The 2023 Hype Hangover
When the Generative AI boom hit, every engineering team panicked. We realized we needed to store “Embeddings” (data translated into numbers) to build RAG systems. So, we all swiped our corporate cards and bought dedicated Vector Databases like Pinecone or Weaviate.
Fast forward to today. The hype has settled, and CTOs are looking at their AWS bills. They are looking at the nightmare of syncing their main SQL databases with an external Vector DB. They are realizing a hard truth: Vectors are a feature, not a database.
The "Syncing" Nightmare
Having a dedicated Vector Database creates a massive architectural headache: Data Duplication.
Let’s say you have a product catalog in PostgreSQL.
To make it searchable for your AI, you generate embeddings and send them to Pinecone.
What happens when a product price changes? Or gets deleted?
You now have to build complex, brittle ETL pipelines just to keep the two databases in sync. If the sync fails, your AI hallucinates old data.
The Rise of the Unified Data Layer
The database giants woke up. Today, you don’t need a specialized tool to store vectors. You can store them right where your data already lives.
PostgreSQL has
pgvector. You can store your relational data and your embeddings in the exact same row.Snowflake, Databricks, and BigQuery now have native vector search built-in.
When your vectors live next to your business data, you can do hybrid queries:
“Find me documents semantically similar to ‘battery issues’ (Vector Search) WHERE the customer is in the ‘Enterprise’ tier (SQL Filter).” Doing that across two separate databases is a nightmare. Doing it in one is a single query.
Is your AI infrastructure bloated? Find out if you can eliminate your Vector DB and save thousands.
Having a dedicated Vector Database creates a massive architectural headache: Data Duplication. Vectors are a feature, not a database. Keep your embeddings right next to your relational data. #DataEngineering #PostgreSQL #VectorDB
Security & Compliance
Every time data leaves your VPC to go to a third-party SaaS Vector DB, your CISO loses sleep.
Embeddings are a mathematical representation of your proprietary data. They must be protected.
By using pgvector or your existing Data Warehouse, the data never leaves your established security perimeter. No new vendor reviews. No new SOC2 compliance checks.
Conclusion: Simplify to Scale In Enterprise software, boring is beautiful.
Don’t add a new moving part to your architecture unless you absolutely have to. Stop building a Franken-stack. Consolidate your vectors, cut your SaaS bills, and simplify your pipelines.
Audit Your Architecture! See if your data layer is ready for consolidation.
