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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchA private endpoint can keep traffic to a managed vector database off the public internet, but it does not move that service into your data center or put every part of your AI workflow under your control. To decide whether vectors are truly “inside the boundary,” map where the source data, embeddings, index, queries, prompts, model calls, logs, backups, and administrative access go—and define which boundary you mean.
What does “inside the boundary” mean for a vector store?
There is no single boundary that fits every organization. It might mean an organization-owned data center, a customer-controlled virtual private cloud (VPC), a provider’s cloud region, or a perimeter defined by a security or regulatory program. State the boundary before evaluating a deployment; otherwise, “private” and “local” can describe very different arrangements.
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A vector store keeps embeddings—numerical representations used to find semantically similar content—and an index for searching them. It may also hold metadata or references to source documents. But the index is only one part of a retrieval-augmented AI workflow. Embeddings may be generated elsewhere, a query may be sent to a model, and retrieved passages may be passed to an inference service. Each transfer can cross a boundary even if the index does not.
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Does a private endpoint mean the service is on-premises?
No. A private endpoint describes how a client reaches a service, not necessarily where that service runs or who operates it. A managed service can have a private network path while remaining in a provider-operated cloud environment. That may meet a requirement for private connectivity without meeting a requirement that storage or inference run on premises.
For example, Amazon Web Services documents VPC interface endpoints using PrivateLink for S3 Vectors, as well as on-premises connectivity through Direct Connect or VPN. Google Cloud documents Private Service Connect for consuming managed Vector Search endpoints through internal VPC IP addresses. These are private-consumption patterns for managed services, not evidence that the services are deployed in a customer data center or operate without cloud connectivity.
A private endpoint can reduce exposure to the public internet. It does not, by itself, establish where every copy of data resides, what provider operations can access it, or whether the arrangement satisfies a regulatory requirement. AWS’s documentation says private connectivity can support requirements that mandate it; that is narrower than a guarantee of overall compliance.
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Which parts of the AI data path should you map?
Trace one realistic request from source ingestion through retrieval and response, including routine operations. For each step, record the system, region or facility, operator, network path, retention behavior, and access controls. This checklist is an architectural way to define the boundary, not a vendor-certified standard.
| Data or operation | Questions to answer |
|---|---|
| Source documents | Where are originals and extracted text stored? Are they copied to a separate ingestion or preprocessing service? |
| Embeddings and index | Where are embeddings generated, stored, indexed, replicated, and backed up? Does the index contain metadata or document content that needs the same protection as the source? |
| Queries and prompts | Where do user queries, prompts, and conversation history travel? Are they retained by the vector service, model service, application, or logging system? |
| Retrieved passages and inference | Where are retrieved passages sent for generation? Does the language model run in the same boundary as the index? |
| Logs and telemetry | Could logs capture query text, document identifiers, prompts, or retrieved content? Who can access them, and how long are they retained? |
| Backups, deletion, and recovery | Where do backup copies and snapshots reside? How does deletion propagate to indexes, replicas, logs, and backups, and what recovery process applies? |
| Administration and support | Who can change policies, access data, or troubleshoot the service? How are privileged access, support access, and audit records governed? |
Do not treat an unanswered location or access question as proof that data stays inside. Resolve it through the service configuration, contract, and operational arrangements that apply to your deployment.
What are the main deployment choices?
These patterns trade network isolation and operational control against the work of running more of the stack yourself. The table describes what the cited providers document; it is not a benchmark or a claim that one option is inherently more secure.
