Performance that holds up in production
QPS at 10M vectors
Built for real-time AI applications that can’t afford to wait.
recall at scale
No accuracy tradeoff as your dataset grows.
milliseconds
p99 latency for consistent performance from prototype to production.
Secure AI deployment anywhere
Discover how Actian VectorAI DB helps you build and run AI applications anywhere, without cloud dependencies. This portable, local-first vector database delivers fast, predictable retrieval while keeping your data and infrastructure fully under your control.
Cloud vector database wasn’t built for your edge case
La latencia de la red impide el funcionamiento de las aplicaciones en tiempo real
Las idas y vueltas a la nube añaden entre 200 y 400 ms a cada consulta que se ejecuta. No es posible crear aplicaciones con una latencia inferior a 100 ms cuando la mayor parte de la latencia proviene de la base de datos.
La infraestructura de terceros impide las implementaciones reguladas
La HIPAA y el RGPD exigen que tus datos permanezcan bajo tu control. Los servicios en la nube implican un tratamiento por parte de terceros que no cumple con tus requisitos de cumplimiento normativo.
La arquitectura exclusivamente en la nube impide la implementación de escenarios completos
Tus dispositivos periféricos, entornos sin conexión y sistemas integrados no pueden contar con una conexión a Internet fiable. Las bases de datos en la nube dejan sin resolver categorías enteras de tus aplicaciones de IA.
From install to production in minutes
Explora los recursos y crea aplicaciones utilizando el lenguaje de programación que prefieras
Diseñado para desarrolladores en el borde
VectorAI DB de Actian posibilita una IA portátil al ofrecer:
Preguntas frecuentes
Actian VectorAI DB is a portable, local-first vector database built for AI systems that run beyond the cloud. It enables developers to run semantic and hybrid search close to their data, delivering low-latency, predictable retrieval across edge, on-prem, hybrid, and cloud environments.
Most vector databases are built for cloud-native deployments, while VectorAI DB is designed to run consistently across edge, on-prem, hybrid, and cloud environments. It delivers portable, local-first retrieval with predictable performance, including up to 22× higher QPS at scale on identical self-hosted hardware.
VectorAI DB supports modern Approximate Nearest Neighbor (ANN) indexing methods, including HNSW, for low-latency, high-accuracy search at scale.
VectorAI DB is model-agnostic and works with embeddings generated by any provider or framework. This includes OpenAI, Anthropic, Cohere, open-source models like Hugging Face, and custom or fine-tuned models.
Yes. VectorAI DB can create and store vector embeddings from multimodal data sources like text, images, audio, and video.