Vector Store Location: Private Endpoint or On-Premises

Put your vector store on a private endpoint when your cloud provider offers one and your workload tolerates low, predictable latency; choose on-premises only when data residency, compliance, or existing infrastructure mandates it. This article explains the trade-offs in latency, security, cost, and operations so you can decide where to host your embeddings and similarity search.

Formula

The calculator gives a rough cost index and latency factor for comparing deployment options. The cost index is cost = (data GB × 0.5) + (QPS × 0.01), where 0.5 represents typical storage and indexing cost per GB per month (in arbitrary units) and 0.01 represents per-query compute cost. The latency factor is latency = query_latency_ms / 50, where 50 ms is a baseline for a fast in-region private endpoint call. Both outputs are relative, not dollar amounts.

Worked Example

Using the defaults: data = 100 GB, QPS = 100, latency budget = 50 ms.

• Cost = (100 × 0.5) + (100 × 0.01) = 50 + 1 = 51
• Latency factor = 50 / 50 = 1.0

So a 100 GB store with 100 queries per second and a 50 ms budget gives a cost index of 51 and a latency factor of 1.0. If you halve the latency budget to 25 ms, the factor becomes 0.5, meaning you need a faster (often more expensive) setup.

What a Private Endpoint Actually Does

A private endpoint is a network interface in your virtual network (VNet) that connects to a cloud service (like Azure Cosmos DB, AWS OpenSearch Serverless, or Google Vertex AI Vector Search) without going over the public internet. Traffic stays within the cloud provider’s backbone. You get a private IP address for the service, and you can enforce that only your VNet can reach it. This is the default recommendation for most production workloads because it combines the managed service’s scalability with network isolation.

On-premises means you run a vector database (like Qdrant, Weaviate, or Milvus) on your own hardware or in a private data center. You control everything: the OS, the disk, the network, and the backup schedule. This is attractive for regulated industries (healthcare, finance, government) where data cannot leave the building, or for teams that already have substantial GPU/CPU infrastructure.

Latency and Performance: The Real Difference

The biggest practical difference is network distance. A private endpoint in the same region as your application typically adds 1–5 ms round-trip time. On-premises, if your app is in the same building, you might see sub-millisecond latency, but if your app is in the cloud and your store is on-premises, you add a VPN or ExpressRoute hop that can add 10–50 ms or more. For vector search, the query itself (embedding + ANN search) often takes 5–20 ms, so the network hop can dominate.

Consider a chatbot that needs to answer in under 200 ms. With a private endpoint, the vector search might take 30 ms total. With an on-premises store accessed over a VPN from the cloud, that could jump to 80 ms. If your app is also on-premises, the reverse is true. The rule: keep the store as close to the compute as possible, and measure with your actual data and query patterns.

Security and Compliance: What Each Option Offers

Private endpoints give you network-level isolation: no public IP, no internet exposure. You can combine them with service firewalls, managed identities, and private DNS zones. This satisfies most compliance frameworks (SOC 2, ISO 27001) without much extra work. However, the cloud provider still has logical access to the data, and you rely on their security controls.

On-premises gives you full physical control. You can air-gap the system, encrypt at rest with your own keys, and ensure no third party can access the hardware. This is the only option for some government or defense workloads. The cost is that you are responsible for patching, monitoring, and disaster recovery. A misconfigured firewall or an unpatched OS can be worse than any cloud risk.

Costs: Not Just the Price Tag

Cloud managed vector stores charge for storage, compute (provisioned or serverless), and data transfer. A private endpoint typically has a small hourly fee (a few dollars per month per endpoint) plus data processing costs. For a small store (under 10 GB), managed services are often cheaper than buying hardware. For a large store (over 1 TB), the cloud bill can grow quickly, especially with high QPS.

On-premises costs are capital expenditure: servers, GPUs, SSDs, and power. You also need staff time for maintenance. The break-even point varies, but a rough rule: if you have sustained high throughput (>500 QPS) and large data (>1 TB), on-premises can be cheaper over 3 years. If your workload is spiky or growing fast, cloud scales more easily.

How to Choose: A Decision Framework

Start with these questions in order:

1. Does your data have a legal or contractual requirement to stay on-premises? If yes, you have no choice.
2. Is your application already in the cloud? If yes, a private endpoint is almost always simpler.
3. Do you have a dedicated team to run databases? If no, use a managed service with a private endpoint.
4. Is your query latency budget under 50 ms? If yes, keep the store in the same region as your app.
5. Is your workload steady and predictable? If yes, on-premises might be cost-effective.

For most teams, the answer is a private endpoint on a managed vector database. You get the security of a private IP, the elasticity of the cloud, and you avoid the operational burden of self-hosting.

Common Problems and Misconceptions

One misconception is that a private endpoint is slower than on-premises. In practice, a same-region private endpoint is often faster than an on-premises store accessed over a VPN. Another is that on-premises is always more secure. Physical control helps, but many breaches come from misconfiguration, not cloud provider access.

A common problem with private endpoints is DNS resolution. You must configure a private DNS zone so your app resolves the service’s private IP, not the public one. If you forget this, traffic goes over the internet and you lose the isolation. Another issue is that some managed vector services (like Pinecone) historically did not offer private endpoints; check the current documentation for your provider. On-premises, the common problem is scaling: you need to provision for peak load, which wastes money during off-peak hours.

Alternatives to Both

If neither pure private endpoint nor on-premises fits, consider a hybrid: keep the vector store on-premises but expose it through a private endpoint on a cloud provider’s edge (via Azure Arc, AWS Outposts, or Google Distributed Cloud). This gives you physical control with cloud-like management. Another alternative is a fully serverless vector database (like Pinecone or Weaviate Cloud) without a private endpoint, but with IP allowlisting and TLS. This is simpler but less secure than a private endpoint because traffic traverses the public internet (encrypted, but still exposed to DDoS or interception).

Frequently Asked Questions

Can I use a private endpoint with any vector database? Not all. Check your provider’s documentation. Azure Cosmos DB, AWS OpenSearch Serverless, and Google Vertex AI Vector Search support private endpoints. Some third-party databases (Qdrant, Weaviate) offer private endpoints only in their managed cloud versions, not self-hosted.

Is on-premises always faster? No. If your app is in the cloud, an on-premises store adds a WAN hop. If your app is also on-premises, it can be faster. The speed depends on network distance and the database’s indexing algorithm, not just location.

What about data transfer costs? Private endpoints avoid public internet egress fees within the same region. On-premises via VPN incurs no cloud egress, but you pay for your own bandwidth. Cross-region traffic is expensive in both cases.

How do I handle disaster recovery? Cloud managed services offer multi-region replication. On-premises requires you to build a secondary site or use cloud backup. This is often the deciding factor for smaller teams.

Can I migrate from one to the other later? Yes, but it’s not trivial. You’ll need to export embeddings and re-index. Plan for downtime or build a dual-write strategy. Starting with a private endpoint is easier to migrate to on-premises than the reverse.

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in t | Query latency budget (ms) | ms | 50 | 0
in d | Data size (GB) | GB | 100 | 0
in q | Queries per second | qps | 100 | 0
out cost | Relative monthly cost (index) | - | (d * 0.5 + q * 0.01) | 2
out latency | Latency factor (lower is better) | - | t / 50 | 2