Platform · 2022–now · AI Present
Vector DB gold rush
Specialized stores for embeddings. Many workloads are folding back into Postgres extensions — after the vendor tour.
Vector DBs mutated retrieval stacks but many teams only needed pgvector and good chunking. The gold rush sold standalone infra for demos that outgrew nothing. Specialized stores stick for scale; hype sold them to everyone.
Context
The stack gets a co-pilot
AI pair programming is already changing how code is written. Agent frameworks and vector stores are still sorting winners from demos. The durable layer will look familiar: evals, retrieval quality, product UX, and ownership. Autopilot rewrites without tests are just big-bang migrations with better slides.
Compare with
Related
Framework · 2023–now
LLM app frameworks
Chains, agents, and prompt wrappers shipping weekly. The durable pieces will be boring: evals, retrieval, and product UX.
Platform · 1974–now
SQL / Relational
Declarative data that outlived every ORM fashion cycle. Postgres and friends keep winning by being boring.
Platform · 2010–now
Elasticsearch
Full-text search and log analytics at scale. Powerful, hungry, and everywhere in observability stacks.
Platform · 2023–now
Postgres + pgvector
Embeddings back in the database you already run. The quiet winner of the vector DB gold rush.
Practice · 2023–now
RAG as default architecture
Retrieval-augmented generation as the answer to every knowledge problem. Often right; often Postgres with pgvector would suffice.
$ Teams stood up vector pipelines, chunking strategies, and rerankers before asking if fine-tuning or a SQL query would answer the question. Retrieval infra became the product.