Launched this week
Actian VectorAI DB
The portable vector database for AI agents beyond the cloud
274 followers
The portable vector database for AI agents beyond the cloud
274 followers
Actian VectorAI DB is a portable vector database built for AI beyond the cloud. Developers can store, retrieve, and reason over data locally, delivering low-latency vector search on embedded, edge, on-prem, and hybrid systems - with a 22x QPS advantage over Milvus and Qdrant at 10M vectors. Build once, deploy consistently, without relying on cloud-native infrastructure. Teams maintain full data ownership and predictable behavior across edge, on-prem, hybrid, and cloud environments.
Actian VectorAI DB
Hey Product Hunt 👋 - I'm Tahiya. We spent years watching AI teams hit the same wall: the moment they tried to move their applications outside the cloud - to a factory floor, an edge device - their vector database stopped working. Latency spiked, connectivity dropped, data residency requirements kicked in. The infrastructure just wasn't built for it.
We've seen that most vector databases were designed for the cloud, and that was fine when AI lived there. But AI doesn't anymore. It's moving to edge devices, disconnected field environments, and embedded systems. And cloud-based databases break the moment you leave the data center.
Actian VectorAI DB is a portable vector database built for exactly this reality. You can run it on a Raspberry Pi, an NVIDIA Jetson, on-prem behind a firewall, or in the cloud - using the exact same API and architecture throughout. No re-platforming. No re-architecting.
We're launching GA today. In VectorDBBench tests at 10M vectors on identical self-hosted hardware - with zero vendor optimizations applied to any database - VectorAI DB delivered a 22x QPS advantage over Milvus and Qdrant, retaining 72% of its throughput at scale while competitors dropped to ~12% of theirs.
You can build on VectorAI DB today for:
• RAG pipelines (local, edge, or hybrid)
• Monitoring & anomaly detection
• Enterprise semantic search
Python and JavaScript SDKs. LangChain, LlamaIndex, and Hugging Face support. Runs as a Docker container: Kubernetes, Helm and Terraform compatible. Linux and Windows are supported, both on ARM and x86. Compliance-ready for ISO 27001, SOC 2 Type II, HIPAA, and GDPR.
We're building for teams who can't compromise on where their data lives. If that's you - grab the community edition or free trial, join us on Discord, and tell us what you're working on. We're reading every comment today. 🙏
RiteKit Company Logo API
@tahiya_chowdhury "Infrastructure just wasn't built for it" — that's the exact sentence every edge team has muttered. Two questions: how does VectorAI handle intermittent connectivity during vector syncs between edge and cloud, and what's your strategy for conflict resolution when two disconnected devices update the same embedding space?
Actian VectorAI DB
@osakasaul Both vector synchronization between edge devices and the cloud and replication are not yet implemented in the current version. The database follows a single-node deployment model today. However, we plan to add Replication and distributed deployment in a future released.
RiteKit Company Logo API
This is a real problem you're solving—edge AI deployments genuinely need infrastructure that doesn't fall apart when you leave the cloud. The Raspberry Pi to data center portability is compelling, and those VectorDBBench numbers are impressive. Curious how you're thinking about the developer experience for teams who've already built workflows around Milvus or Qdrant—is migration a focus for you?
Actian VectorAI DB
@osakasaul Migration is a top priority because for us tech only scales if the switching cost is low. We’ve designed the DX to be high-affinity with the tools developers already know, so if you’ve built on Milvus or Qdrant, the learning curve is nearly zero.
Open Wearables
portable vector db is exactly what's missing in this space. most solutions lock you into their cloud infrastructure which kills flexibility. what's the memory footprint like for embedded deployments? thinking about IoT scenarios where you're super constrained on resources.
Actian VectorAI DB
@piotreksedzik Actina VectorAI DB's memory footprint depends on the data size but it is extremely small. It was designed to work on small, resource constrained devices
Open Wearables
interesting to see focus on edge deployment. we've been running into latency issues with cloud vector searches for real-time wearable data processing. how does the performance hold up when you're doing frequent updates to the embeddings, not just reads? the 22x claim is impressive but curious about write performance.
Actian VectorAI DB
@piotr_pasierbek Great question! In our 10M vector tests, Actian VectorAI DB maintained a load duration of 27,170s, outperforming Qdrant Local by ~2,000s and Milvus by over 12,000s. For real-time wearables, this means we’re handling the ingestion of sensor embeddings significantly faster, which directly translates to lower CPU overhead. We’ve optimized the engine to ensure that frequent writes don't choke the query engine, which is likely where you're seeing those cloud latency spikes right now
curious how it handles intermittent connectivity — like if an edge device goes offline mid-query, does it fail gracefully or does it need a persistent connection to work?
Actian VectorAI DB
@ahmadhajj Great question! VectorAI DB doesn’t need a persistent connection to function, which is one of its core architectural advantages. The database is purpose-built to run in zero-to-low bandwidth environments.
What volume of data can it handle? In our tourism AI we have over 10 million objects, each with a lot of information, plus a vector database with general tourism information. Will it slow down?
Actian VectorAI DB
@natalia_iankovych The volume of data it can handle really depends on the hardware it's embedded in. A rough estimate would be should not be more that 70% of RAM size. You can always contact our sales team to discuss your specific use case.
Edge vector DBs are a real underserved space - most "portable" claims fall apart the second you actually deploy on a Jetson or factory PC. Curious about two things on the 22x QPS claim. What is the storage footprint at 10M vectors vs Milvus on the same hardware - is the speedup paid for in disk? And on hybrid deployments, when an edge index syncs back to a cloud cluster, is it a full rebuild or do indexes merge incrementally?