Platform · 2022–now · AI Present
Vector DB gold rush
Specialized embedding stores sold as the default. Mid-market folded back into Postgres — after the vendor tour.
How Vector DB gold rush mutated
The gold rush sold standalone vector infra for demos that outgrew nothing. Specialized stores stick at extreme scale; everyone else needed an extension, good chunking, and fewer invoices. The hype layer faded; the retrieval problem did not.
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Framework · 2023–now
LLM app frameworks
LangChain-class glue mutated into MCP clients and thin wrappers around vendor APIs. The durable pieces are still 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 default for mid-market RAG — not the quiet alternative.
Practice · 2023–now
RAG as default architecture
Retrieval-augmented generation as the answer to every knowledge problem — then "just stuff the window" as the counter-fad. 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.
Practice · 2023–now
Embed everything
If it is text, vectorize it. Semantic search cosplay for problems that needed a better filter.
$ Embedding pipelines for tickets, PDFs, Slack, and the cafeteria menu — then nobody measured retrieval quality. Vector bills and reindex jobs became the product.