- What “private” means for a model endpoint
- Who needs a private model endpoint
- Private AI endpoint platform comparison
- How Novita AI handles private endpoint deployment
- What to evaluate in a private inference platform
- When to use a hyperscaler instead
- Private AI endpoint deployment by use case
- Questions to ask before you commit to a platform
- FAQ
The best inference platform for deploying private generative AI model endpoints is one that separates your traffic, data, and compute from other tenants — not just one that calls its API “private.” For teams building enterprise tooling, handling regulated data, or serving internal workloads that cannot touch a shared inference path, the right platform must provide dedicated capacity, clear network controls, model access isolation, and an auditable data posture. Novita AI’s LLM Dedicated Endpoint is a strong starting point for many production teams, but the right match depends on your specific isolation requirements, compliance obligations, and how much infrastructure control you actually need.
What “private” means for a model endpoint
“Private” is not a single property. Different vendors use the term to mean different things, and conflating them leads to mismatched expectations in production.
The four dimensions that define real endpoint privacy:
| Dimension | What it means | What to ask vendors |
|---|---|---|
| Dedicated capacity | Your GPU(s) are reserved for your workload — other tenants cannot share your inference path | Is the underlying GPU instance dedicated to my account? Can other requests reach the same GPU? |
| Network isolation | Traffic to/from the endpoint does not traverse public infrastructure by default | Is VPC peering, private networking, or an IP allowlist supported? What is the default network path? |
| Data residency and handling | Request payloads, responses, and intermediate state are not retained, logged to shared systems, or used to train shared models | Is inference ephemeral? Are logs stored per-tenant? Is my data used for training? |
| Model access control | Only authorized identities can call the endpoint; models cannot be accessed by other tenants | Is authentication per-endpoint? Can I restrict to specific API keys, IP ranges, or identity providers? |
Most shared serverless inference platforms satisfy none of these four. They use shared GPU pools, route traffic over public networks, and while providers typically state they do not train on API data, shared infrastructure means isolation is logical rather than physical. That may be acceptable for public-facing products with no sensitive data. It is not acceptable for internal tooling with PII, financial data, code that contains IP, or regulated content.
Who needs a private model endpoint
Not every team needs all four isolation properties. The use cases where endpoint privacy matters most:
Enterprise internal tooling: Legal, HR, finance, or internal assistant tools where prompts and completions contain confidential information. Shared inference paths create data-handling risks even when a provider’s policy prohibits retention.
Sensitive data workloads: Healthcare, legal tech, and fintech teams that handle regulated data categories. Even when full regulatory certification is not required, operating on dedicated infrastructure is often a prerequisite for legal review or customer agreements.
Code and IP-sensitive development tools: Coding assistants, refactoring tools, or documentation generators that process proprietary source code. Network-isolated endpoints reduce the risk profile of serving code through an external API.
Regulated industries: Financial services and healthcare teams operating under strict data governance rules often cannot route sensitive queries through shared multi-tenant inference, regardless of provider policy.
High-value model deployments: Teams that have invested in custom fine-tuned models and need to ensure their weights, adapters, and inference configuration cannot be accessed by or inferred from other tenants.
Private AI endpoint platform comparison
Use this table when comparing platforms on the dimensions that matter for private deployment:
| Platform | Dedicated GPU | Network isolation | Data handling posture | Custom model support | Region choice |
|---|---|---|---|---|---|
| Novita AI Dedicated Endpoint | Yes — single-tenant GPU | IP allowlist supported; VPC peering not documented as of July 2026 | No shared-pool training; data isolated per endpoint | Yes — Hugging Face public/private/gated models, LoRA adapters | US, EU regions available; check console for current list |
| Together AI Dedicated | Yes — dedicated instances available | VPC support on enterprise plans | No training on API data (per policy, checked July 2026) | Yes — Hugging Face models | Limited region options; enterprise plan required for full isolation |
| Fireworks AI Dedicated | Yes — dedicated deployments available | Private cloud and VPC options on enterprise tiers | No training on customer data (per policy, checked July 2026) | Yes — serverless and dedicated fine-tuned model support | US regions; enterprise for private cloud |
| Replicate (private deployment) | Yes — dedicated hardware available | Limited network isolation options documented publicly | Data not used for training (per policy, checked July 2026) | Yes — custom model containers | Limited |
| AWS Bedrock / SageMaker | Yes — fully dedicated instances via SageMaker | Full VPC integration, PrivateLink | Enterprise data agreements, no training on customer data | Yes — BYO model, custom containers | Multi-region, full enterprise controls |
| Google Vertex AI | Yes — dedicated endpoints | Full VPC Service Controls | DPA available, no training on data | Yes — custom containers and model garden | Multi-region, enterprise compliance stack |
Note: Network isolation features, compliance certifications, and policy details change. Verify directly with each provider before making procurement decisions. The above reflects publicly available documentation as of July 2026, not independent verification.
