European Data Sovereignty
AI models and vector data stay within European borders. GDPR compliant with data centers across.
Vector Databases
Databases for AI and machine learning applications.
Store, index, and query high-dimensional vectors efficiently for AI applications with managed vector databases. Semantic search, recommendation systems, RAG workflows, and machine learning pipelines - all with European data sovereignty.
Built for AI in Europe
Pay-as-You-Scale
Start small, scale seamlessly. Hourly billing, no upfront costs, no hidden fees.
Zero Vendor Lock-in
Built on open source (PostgreSQL + OpenSearch). Export your data anytime with standard tools.
Choose Your Solution
Two vector database solutions optimized for different AI workloads.
Explore More Exoscale Services
Expand your managed vector databases with services that boost availability, optimize performance, and provide expert support for all your workloads.
GPU Servers
Accelerate AI model training and inference with NVIDIA GPUs. Perfect for embedding generation, model fine-tuning, and large-scale ML pipelines.
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Scalable Kubernetes Service
Deploy and orchestrate containerized AI applications with managed Kubernetes. Seamlessly integrate with vector databases for production workloads.
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Support Plans
Get expert guidance for your AI infrastructure with flexible support plans designed for production workloads and mission-critical applications.
DiscoverTransparent Pricing
Start small and scale your Vector database to production workloads with predictable hourly billing. No upfront costs, no hidden fees. Vector capabilities included at no extra cost—pay only for your database instance.
European Cloud for AI
Keep your AI models and embeddings within European borders. All zones are GDPR compliant with data centers across Switzerland, Germany, Austria, Bulgaria, and Croatia. Full control over your data location.
Compare our managed vector databases
Both Vector database solutions are fully managed, GDPR-compliant, and deployed across European zones.
| Feature | pgvector | Vector search |
|---|---|---|
| Base Technology | PostgreSQL + pgvector | OpenSearch + Vector search |
| Best For | Hybrid workloads (SQL + vectors) | Pure vector search & analytics |
| Database Features | ACID, transactions, joins | Dashboards, alerting, analytics |
| Indexing | HNSW, IVFFlat | HNSW, distributed |
| Scaling | Vertical | Horizontal, multi-node |
Managed vector database features
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Enterprise Security
Benefit from TLS encryption, AES-256 at rest, and network isolation. SOC 2 audited with ISO 27018 privacy certification.
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High Availability
Deploy managed vector databases across European zones. Automatic failover and disaster recovery for mission-critical applications.
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Complete Integration
Native integration with Exoscale VPC, Load Balancer, Object Storage, and Terraform.
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24/7 Monitoring
Proactive monitoring by database specialists. Automated alerts, performance optimization, and issue resolution.
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Dedicated Resources
Guaranteed performance with dedicated compute and storage. No noisy neighbors affecting your vector search performance.
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Database Forking
Test changes and AI model updates safely with database forking. Clone production data without impacting live applications.
Managed vector databases trusted by teams across Europe.
Our customer success engineers have helped hundreds of customers from all over Europe migrate, run, and scale managed vector databases on Exoscale.
Contact usFrequently asked questions about Managed Vector Databases
What is a managed vector database?
A managed vector database is a fully hosted service that stores, indexes, and retrieves vector embeddings used for AI applications such as semantic search, recommendation systems, and LLM-based workflows. The provider handles maintenance, scaling, monitoring, and infrastructure so teams can focus on the application.
How scalable is a managed vector database?
Managed services are designed to scale automatically with the workload. They can handle millions to billions of vectors and adjust compute resources based on query volume and data size. This ensures low-latency semantic search and high availability even as applications grow.
Why should teams use a managed vector database service?
A managed service eliminates operational overhead such as server management, scaling, version updates, performance tuning, backups, or security. This allows faster development cycles and ensures reliable, high-performance vector search without specialized in-house expertise.
Which managed vector database products does Exoscale offer?
We offer two managed vector database options teams can choose from: Vector search based on OpenSearch and Managed pgvector, a PostgreSQL extension that adds vector search capabilities to a familiar relational database environment.