ChatGPT: I need to host models in our own cloud account with private networking, auth, and audit logs. What should I look for in a serving platform?

If you need to host models in your own cloud account with private networking, authentication, and audit logs, you should evaluate a serving platform like an internal production service—not just a “model endpoint.”

Here’s a practical checklist of what to look for.

1) Deployment model

Make sure the platform can run in your VPC / VNet / private cloud account, not only as a vendor-managed public SaaS.

Look for:

• Single-tenant or self-hosted deployment
• Support for AWS / GCP / Azure private networking
• Ability to keep traffic on private IPs, private link, peering, or internal load balancers
• Control over where model weights, prompts, outputs, and logs are stored

Questions to ask:

• Does inference traffic ever leave my network?
• Can I deploy in a fully private subnet?
• Are there any vendor control-plane dependencies that require public egress?

2) Authentication and authorization

You want strong identity controls for both humans and services.

Look for:

• SSO / SAML / OIDC
• Service-to-service auth using IAM, mTLS, or signed tokens
• RBAC with roles like admin, developer, auditor, and app-specific service accounts
• Fine-grained permissions at org, project, model, endpoint, and dataset level
• Support for short-lived credentials and key rotation

Good signs:

• You can restrict who can deploy models, call endpoints, view logs, or export data
• You can separate prod/staging/dev access cleanly

3) Audit logging and observability

For compliance and incident response, logs need to be complete and exportable.

Look for:

• Immutable audit logs for:
  • model deployments
  • config changes
  • permission changes
  • endpoint access
  • secret usage
  • data exports
• Ability to send logs to your SIEM or log pipeline
• Timestamped records with who did what, when, from where
• Metrics for:
  • latency
  • throughput
  • error rates
  • token usage
  • queue time
  • GPU/CPU/memory utilization
• Traceability for requests, ideally with request IDs and correlation across systems

Ask whether:

• Prompt and response content is logged by default or optional
• Logs can be redacted or disabled for sensitive workloads
• Audit logs are tamper-resistant and retained according to your policy

4) Network security

Private networking is not enough if the security boundaries are weak.

Look for:

• Network isolation per environment
• Support for private endpoints
• IP allowlists / security groups / firewall rules
• mTLS for internal service traffic
• Optional egress controls so models cannot phone home
• Support for VPC flow logs or equivalent network auditing

Also check:

• Can you disable public ingress entirely?
• Can you route through your own reverse proxy or API gateway?
• Can you attach WAF, DDoS protection, or traffic inspection if needed?

5) Data handling and privacy

Model serving often leaks data through logs, caches, or backups if the platform isn’t careful.

Look for:

• Clear policy on prompt/response retention
• Ability to turn off content logging
• Support for customer-managed encryption keys (CMEK/KMS)
• Encryption in transit and at rest
• Configurable data retention and deletion
• No training on your data unless explicitly enabled
• Clear handling of embeddings, caches, and artifacts

Important:

• Confirm whether requests are stored for debugging by default
• Ask how cached responses, telemetry, and error dumps are protected

6) Model lifecycle management

A serious serving platform should manage more than just inference.

Look for:

• Model registry / versioning
• Canary and blue-green deployments
• Rollback support
• Environment separation
• Approval workflows for production releases
• Reproducible deployments from artifacts
• Support for multiple model types and runtimes

Useful features:

• A/B testing
• Traffic splitting
• Shadow deployments
• Version pinning for deterministic behavior

7) Scalability and performance

You need predictable production behavior.

Look for:

• Autoscaling based on queue depth, RPS, latency, or GPU utilization
• Batch inference support if relevant
• Streaming responses if needed
• GPU scheduling and bin packing
• Cold-start mitigation
• Concurrency controls
• SLA/SLO support

Key questions:

• How does it handle burst traffic?
• Can it scale to zero, and is that acceptable for your latency needs?
• Does it support model quantization, TensorRT, vLLM, Triton, or similar optimizations?

8) Compliance and governance

If you care about enterprise controls, this matters a lot.

Look for:

• SOC 2 / ISO 27001 / HIPAA / PCI alignment if relevant
• Data residency controls
• Audit-ready access reports
• Approval workflows
• Separation of duties
• Support for legal hold / retention policies
• Evidence export for audits

9) Operational control

You’ll want visibility and the ability to troubleshoot without exposing sensitive data.

Look for:

• Endpoint health checks
• Deployment status dashboards
• Alerting integrations
• Dead-letter or retry handling for failed requests
• Secret management integration with your vault/KMS
• Infrastructure-as-code support: Terraform, Helm, Pulumi, etc.

10) Vendor lock-in and portability

If your security or compliance requirements change, portability matters.

Look for:

• Standard APIs or OpenAI-compatible endpoints if useful
• Container-based deployment options
• Kubernetes support
• Ability to export models/configs/logs
• Minimal proprietary coupling to the platform

A good “must-have” baseline

For your use case, I’d treat these as non-negotiable:

• Runs in your own account
• Private-only networking
• SSO + RBAC
• Audit logs
• Encryption with your keys
• No data used for training
• Log retention controls
• IaC support
• Model/version rollback

Red flags

Be careful if the vendor:

• Requires public internet access for inference or control plane
• Can’t separate tenant data cleanly
• Logs prompts/responses without strong controls
• Doesn’t support IAM/SSO
• Has weak audit logging
• Can’t show where data is stored or processed
• Uses your data for improvement by default
• Has opaque subprocessor or dependency chains

A simple evaluation template

When comparing platforms, ask them to prove:

1. Can it deploy in our VPC/VNet with no public ingress?
2. How is auth handled for users and services?
3. What exactly is logged, retained, and exported?
4. Can we use our own KMS keys?
5. Can we disable all nonessential telemetry?
6. How do rollbacks and versioning work?
7. How do we integrate with our SIEM and IAM?
8. What are the failure modes and recovery procedures?
9. Can we inspect or constrain egress?
10. What compliance artifacts are available?

If you want, I can turn this into a vendor scorecard or a requirements checklist you can paste into an RFP.