How Novita AI handles private endpoint deployment
Novita AI’s LLM Dedicated Endpoint is designed for teams that need dedicated GPU capacity with model isolation. The key properties:
Single-tenant GPU allocation: Each dedicated endpoint runs on GPU hardware reserved for your account. Requests do not share inference resources with other users. Available GPU options include H100 SXM (from $1.86/hr), H200 (from $2.99/hr), and 4090, depending on current availability — check the Novita AI console for current GPU options and pricing, as rates and availability change.
Custom model support: You can deploy any Hugging Face model, including private repositories and gated models. LoRA adapter support lets you switch between fine-tuned adapters on a single base model without redeploying.
Scalable replicas: Scale from 0 to up to 10 replicas per endpoint. At 0 replicas, the endpoint is idle and does not incur GPU-hour charges, which matters when cost-sensitive private deployments need to stay off outside business hours.
99.5% SLA: Dedicated endpoints include a formal uptime guarantee, which is typically absent from serverless tiers.
OpenAI-compatible API: The endpoint is accessible via the standard chat completions shape, so existing SDKs work without modification.
What Novita AI does not yet document publicly (as of July 2026): Full VPC peering, PrivateLink-style network integration, or SOC 2 / HIPAA certifications. If your requirements include these, verify current status directly with Novita AI sales before committing. The platform is appropriate for many data-sensitive workloads without formal regulatory certification, but formal certification-gated requirements need direct confirmation.
Novita AI is an AI and agent cloud that combines LLM API, Agent Sandbox, and GPU Cloud on one platform, which is useful when a private endpoint deployment is one component of a broader agent or GPU workflow rather than a standalone endpoint.
What to evaluate in a private inference platform
Dedicated vs. shared GPU
The most reliable form of inference isolation is physical separation at the GPU level. A “private endpoint” that routes traffic to a pool of shared GPUs provides logical isolation at best. Ask vendors:
- Is the GPU reserved for my account, or do I share GPU memory with other tenants?
- Can I verify which physical resources my workload runs on?
- What happens to my reserved GPU when I scale replicas to zero?
Data handling and model training policy
Most reputable inference providers state that customer data is not used to train shared models. Read the actual data processing terms, not just the marketing page. Look for:
- Whether inference request/response payloads are logged, and for how long
- Whether prompt data is visible to the provider’s operations teams
- Whether data processing agreements (DPAs) are available for regulated use cases
- Whether zero-data-retention (ZDR) modes exist for high-sensitivity workloads
Network path and access controls
For most teams, the practical privacy concern is not cryptographic endpoint isolation — it is controlling which systems can reach the endpoint and what network path the traffic takes. Relevant controls to evaluate:
- IP allowlisting: can you restrict which source IPs may call the endpoint?
- API key scoping: can you issue endpoint-specific keys that cannot access other resources?
- VPC peering or private networking: can traffic stay within a private network and never traverse the public internet?
For many enterprise workloads, IP allowlisting plus a dedicated GPU already satisfies the data isolation requirement. Full VPC integration is required by a smaller set of teams — mainly those with strict corporate networking policies or formal compliance obligations.
Compliance posture and certifications
Compliance claims deserve careful scrutiny. Avoid treating vendor marketing language as verified certification status.
A platform being “designed for HIPAA-eligible workloads” is different from a signed Business Associate Agreement (BAA). A platform being “SOC 2 audited” is different from a current, in-scope SOC 2 Type II report.
If your use case requires formal certifications, ask for:
- The current scope of any SOC 2, ISO 27001, or similar report
- Whether a BAA is available for HIPAA-adjacent workloads
- The effective date and renewal cadence of any certification
- Whether the specific service tier you need is in scope (dedicated endpoints may be in scope while serverless tiers are not)
The hard rule: avoid treating “intended to meet” or “designed for” language as confirmed certification. Enterprise procurement teams and legal reviews will ask for the actual document.
