Emma K McGrattan

We're excited to announce VectorAI DB, the first vector database purpose-built for high-performance, reliable AI at the edge. RAG isn't dead. It just can't run in the environments that need it most. In manufacturing, 46% of AI pilots never leave the OT network. Healthcare, defense, and financial services are no different. VectorAI DB runs RAG pipelines, semantic search, and real-time AI agents on-premises, at the edge, or air-gapped. Before today, 1,000+ devs across three hackathons had already built on it. A maritime AI system. An on-device AI therapist. Cardiac imaging on a closed hospital cluster. And these are just a handful of what VectorAI DB makes possible. VectorAI DB is NOW OPEN to the public. Come throw your hardest problem at it. Download link in the comments.

@GonnabeNikhil This is incredible, Nikhil!

@emmakmcgrattan github.com/TryingtobeingN… made this crazy project using vector AI DB which works locally fully offline

We're excited to announce VectorAI DB, the first vector database purpose-built for high-performance, reliable AI at the edge. RAG isn't dead. It just can't run in the environments that need it most. In manufacturing, 46% of AI pilots never leave the OT network. Healthcare, defense, and financial services are no different. VectorAI DB runs RAG pipelines, semantic search, and real-time AI agents on-premises, at the edge, or air-gapped. Before today, 1,000+ devs across three hackathons had already built on it. A maritime AI system. An on-device AI therapist. Cardiac imaging on a closed hospital cluster. And these are just a handful of what VectorAI DB makes possible. VectorAI DB is NOW OPEN to the public. Come throw your hardest problem at it. Download link in the comments.

@javarevisited VectorAI DB supports HNSW indexing at the edge

Everyone is obsessed with 'Cloud-Scale' AI, but the real $100B opportunity is in 'Offline-Scale' AI. Manufacturing and Healthcare don't need faster APIs; they need reliable local compute. Does VectorAI DB support HNSW indexing at the edge, or are you using something more lightweight?

@stevenjhammond We’re model-agnostic! The database doesn't generate the embeddings, so you have total flexibility. You can generate them offline using local models to keep the data loop air-gapped.

@emmakmcgrattan Where are the embeddings generated? Can they be done offline too or are we still relying on OpenAI for this part?

@zulkarnaimx Exactly. If a drop in bars means a drop in intelligence, it's not field-ready.

@emmakmcgrattan Asked the VectorAI DB team what happens when the network drops mid-query. queries keep resolving against the local index. that's the only acceptable answer for field deployments and someone finally shipped it.

@heyfionaai Agreed

@emmakmcgrattan cloud vector search made sense in the experimentation phase. at production volumes with regulated data it breaks on cost and on compliance. VectorAI DB launched today. the infrastructure is catching up.

@pannaa_ai Bingo. You can't optimize your way out of the speed of light.

Cloud vendors didn't break real-time analytics. Physics did.' that's from an Actian piece and it's the most accurate one-line summary of the edge AI problem I've read. light in fiber travels at ~200,000 km/s. US coast-to-coast is ~100ms for the round-trip alone. real-world cloud latency runs 200-500ms. a robotic welding controller adjusting torque in <10ms can't route through the cloud. ever.

@linaa_ai Exactly. By keeping the data local, you eliminate the legal headache before it starts.

Option two is an on-premise database where the data never crosses a border. GDPR Article 44 becomes an architectural fact rather than an ongoing legal question. VectorAI DB is option two. launched today at AI Dev 26. what does your current AI stack look like under a GDPR review?

@kubilayarkan1 Spot on! We launched VectorAI DB specifically to end that tension. You shouldn't have to choose between advanced AI and strict data sovereignty.

Actian's CTO Emma McGrattan used her AI Dev 26 mainstage slot on April 28 to launch a vector database built for on-premise, air-gapped, and edge environments. Two conversations I keep having with CTOs in regulated industries: 'we want to deploy AI on the factory floor' and 'legal won't let us send embeddings to a cloud provider.' Those used to be in tension. VectorAI DB closes that gap.

@MakadiaHarsh Glad you found it cool. Hardware is the ultimate reality check

@emmakmcgrattan Working with hardware recently making me realise how cool this is.

@AIwithArsalan Agreed

@emmakmcgrattan VectorAI DB feels like a smart bet on where real world AI actually has to run

@BharukaShraddha Precisely. We shifted the focus from "what can RAG do" to "where can RAG live." Once you remove the connectivity and compliance constraints, the architecture becomes much more interesting.

@emmakmcgrattan Love this direction. The real constraint was never RAG, it was where you could actually run it. Excited to see what people build with this 🔥

@steve_social_ Thanks

@emmakmcgrattan Nice

@Aria_Nawi Spot on. On a factory floor, a WAN drop shouldn't mean a line stoppage.

@emmakmcgrattan VectorAI DB makes manufacturing AI simpler: → real-time QC even with WAN drops → sub-150ms defect lookup → on-prem knowledge base → seamless on-site model updates Worth a look for factory AI setups

@HeyNayeem That's exactly the shift we're pushing for.

@emmakmcgrattan This feels like a shift from “cool AI demos” to actually usable systems in the real world — especially where cloud just isn’t an option.

@maarcoofdezz Env: Intel Xeon Gold 6138 (8 core / 64GB) Data: 1M vectors @ 768-dim Load: Concurrent, pure vector search

@emmakmcgrattan VectorAI DB is claiming 1,040 QPS and 12.7ms p99. what hardware? what dimensions? sequential or concurrent? filtered or pure vector? not saying the numbers are wrong. saying they're meaningless without the test conditions.

@Diptish09 Broad SDK support was a non-negotiable for us.

@emmakmcgrattan didn't expect a JavaScript SDK from an edge vector database. VectorAI DB has one. REST and gRPC too. if your product ships AI and the data can't leave the customer's environment this is probably relevant to you.

@Abdullah__Ai7 Dependency hell is a massive productivity killer. We intentionally kept it to one package to avoid those runtime conflicts.

The most common production failure in Python vector database setups is packaging. ChromaDB throws a runtime error if you have both chromadb and chromadb-client installed. takes hours to diagnose. VectorAI DB is one package, one endpoint, same behavior everywhere. pip install actian-vectorai and you're running. [producthunt.com/products/actia…]

Had great fun presenting my keynote at AI Dev 26! Thanks for having me @DeepLearningAI!

@Ulobex Love to hear it. Making RAG "hatefuly easy" was the goal. Glad the LangChain integration made the setup a non-issue for you.

@emmakmcgrattan wired VectorAI DB into a LangChain pipeline this morning and I kinda hate how easy it was. native support, queries resolve locally. if you're building RAG inside a HIPAA environment this removes the legal conversation entirely. [producthunt.com/products/actia…]