CoreWeave vs Lambda Labs vs RunPod GPU Cloud Security: What Changes When Your Training Infrastructure Is Not AWS

CoreWeave provides the most hyperscaler-like network isolation. CoreWeave runs workloads on Kubernetes and offers:

• Private VPC-equivalent networking with dedicated IP space per customer
• Layer 4 network policies using Kubernetes NetworkPolicy resources
• Private connectivity to external networks via VPN or dedicated interconnect
• Instances not exposed to the public internet by default unless explicitly configured

For AI training workloads, CoreWeave's network isolation is comparable to running on AWS EKS with VPC networking -- tenant workloads cannot reach other tenants' networks by default.

Lambda Labs offers network isolation through SSH-key-based access and optional private networking on enterprise plans. The default configuration:

• Instances are internet-accessible via SSH on a public IP
• No native security group equivalent for filtering inbound traffic by default (firewall rules are configured at the OS level using iptables or ufw)
• Enterprise plans offer VPC-style private networking and dedicated instances not shared with other tenants

For non-enterprise Lambda accounts, the security model requires OS-level firewall configuration to restrict access, which is functional but requires the security team to manage it rather than the provider.

RunPod operates a marketplace model where GPU instances come from both RunPod's own data centers ('Secure Cloud') and community-contributed hardware ('Community Cloud'):

• Secure Cloud pods have network isolation between tenants
• Community Cloud pods run on third-party hardware with less network isolation guarantees
• By default, pods expose ports via RunPod's public proxy layer, which requires explicit port mapping rather than direct IP-level firewall control

For training workloads that involve sensitive model weights or proprietary training data, RunPod Secure Cloud is the minimum acceptable configuration; Community Cloud is not appropriate for sensitive workloads.

CoreWeave offers Kubernetes-native RBAC for controlling access to cluster resources:

• Service accounts with scoped permissions per namespace
• Integration with external identity providers via OIDC
• Multi-user access with role-based access controls at the organization level
• API key management for programmatic access

This is the closest to AWS IAM of the three providers and allows security teams to scope permissions per training job rather than granting broad cluster access.

Lambda Labs provides:

• API keys for programmatic access (single key per account by default on standard plans)
• SSH keys per instance for direct access
• Team members can be added to an organization with account-level access; granular per-resource permissions are not available on standard plans

For organizations with multiple teams running different training workloads, Lambda Labs' IAM model requires either separate accounts per team or accepting that all team members have equivalent access to all running instances.

RunPod provides:

• API keys for pod management (create, stop, terminate)
• No RBAC equivalent; API key access is all-or-nothing for the account
• SSH access to pods using keys provided at pod creation

For multi-team environments, RunPod's access model requires a separate API key strategy (one account per team) or accepts that all team members share equivalent pod management access.

None of the three providers offers a native secrets management service equivalent to AWS Secrets Manager, HashiCorp Vault, or Azure Key Vault. Security teams must implement secrets management externally:

Common approaches for GPU cloud secrets management:

1. External vault with instance authentication. Run HashiCorp Vault or AWS Secrets Manager as the secrets backend, and authenticate GPU instances using a fixed token or a Vault AppRole. This works but requires the instance to have a secret (the initial Vault token or AppRole secret ID) to retrieve secrets -- the bootstrap problem.

2. CoreWeave Kubernetes Secrets. For CoreWeave workloads, Kubernetes Secrets provide a native (though not ideal) secrets management mechanism. Secrets can be encrypted at rest using customer-provided KMS keys and accessed via Kubernetes volume mounts. This is the most integrated option for CoreWeave environments.

3. Environment variable injection at job submission. The simplest approach: inject secrets as environment variables at job submission time from the CI/CD pipeline. This avoids the bootstrap problem but stores secrets in job logs and system environment dumps. Acceptable for non-production training; not for production model training with proprietary data.

4. Sealed Secrets or external-secrets-operator for CoreWeave. For CoreWeave Kubernetes environments, Bitnami Sealed Secrets or the external-secrets-operator provides encrypted secret storage that integrates with AWS Secrets Manager or Vault without storing secrets in Kubernetes plaintext.

CoreWeave provides Kubernetes audit logs that record API server interactions (pod creation, namespace changes, service account usage). For compliance-oriented teams, CoreWeave's Kubernetes audit logs are queryable and can be exported to external SIEM systems. GPU workload activity (what jobs ran, what data was accessed) is captured through the job's own logging, not through a provider-managed audit log service.

Lambda Labs provides:

• Instance create/terminate event logs accessible through the API and dashboard
• No equivalent to CloudTrail for recording API calls and their callers
• SSH access logs at the OS level (which requires the security team to configure and export them)

RunPod provides:

• Pod lifecycle events (create, start, stop, terminate) via the API
• No API-call audit log equivalent

For all three providers, security teams that need comprehensive audit trails must implement their own logging: configure the OS to send syslog and auth.log to an external SIEM, instrument ML training code to log data access events, and use the provider's API to export pod/instance lifecycle events.

Compliance implication: Organizations with SOC 2, HIPAA, or ISO 27001 requirements that require comprehensive audit logs of access to training data and model artifacts must implement this logging layer themselves when using GPU cloud providers. This is a material difference from AWS, which provides this via CloudTrail and S3 access logging without additional configuration.

At-rest encryption: All three providers encrypt physical storage at the hardware level. For tenant-managed encryption above the hardware layer:

• CoreWeave: supports customer-provided KMS keys for Kubernetes Secrets encryption
• Lambda Labs: block storage volumes are encrypted at rest by Lambda Labs; customer-managed key support is not available on standard plans
• RunPod: pod storage is encrypted at the platform level; persistent storage volumes use provider-managed encryption

For training data and model weights that require customer-managed key (CMK) encryption, CoreWeave is the only provider that supports CMK out of the box. Lambda Labs and RunPod require keeping training data in an external store (AWS S3, GCS) with CMK encryption, and accessing it over the network during training.

In-transit encryption: All three providers use TLS for API and SSH connections. Intra-cluster or inter-pod traffic within a training job is not encrypted by default on any provider -- this is standard for training job communication (NCCL for GPU communication, MPI for distributed training). If in-transit encryption for training communication is required, it must be implemented at the application layer.

Key gaps that every GPU cloud deployment needs to bridge:

1. External secrets management (Vault, AWS Secrets Manager, or sealed secrets)
2. Centralized log collection for auth.log, syslog, and GPU job logs
3. Network egress filtering (iptables/ufw or provider-native where available) to prevent training jobs from reaching unintended external hosts
4. Data classification before moving training data to any GPU cloud provider