Apr 19, 2025 · 27m · latent-space
The Rise and Fall of the Vector DB category: Jo Kristian Bergum (ex-Chief Scientist, Vespa)
gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
Former Vespa Chief Scientist Jo Kristian Bergum analyzes why the standalone vector database category is declining and shares practical guidance on building resilient, hybrid search architectures for AI systems.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. How this is scored →
speaking balance: gold is the hosts, purple is the guest (3 minute bins)
Jo Kristian immediately shuts down the host's question about PostgresML, declaring that executing large SQL queries to handle embeddings inside the database creates serious DX, cost, and scaling issues.
Hardest push from the hosts ▶ 20:07 Dismissing long-context hype as engagement farmingThe host calls out commentators claiming long-context windows will kill RAG, labeling their arguments nonsense designed purely for social media engagement.
Biggest teaching moment ▶ 10:27 Detailing PGVector's feature leap over vector databasesJo Kristian explains how PGVector rapidly implemented advanced capabilities like HNSW, IVFFlat, halfvec, and binary quantization, outperforming many standalone vector database startups.
The host holds their own ▶ 6:51 Benchmarking category fundraising against MongoDBThe host brings concrete market data, showing that the 230 million dollars poured into vector DB startups exceeded the entire lifetime VC raise of MongoDB, proving the category was overfunded.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | The hosts as informed peer | Guest teaching | Guest disagreement | The hosts pushing back | Why |
|---|---|---|---|---|---|---|
| The Emergence and Pitfalls of Dedicated Vector Databases | 5 | 3 | 3 | 2 | The host highlights his early involvement in the vector DB ecosystem by mentioning he wrote the OpenAI cookbook Chroma example. Jo Kristian details his 20-year search background, clarifying that while vector DB companies might survive by pivoting into general search engines, the standalone vector DB infrastructure category itself is dead. | |
| Venture Hype, Embeddings Utility, and Database Architectures | 6 | 4 | 2 | 3 | The host demonstrates domain expertise by comparing vector DB funding rounds to MongoDB's entire lifetime venture raise and questions the convergence of database storage and search indexing. Jo Kristian provides technical depth, explaining why pure semantic similarity fails without web search signals like freshness and detailing how PGVector rapidly caught up to dedicated vector engines. | |
| Recommender Systems, Practical RAG Architecture, and PostgresML | 5 | 5 | 5 | 2 | The host asks about the exact operational sequence for building retrieval pipelines and queries Jo Kristian on running models inside databases like PostgresML. Jo Kristian firmly rejects PostgresML's approach, arguing against embedding logic in SQL and explaining the multi-stage cascade architectures used in production search and recommender systems. | |
| Long Context Limits, Graph RAG, and Embedding Frontiers | 5 | 4 | 3 | 3 | The host dismisses the long-context-versus-RAG narrative as engagement farming and prompts Jo Kristian on Graph RAG and vision-based embedding models. Jo Kristian nuances the long-context debate with specific token calculations and demystifies Graph RAG by emphasizing that knowledge graph extraction is the real hurdle rather than graph database storage. |