Toyota Motor North America has decades of manufacturing knowledge, but using it with AI came with one requirement: it had to stay inside Toyota’s environment Pinecone BYOC runs there, while we manage upgrades and scaling without inbound access. BYOC is now GA 🔗

Pinecone introduces VQ-bench, an open-source framework for testing and comparing vector quantization methods. See how it makes quantizers easier to build, benchmark, and evaluate across metrics like recall, reconstruction error, and performance. Read the full breakdown on the Pinecone blog:

Dozens of vector quantization papers come out every year. When we surveyed them, we noticed that most quantizers are not new algorithms. They are the same handful of primitive operations, strung together in a different order. So we built VQ-bench: an open-source library of those primitives, composable into pipelines. E-RaBitQ, one of the strongest methods we tested, is four primitives in a list. Swap one and you have a new quantizer, evaluated exactly the way every other method is evaluated. We used it to benchmark 14 popular quantizers on recall, reconstruction error, and encode time. Two takeaways so far: PQ and OPQ have the lowest reconstruction error, and EDEN matches E-RaBitQ on recall while encoding much faster. This is a first iteration. We want your feedback, corrections, and contributions, and we will keep adding quantizers over time. Website: Blog: Repo: Paper (VecDB@VLDB 2026):

A database knows where your data is. A vector database understands what your data means. It finds information based on similarity and meaning, not just exact keywords, powering semantic search, RAG, recommendations, and agent memory. See how it works:

Most of the attention on full-text search goes to BM25. Query a part number, get that part number back first. A query like "why does PROD-001 overheat under load" has a literal string and a question in it though. That's where text-match filtering comes in. It narrows results to the documents that contain the literal string, and semantic search ranks what's left, all from one index. Full-text search is GA in Pinecone Database, text-match filtering included. 🔗

The LA Agentic AI Meetup in on the Tech Week calendar. Join us Tuesday, October 13, 5 to 7pm at Gulp in Playa Vista, for a few live demos, drinks, and real people comparing notes on what they're building and where things are going. RSVP: @Techweek_

Jörg Schad is the VP of Engineering at @pinecone. He joins @kbal11 to discuss moving beyond RAG with precomputed context, context artifacts, semantic layers, and AI retrieval. @joerg_schad

Next Tuesday, September 8, we're hosting the September LA Agentic AI Meetup, 5 to 7pm at Gulp in Playa Vista. Come hang out with builders, engineers, founders, and the AI-curious for networking, live demos, drinks, and real conversations about what people are building in AI. RSVP:

We just added a "Start demo" button to our @AWS Marketplace listing. Click it, get a pre-provisioned AWS environment, and start building on Pinecone immediately. Zero setup. Zero installation. No provisioning required. Try it out and let us know what you think:

API design has always had one reader in mind: a human developer. However agents are the new reader that looks at things a bit different. Here are some of the tenets that we now hold our API to: 1. Errors are guidance 2. Budget the reader's context 3. Self-description beats documentation 4. Safe at machine tempo 5. Access without a human in the loop 6. The agent surface is a product, not a mirror Read more: