Once you’ve decided a project needs semantic search or retrieval, the next question is which vector database to actually run, and what server it needs to run well. This is a narrower, more practical question than the general infrastructure principles covered in our broader guide to vector databases and hosting requirements for AI search applications — here, the focus is comparing specific options and translating that choice into concrete vector database server specifications.
Vector database hosting decisions genuinely differ by which database you pick — Qdrant, Milvus, and pgvector have different memory profiles, scaling models, and operational complexity, and the “right” one depends on your existing stack and query patterns as much as raw performance.
Qdrant: Purpose-Built and Operationally Simple
Qdrant is a dedicated vector search engine written in Rust, designed specifically around vector similarity search with metadata filtering as a first-class feature rather than an afterthought.
- Memory profile — Qdrant’s HNSW-based indexing performs best with the index held in memory; sizing RAM to comfortably fit your vector count times dimensionality (plus index overhead) avoids the latency penalty of falling back to disk mid-query.
- Filtering performance — Qdrant’s payload filtering (combining metadata conditions with vector search) is built into its core indexing structure rather than bolted on, which tends to hold up well even with complex filter conditions applied alongside similarity search.
- Operational simplicity — a single-purpose database is generally easier to reason about, monitor, and tune than a general-purpose database extended to do vector search, at the cost of needing to run and maintain a separate system alongside your primary datastore.
- Scaling model — Qdrant supports sharding and replication for horizontal scaling, relevant once a single node’s memory and query throughput ceiling is reached.
Milvus: Built for Very Large Scale
Milvus takes a distributed-systems-first approach, with separate, independently scalable components for data ingestion, indexing, and query serving — a more complex architecture that pays off specifically at very large vector counts.
- Disaggregated architecture — storage, compute, and coordination are separate services, which allows scaling each independently but introduces meaningfully more operational complexity than a single-binary deployment.
- Index type flexibility — Milvus supports multiple index types (including IVF and HNSW variants) with different memory-versus-speed tradeoffs, giving more tuning surface for teams with very specific latency or memory constraints.
- Better suited to billion-scale vector counts — the added architectural complexity is generally justified once vector counts grow large enough that a single-node deployment genuinely can’t hold the index in memory, rather than for small-to-mid-size collections where it adds overhead without a corresponding benefit.
- Kubernetes-native deployment — Milvus’s distributed components are commonly deployed via Kubernetes, which pairs with the orchestration considerations covered in our comparison of Kubernetes vs Docker Swarm for dedicated servers.
pgvector: Vector Search Inside Postgres
pgvector adds vector similarity search as an extension to PostgreSQL, rather than requiring a separate database system altogether.
- No separate system to operate — if your application already runs PostgreSQL, adding vector search via an extension avoids introducing an entirely new database to deploy, monitor, and back up, which is a genuine operational simplification.
- Transactional consistency with your other data — vector data lives alongside relational data in the same database, meaning updates to both can happen within the same transaction, something a separate vector database can’t offer without extra coordination.
- Index performance at scale — pgvector’s HNSW and IVFFlat index types perform well for many production workloads, but very large vector counts or extremely high query-per-second requirements are where dedicated vector databases like Qdrant or Milvus tend to pull ahead, since they’re purpose-built for exactly this workload rather than extending a general-purpose database.
- Same PostgreSQL infrastructure requirements — since pgvector runs inside Postgres, the tuning and high-availability considerations covered in our PostgreSQL HA clusters guide apply directly to a pgvector deployment as well.
Which One Actually Fits Your Situation
| Factor | Qdrant | Milvus | pgvector |
|---|---|---|---|
| Best fit | Dedicated vector search with rich filtering, small-to-mid scale | Very large vector counts (hundreds of millions+) | Teams already running Postgres, moderate scale |
| Operational complexity | Moderate — single dedicated system | High — distributed, multi-component | Low — extends existing Postgres |
| Filtering performance | Strong, built-in | Good, index-dependent | Good, via standard SQL WHERE clauses |
| Scaling ceiling | High, via sharding | Very high, purpose-built for it | Moderate, bound by single-node Postgres limits |
| Transactional consistency with other data | No — separate system | No — separate system | Yes — same database |
Server Sizing Principles That Apply Across All Three
Regardless of which database you choose, the underlying server sizing logic follows the same principles:
- RAM sized to the full index, not just the raw vector data — index structures (HNSW graphs, IVF cluster metadata) add meaningful overhead beyond the raw vector storage size; sizing memory only for raw vectors routinely leads to underprovisioned deployments.
- NVMe storage for persistence and cold starts — even memory-resident indexes need to persist to disk, and fast storage meaningfully reduces both write latency and restart/rebuild time, a point covered in depth in our guide to why NVMe storage is essential for modern AI and database workloads.
- CPU for index building and query scoring — building or rebuilding an index is CPU-intensive, and query-time similarity scoring, especially with complex filters, benefits from dedicated, unshared CPU cores rather than shared or oversold hosting.
- Dedicated infrastructure avoids noisy-neighbor query latency spikes — the same reasoning covered in our bare metal servers vs cloud VMs comparison applies directly to vector search: unpredictable latency from shared tenancy undermines what’s usually a latency-sensitive part of an application’s critical path.
Where This Fits in a Larger AI Search or RAG Stack
Vector database hosting rarely stands alone — it’s typically one stage in a larger pipeline involving embedding generation and, often, an LLM producing a final response. See our guide to RAG infrastructure for production retrieval-augmented generation apps for how the vector database choice covered here fits into that broader pipeline, including why colocating the vector database with the embedding and generation layers matters for end-to-end latency.
How BeStarHost Supports Vector Database Hosting
Whichever vector database fits your workload, the underlying server needs to deliver genuine, unshared memory and I/O performance:
- Dedicated servers with guaranteed, unshared CPU and RAM — index performance and query latency are predictable, not subject to contention from other tenants.
- NVMe storage across server tiers, keeping index persistence and rebuild times fast regardless of which vector database you run.
- Dedicated, unshared bandwidth on a global low-latency network, relevant when the vector database is queried from a separate application or embedding layer.
- 99.9% uptime on Tier 3 / Tier 4 hardware with RAID 0 / RAID 1 configurations.
- IPMI KVM-over-IP for direct remote access when configuring sharding, replication, or index rebuilds.
- 14 global data center locations across Europe (France, Germany, Netherlands, United Kingdom), Asia (Singapore, Hong Kong, India, South Korea, Taiwan, Philippines, Myanmar, Cambodia), and North America (United States, Canada) — letting you place your vector database close to the applications querying it.
- No setup fees and 24/7/365 support if you need help sizing a server for Qdrant, Milvus, or pgvector.
Explore our dedicated server plans, read more on our About Us page, or contact our team to scope infrastructure for your vector database.
Frequently Asked Questions
Should I use pgvector or a dedicated vector database like Qdrant or Milvus?
If your application already runs PostgreSQL and your vector search needs are moderate in scale, pgvector avoids the operational overhead of running a separate database system. For very large vector counts or the highest query throughput requirements, dedicated vector databases purpose-built for the workload tend to perform better.
When does Milvus make more sense than Qdrant?
Milvus’s distributed, disaggregated architecture pays off specifically at very large scale — hundreds of millions of vectors or more — where its ability to scale ingestion, indexing, and querying independently justifies the added operational complexity. For small-to-mid-size collections, Qdrant’s simpler single-system model is often a better fit.
How much RAM does a vector database need?
Sizing should account for the full index structure, not just raw vector storage — index overhead from structures like HNSW graphs can add significant memory beyond the base vector data. Underestimating this overhead is a common cause of underprovisioned vector database deployments.
Does pgvector support the same features as dedicated vector databases?
pgvector supports HNSW and IVFFlat indexing and integrates vector search with standard SQL filtering, covering most production use cases well. Dedicated vector databases generally offer more specialized indexing options and tend to scale further at very high vector counts or query throughput.
Why does vector database hosting need dedicated infrastructure rather than shared hosting?
Vector search is typically a latency-sensitive part of an application’s critical path, and shared or oversold hosting introduces unpredictable latency from other tenants’ workloads contending for the same CPU and memory. Dedicated infrastructure keeps query performance consistent and predictable.
Choosing infrastructure for a production vector database deployment? Talk to BeStarHost about dedicated servers built for vector database hosting →