Vector Databases Redefine Data Architecture in 2025

In 2025 what became painfully obvious was that vectors were no longer a specific database type but rather a specific data type that could be integrated into an existing multimodel database. So instead of an organization being required to use a purpose-built system, it could just use an existing database that supports vectors. For example, Oracle supports vectors and so does every database offered by Google.

Amazon S3, long the de facto leader in cloud based object storage, now allows users to store vectors, further negating the need for a dedicated, unique vector database. That doesn't mean object storage replaces vector search engines -- performance, indexing, and filtering still matter -- but it does narrow the set of use cases where specialized systems are required. No, that doesn't mean purpose-built vector databases are dead.

Much like with RAG, there will continue to be use cases for purpose-built vector databases in 2026. What will change is that use cases will likely narrow somewhat for organizations that need the highest levels of performance or a specific optimization that a general-purpose solution doesn't support. PostgreSQL ascendant As 2026 starts, what's old is new again.

The open-source PostgreSQL database will be 40 years old in 2026, yet it will be more relevant than it has ever been before. Over the course of 2025, the supremacy of PostgreSQL as the go-to database for building any type of GenAI solution became apparent. Snowflake spent $250 million to acquire PostgreSQL database vendor Crunchy Data; Databricks spent $1 billion on Neon; and Supabase raised a $100 million series E giving it a $5 billion valuation.