The rivalry between Snowflake and Databricks has expanded past information lakes and warehouses, shifting into the rising world of PostgreSQL.
The battle has intensified with strategic acquisitions on either side. Snowflake just lately acquired Crunchy Information for $250 million, whereas Databricks snapped up Neon for $1 billion.
Saying the acquisition in a weblog put up, Snowflake referred to as Postgres a best choice for builders as a result of its flexibility, value effectivity, and native AI options, comparable to vector assist (pg vector). Its open-source nature and vibrant ecosystem solely add to its enchantment.
“We’re tackling an enormous $350 billion market alternative and an actual want for our clients to deliver Postgres to the Snowflake AI Information Cloud,” mentioned Vivek Raghunathan, Snowflake’s SVP of engineering.
Why are the Giants Betting on PostgreSQL?
“PostgreSQL’s ecosystem and extensions are rising quick. Extra folks now know this database higher than another. pgvector gave it a giant push,” mentioned Arpit Bhayani, creator of DiceDB, when AIM askedwhy PostgreSQL is changing into the popular database for AI-native functions.
Snowflake Postgres builds on Snowflake’s earlier transfer into transactional information with Unistore, which mixes transactional and analytical workloads in a single system.
Constructing on native PostgreSQL assist, Snowflake Postgres extends that imaginative and prescient, providing enterprises a production-ready resolution for transactional functions that require Postgres compatibility.
PostgreSQL, the open-source relational database, has surged in recognition, surpassing MySQL as probably the most favoured database amongst builders, based on Stack Overflow’s 2023 and 2024 Developer Surveys.
Its skill to deal with geospatial information (by way of PostGIS), time sequence information (by way of TimescaleDB), JSON, and vector embeddings (by way of pgvector) makes it very best for AI functions.
Commenting on the acquisitions in a LinkedIn put up, senior information engineer Avinash S mentioned it’s extra than simply including one other database, calling it a strategic wager on PostgreSQL because the spine of the AI-native period, particularly in its serverless and cloud-native kind.
“Think about AI brokers spinning up databases for each real-time activity or experiment, then discarding them. Conventional databases can’t deal with this “disposable” scale. Serverless Postgres delivers the fast provisioning, elasticity, and cost-efficiency that AI brokers desperately must work autonomously and at pace,” he added.
“It’s not simply agentic. As a result of many individuals are speaking about it and utilizing it, it has change into the de facto customary,” mentioned Bhayani. Nonetheless, he added that AI brokers spinning databases looks like a robust assumption.
Equally, Factorial Advisors mentioned in a weblog put up that the Neon acquisition suits into Databricks’ broader push to construct a unified information intelligence platform.
“With over $19 billion in financing and a current $62 billion valuation, Databricks has the monetary muscle to maintain buying,” they wrote. “Following earlier buys like Tabular ($2 billion) and MosaicML ($1.3 billion), Neon helps tackle the rising demand for databases that function at ‘agentic pace’ whereas staying cost-effective via pay-as-you-go economics.”
With these acquisitions, each Snowflake and Databricks are going to problem hyperscalers like AWS, Microsoft Azure, and Google Cloud, which provide managed PostgreSQL providers tightly built-in with their AI stacks.
Neon vs Crunchy Information
Based in 2012, Crunchy Information gives a full-service, production-ready PostgreSQL resolution that features backups, excessive availability, catastrophe restoration, connection scaling, and monitoring. It helps mission-critical deployments throughout cloud, on-premises, and hybrid environments.
Snowflake mentioned its new providing, Snowflake Postgres, will deliver transactional Postgres information into its platform, accelerating innovation and giving builders larger agility, visibility, and management to construct reliable AI brokers and functions quicker.
Crunchy’s experience means Postgres-powered apps can run natively on Snowflake with out having to rewrite code. Builders profit from built-in connection pooling, efficiency metrics, and logging, making it simpler to construct and handle scalable apps.
On the opposite facet, Databricks CEO Ali Ghodsi identified that frontier LLMs have been educated on huge quantities of public information from the Postgres open-source ecosystem, making AI brokers naturally proficient in utilizing Neon, which is constructed on Postgres.
Ghodsi added that Databricks and Neon share a typical basis in technical infrastructure and a deep perception in open supply. Databricks began the Apache Spark undertaking at UC Berkeley, additionally the birthplace of Postgres, the open-source database on which Neon is constructed.
He added that OLTP databases, a $100 billion market, are nonetheless dominated by decades-old merchandise. With Neon, Databricks goals to assist disrupt this area by creating probably the most developer- and AI agent–pleasant database platform.
When Neon turned usually out there final 12 months, round 30% of the databases on the platform have been being created by AI brokers moderately than people. However in a current replace, that quantity has jumped to over 80%. In different phrases, AI brokers at the moment are creating greater than 4 instances as many databases as human customers.
Everybody’s speeding to PostgreSQL, and Snowflake and Databricks are leaping in by choosing up area of interest suppliers. It’s not nearly extra databases, it’s about preparing for AI, dwell information, and large enterprise wants.
Furthermore, these acquisitions mirror a broader consolidation pattern within the information and AI infrastructure market.
Latest offers comparable to Salesforce’s $8 billion acquisition of Informatica, ServiceNow’s buy of Information.World, and Alation’s acquisition of Numbers Station, present how firms are racing to construct complete AI-ready platforms. In keeping with Bhayani, a lot of that is pushed by the push to accumulate clients and specialised experience.
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