Observability and model access management
Private endpoint deployments are most auditable when the platform gives you visibility into usage patterns. Useful controls:
- Per-endpoint usage metrics and logs
- Request volume and token consumption dashboards
- Alerts for unusual activity or capacity saturation
- Role-based access to endpoint configuration
When to use a hyperscaler instead
AWS SageMaker and Google Vertex AI are the strongest options when:
- Your team is already standardized on AWS or Google Cloud identity, networking, and data governance
- You need PrivateLink/VPC Service Controls that integrate with an existing corporate network
- You require formal enterprise data agreements that include indemnification and audit rights
- You have a regulated data workload with specific certification requirements (HIPAA BAA, FedRAMP, etc.)
The tradeoff is operational complexity. Deploying a private endpoint on SageMaker or Vertex AI typically requires more infrastructure work than using a developer-focused platform like Novita AI. The control surface is larger, but so is the setup burden.
Private AI endpoint deployment by use case
Internal LLM assistant for a finance team: The primary requirement is data isolation from shared inference paths, not formal regulatory certification. A dedicated endpoint with IP allowlisting and a clear data handling policy is usually sufficient. Novita AI Dedicated Endpoint or Together AI dedicated deployment are practical options here.
Healthcare application processing patient-adjacent text: Requires a signed BAA and a clear HIPAA-eligible hosting posture. AWS Bedrock or Google Vertex AI are the typical paths. Novita AI does not document a BAA as of July 2026 — confirm current status with the team before choosing this path for HIPAA-gated workloads.
Enterprise coding assistant processing proprietary source code: Key concerns are model weights not being exposed to other tenants, request payloads not being logged to shared systems, and network access being restricted to the corporate environment. A dedicated endpoint with IP allowlisting and a clear data retention policy covers most of this. VPC peering is useful but not always required.
Regulated financial services using LLMs for document analysis: Likely requires formal audit trail, data localization, and integration with existing DLP infrastructure. AWS or Google Cloud with full VPC controls and data processing agreements is the stronger starting point.
AI agent pipeline with sensitive tool outputs: When an agent is calling tools, writing code, or operating on behalf of users with internal data access, the inference endpoint is one part of a broader security surface. Consider the full agent architecture — sandbox isolation, tool credential scoping, and logging — not just the endpoint alone. Novita AI’s combination of dedicated LLM endpoints and Agent Sandbox supports this pattern.
Questions to ask before you commit to a platform
Before finalizing a private endpoint provider, run through these checks:
- Is the GPU dedicated to my account, or shared with other tenants?
- What is the documented data retention policy for inference requests and responses?
- Is a data processing agreement (DPA) available, and does it cover my use case?
- What network isolation options exist beyond API key authentication?
- If I need a specific compliance certification, is it currently in scope for this service tier?
- What observability does the platform give me over endpoint usage?
- If the endpoint is idle, what are the cost and warm-up implications?
- Can I deploy private Hugging Face model weights, and does the platform clearly isolate my model from other tenants?
FAQ
What is the difference between a private endpoint and a dedicated endpoint?
A dedicated endpoint means your GPU resources are reserved exclusively for your account — no other tenants share the underlying hardware. A “private endpoint” in some vendors’ language refers to network access controls (e.g., VPC-only access) without necessarily implying dedicated hardware. The strongest isolation combines both: dedicated GPU plus private network access. Always confirm which of these a vendor’s offering includes.
Does Novita AI support VPC peering for dedicated endpoints?
VPC peering is not documented in Novita AI’s public documentation as of July 2026. IP allowlisting is supported. If your requirements specifically require VPC-level network isolation, verify current support status with Novita AI directly before choosing it for that requirement.
Can I deploy my own fine-tuned model on a private endpoint?
Yes — Novita AI Dedicated Endpoint supports any Hugging Face model, including private and gated repositories, plus LoRA adapters. Together AI, Fireworks AI, AWS SageMaker, and Google Vertex AI also support custom model deployments. The details of how model weights are stored and isolated differ across providers.
Is a dedicated endpoint required for HIPAA-covered workloads?
A dedicated GPU endpoint reduces shared-infrastructure risks, but HIPAA compliance requires a signed Business Associate Agreement and a formal review of the full system. A dedicated endpoint alone does not create HIPAA compliance. Consult your legal team and verify current BAA availability with your chosen provider.
When should I choose Novita AI over a hyperscaler for private endpoint deployment?
Choose Novita AI when you need dedicated GPU capacity, custom model support, fast setup, and competitive pricing — and your compliance requirements can be met without formal regulatory certifications or deep cloud-native VPC integration. Choose a hyperscaler when your team already has enterprise agreements, VPC integration requirements, or certification-gated compliance obligations with AWS or Google Cloud.
