Turbopuffer, the vector search vendor behind Cursor, Notion, and Anthropic, is demoting the vector index in its v3 engine from the center of the system to just one index among keyword search and filters. The video walks through the three alternatives competing for that center slot s agentic search loops, live MCP connectors, and pre-compiled knowledge files s with benchmark numbers and costs for each. The conclusion: vector DBs aren't dead, just demoted, and for most new builds the default should be an agent with cheap search tools, keyword search first.
- Turbopuffer's v3 gives every document a plain ID and makes the vector index one index among many, instead of storing documents at the address of their vector; v3 isn't in production yet, so it's partly marketing from a company running search over 10 billion Notion documents.
- On Google's FRAMES benchmark (824 questions, 12,000 Wikipedia articles), Pipeshub's agentic search scored 92.7% versus 78.9% for the best of 18 fixed RAG pipelines, and only a third of a point below being handed the exact right articles.
- The agent loop costs about 3x more per question, the slowest answer took 134 seconds versus 54 for the pipeline, and 7% of questions consumed 37% of the budget; an independent LREC study found 3x input tokens, 50% more time, and a plain reranker beating the agent at ranking.
- On Enterprise RAGBench (half a million documents from Slack, Gmail, Jira, Confluence, GitHub), BM25 keyword search answered 68.8% correctly versus 51.4% for vector search, because embedding models trained on public text don't know internal code names, acronyms, and ticket numbers.
- Vectors still win in places: GPT-5 as a search agent scored 70.1% on BrowseComp Plus with an embedding retriever versus 55.9% with BM25, and Cursor reports 12.5% better accuracy adding semantic search on top of grep.
- MCP connectors skip the index and query apps live; Microsoft's federated Copilot connectors index nothing, Anthropic's Claude marketplace opened September 23rd with 2,000+ connectors, and OpenAI now restricts app syncing to admins while Slack bans bulk exports.
- Live access is slow and expensive: Glean measured Claude co-work on live connectors at $2.98 per task against $0.58 for Glean's index with more than 3x the tokens s though Glean also added 182 live MCP server templates on September 22nd.
- The compiled-knowledge approach (Google's Open Knowledge Format) lost its own test: an August study found the wiki spent about 21x more tokens per query, so no break-even point exists, though it cited far better (40% of claims held up versus 19% for RAG); the OKF repo's last commit was August 21st.
The clearest cost-versus-accuracy tradeoff in the video, with the specific benchmark numbers that justify choosing an agent loop or not.
The agent got 92.7% right. The best of 18 fixed pipelines got 78.9... The agent costs about three times as much per question. The slowest answer in 20 took 134 seconds against 54 for the pipeline and 7% of the questions ate 37% of the budget.
A counterintuitive result with a memorable one-line explanation of why embeddings fail on internal enterprise documents.
BM25 answered 68.8% correctly, and vector search got 51.4. Vectors even lost on the questions written to favor them because embedding models trained on public text don't know your code names, acronyms, and ticket numbers... Customer IDs need equality, not a soulmate.
Customer IDs need equality, not a soulmate.
The loop wins the hard questions, but you pay for every lap.
Before you pay for another vector index, give your agent a keyword search tool.