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| Pattern | What it provides | What it does not establish |
|---|---|---|
| Managed vector storage with private connectivity | AWS documents PrivateLink VPC interface endpoints for S3 Vectors and on-premises connections through Direct Connect or VPN. | The private route does not make the managed storage service on-premises or customer-operated. |
| Managed Vector Search with private service connection | Google Cloud documents Private Service Connect and internal VPC IP addresses for Vector Search endpoints. | This is private access to a managed service, not an air-gapped or local deployment. |
| Data-center or air-gapped AI services | Oracle describes its Private AI Services Container as designed for data-center deployment without public-cloud or internet dependency, with local embedding and inference services and the ability to offload vector-index work. | Those properties describe Oracle’s product, not every container or self-hosted AI system. Oracle’s product page calls it “Your AI data never leaves your realm”; treat that as vendor product-page language, not an independently audited or universal guarantee. |
| Postgres-centered vector and relational data | EnterpriseDB’s white paper describes EDB PG AI with pgvector across on-premises, cloud, and hybrid configurations. | The description is vendor-authored; confirm current product behavior and the controls in the specific configuration. |
| Retrieval platform with private deployment options | Vectara’s platform documentation describes tenant isolation and retrieval-time role filtering, and points to VPC, on-premises, and air-gapped deployment options. | Vendor documentation does not prove a particular deployment is configured correctly; verify the applicable controls and contract terms. |
How should access and governance work at retrieval time?
Protecting an index at rest is not enough if retrieval can return records a user is not authorized to see. Authorization needs to apply to the actual search request and its results, not just to the application’s ability to connect to the database.
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- Connect the vector service’s identity model to the organization’s identity and authorization process; define how role changes and revocations reach retrieval policies.
- Document key custody, audit logging, retention, and deletion behavior across the vector service and adjacent model, application, and observability systems.
- Review encryption, compliance mappings, and support-access terms for the actual region, edition, configuration, and contract. Vendor descriptions are not independent audits of your deployment.
Vectara documents tenant isolation and retrieval-time role filtering. EnterpriseDB’s white paper describes database controls for its platform. Those vendor statements are useful starting points, not evidence that a particular system has been configured or tested to enforce your policies.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you choose between managed, hybrid, and local operation?
Start with the requirement that drives the decision, then choose the least complex architecture that demonstrably meets it. A private network path may be sufficient when the requirement concerns connectivity; a location or operational-control requirement may call for a different deployment.
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- Define the perimeter. Specify whether “inside” means your VPC, a named cloud region, an owned facility, or a disconnected environment. Identify which data classes and operations must remain there.
- Map the full request path. Use the data-path checklist above to identify every service that receives source content, embeddings, queries, prompts, retrieved passages, or logs.
- Match the deployment to the requirement. Consider a managed service with private connectivity when private routing is the need and provider operation is acceptable. Consider local or air-gapped services when cloud or internet dependency is not acceptable and the organization can operate the required stack.
- Validate controls and evidence. Confirm identity enforcement, auditability, key management, retention and deletion, administrative access, support arrangements, and recovery behavior for the actual deployment.
- Assign operational ownership. Decide who handles capacity, availability, patching, model and index updates, disaster recovery, and incidents. Record dependencies and test recovery rather than assuming a deployment mode supplies it automatically.
The trade-off is architectural, not a measured cost or performance ranking: private connectivity can preserve a managed cloud service, while local or air-gapped operation places more infrastructure and lifecycle responsibility on the operator. Also consider portability. A separate vector service can add another policy and operational surface; keeping relational and vector data in a Postgres-centered design may reduce separation between those data paths, but does not remove the need to verify where the database and model operations run.
What do provider figures tell you—and what do they not tell you?
Provider-specific figures can clarify a product condition, but they cannot rank systems across providers or substitute for an architecture review.
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- AWS states that S3 storage underpinning S3 Vectors is designed for 99.999999999% (11 nines) durability. The year is not stated on the opened security page; this is an AWS-published design durability statement, not an independently measured comparison.
- Google Cloud says a deployed Vector Search index with fewer than two replicas per shard is excluded from the service-level agreement described on its documentation page. The year is not stated there. This is a condition of that cited SLA, not a general recommendation for all vector systems.
- Oracle’s Private AI Services Container page, dated March 24, 2026, says six popular vector embedding models ship with the container and that customers may download additional models. The included-model count describes that product page, not model quality or comparative capability.
These figures answer different questions—durability design, an SLA condition, and included models. None establishes that a service is inside your boundary, correctly governed, or preferable to another option.
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