrating with external systems or implementing traditional replication strategies.

None of these differences show up in a product announcement.

They show up when something fails—and your team has to fix it fast.

Why Postgres Matters to the Enterprise

This isn’t just about databases—it’s about where operational and AI-driven applications converge.

Postgres has become the default for:

• Transactional systems
• Microservices and application backends
• Real-time operational data

In the AI era, enterprises are increasingly trying to:

• Combine operational data (Postgres)
• With analytical and AI platforms

The winning approach isn’t replacing Postgres—it’s bringing it closer to governed, scalable data platforms.

That’s why this battle matters.

The Bottom Line

Based on what we’re seeing today:

Snowflake Postgres is a production-ready, enterprise-first implementation of Postgres inside a governed data platform.  And it is hard to ignore the results we’ve observed in practice—including 2x better performance, half the cost, and 8x the storage capacity. Combined with an actual documented SLA, Postgres 18, enterprise security on day one and built on a foundation that was already serving big customers with real production workloads, the platform stands out.

Lakebase might get there. Snowflake Postgres is already there.

If you’re making a decision today—and care about performance, security, and operational maturity—there is a clear difference in readiness.

For questions, please reach out to us at info@squadrondata.com and let’s talk through your situation. These comparisons reflect my professional opinion based on publicly available information and hands-on platform experience.