Data Compute & Vector Stores
High-memory infrastructure for vector search engines (pgvector, Qdrant, Milvus) and analytics data pipelines.
What this is
Compute and memory-optimized server configurations designed to host vector databases, in-memory caches, and batch data processing pipelines where RAM and disk I/O are the primary performance factors.
How it works
- 1High-memory compute instances provisioned with enterprise NVMe block storage.
- 2Vector database engines configured with optimized index caching parameters.
- 3Batch processing workers scheduled to run data transformations and embedding updates.
- 4Encrypted internal networking connects data stores to application nodes.
What's included
| Engines Supported | pgvector, Qdrant, Milvus, Redis, ClickHouse |
| Memory Tiers | 32 GB to 512 GB high-speed ECC RAM |
| Storage | Direct-attached NVMe storage for fast HNSW index traversal |
| Backups | Automated snapshot protection for vector indexes |
Suitability assessment
✓ When this makes sense
- •Retrieval-Augmented Generation (RAG) applications querying millions of vector embeddings.
- •Analytical reporting engines requiring fast in-memory aggregations.
✕ When it doesn't
- •Small static websites with simple relational query requirements.
Technical details & architecture deep-dive▾
Kernel page cache tuning and HugePages memory support to ensure large vector indexes remain pinned in physical memory during nearest-neighbor similarity searches.
Frequently asked questions
Yes. We support and configure the pgvector extension on our managed PostgreSQL hosting instances.