Évaluez le coût de votre déploiement de VectorAI DB
Processeurs : 4 au minimum
* Prix estimé en fonction de la configuration. Le devis final peut varier.
Pricing
Choisissez le niveau qui convient à vos charges de travail d'IA
Community
Le plus populaireGratuit pour toujours
Gratuit
Vector Capacity
5K
déploiement
des machines de développement locales
Support
Communauté
Starter
Le plus populaireEssai gratuit de 30 jours
417 $ / mois
Vector Capacity
1M
déploiement
de serveurs / machines virtuelles de petite taille
Support
Argent
Growth
Built for Production
1 250 $ / mois
Vector Capacity
5M
déploiement
de serveurs de grande capacité / machines virtuelles
Support
Argent
Entreprise
Le plus populaireBuilt for large-scale deployment
Personnalisé
Vector Capacity
10M+
déploiement
de l'infrastructure d'entreprise
Support
Argent
Bord
Le plus populaireBuilt for embedded devices
Personnalisé
Vector Capacity
Custom
Deployment Environment
Devices / Embedded Systems
Support
Personnalisé
Les tarifs mensuels indiqués sont facturés annuellement. Vous avez des questions ou besoin d'aide ? Contactez-nous
Déployez VectorAI DB où vous le souhaitez
VectorAI DB supports flexible deployment environments, including local development, embedded applications, and enterprise infrastructure.
plateformes prises en charge plateformes :
- Self-managed cloud.
- Bare metal environments.
- On-prem infrastructure.
- Embedded/edge deployments.
Fonctionnalités par niveau
Scale seamlessly with VectorAI DB.
Community |
Starter |
Growth |
Entreprise |
Bord |
|
|---|---|---|---|---|---|
Capacité vectorielle |
5 000 vecteurs |
Environ 1 million de vecteurs |
Environ 5 millions de vecteurs |
Plus de 10 millions de vecteurs |
1 000 vecteurs |
cas d'usage principaux |
Developer experimentation |
Small AI applications |
Mid-scale AI workloads |
Large production deployments |
Embedded AI |
Support |
Community |
À déterminer |
À déterminer |
À déterminer |
OEM/Entreprise |
Deployment |
Local dev machines |
Small servers / VMs |
Large servers / VMs |
Enterprise infrastructure |
Devices / embedded systems |
Système d'exploitation |
Linux / Windows / Mac |
Linux / Windows / Mac |
Linux / Windows / Mac |
Linux / Windows / Mac |
Linux |
SDK |
Python JavaScript |
Python JavaScript |
Python JavaScript |
Python JavaScript |
C++ |
processeur |
x86 / ARM64 |
x86 / ARM64 |
x86 / ARM64 |
x86 / ARM64 |
ARM64 |
Architecture |
Client server |
Client server |
Client server |
Client server |
In-process runtime |
Persévérance |
Sur disque |
Sur disque |
Sur disque |
Sur disque |
Device storage |
Modèle de mise à l'échelle |
Vertical |
Vertical |
Vertical |
Vertical |
Au niveau de l'appareil |
Modèle de licence |
Perpetual (Dev Only) |
Abonnement |
Abonnement |
Enterprise subscription |
Perpetual |
Disponibilité |
Site web |
Site web |
Site web |
Sales |
Sales/OEM channel |
Utilisation en production |
Non |
Oui |
Oui |
Oui |
Oui |
Redistribution commerciale |
Non |
Fabricant de serveurs |
Fabricant de serveurs |
Fabricant de serveurs |
Embarqué |
FAQ
Yes. Actian VectorAI DB Community Edition is free forever for developers working on small projects, experiments, and prototypes. When you sign up for Community Edition, you also get a 30-day full-featured trial of VectorAI DB that starts immediately and supports up to 1 million vector embeddings. After the trial, Community Edition remains available for free with up to 5,000 vector embeddings on local development machines. For production use, higher capacity, or commercial redistribution, choose a paid plan.
VectorAI DB pricing is primarily based on vector capacity. The pricing calculator above helps estimate the right package based on the number of vectors, embedding dimension, precision, metadata size, and recommended hardware. It also supports license stacking, so teams can scale capacity incrementally by combining license tiers and paying for the storage they need.
Yes. VectorAI DB supports flexible deployment environments, including bare metal, on-prem infrastructure, self-managed cloud, embedded, and edge deployments.
Edge is designed for embedded and edge AI applications that need vector search inside a device, application, or OEM product. Unlike the standard client-server editions, Edge uses an in-process runtime, is built for device-level deployment, supports ARM64 on Linux, and uses an embedded/OEM licensing path. The other paid tiers are client-server deployments for small servers, large servers, VMs, or enterprise infrastructure.
Not Sure Where to Start?
Talk with our team about how organizations are using VectorAI DB to build AI agents, reduce retrieval latency, keep sensitive data local, and deploy AI in environments where cloud databases don’t work. We can help you evaluate architectures, deployment models, and real-world AI use cases.
Book a Personalized Demo
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