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Available on the Enterprise plan.
Bring Your Own Model (BYOM) lets you connect your own LLM provider to power AI agents in Cube, instead of using the built-in models. This gives you full control over which models your agents use, where your data is processed, and how you manage AI costs.

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Supported providers

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Configuration

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Step 1: Add a model

Before assigning a BYOM model to an agent, you need to register it in the admin panel:
  1. Navigate to Admin > Models
  2. Click Add Model
  3. Provide a name for the model
  4. Select the model type (LLM or Embedding)
  5. Choose a provider and model
  6. Enter the required credentials for the provider

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Step 2: Assign the model to an agent

Once a model is registered, reference it in the agents YAML configuration by name or ID:
Each agent can use a different model. If no BYOM model is specified, the agent uses the built-in default.
Switching embedding models for an agent means existing memories stored with the previous embedding model will not be compatible. Memories are tied to the embedding model that created them.

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Network configuration

When using BYOM, Cube connects to your model provider from its control plane. If your provider requires IP allowlisting, ensure the Cube outbound IP addresses are added to your allowlist. For agents running in dedicated regions, additional per-region IP addresses may also need to be allowlisted.

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Billing

When using a BYOM model, Cube AI tokens are not consumed. You are billed directly by your model provider based on their pricing. This means:
  • No Cube token quota is deducted for BYOM chat requests
  • No token usage is tracked in the AI Tokens Usage dashboard for BYOM requests
  • Per-seat token grants and token packages do not apply
See AI Tokens for details on how token billing works with built-in models.

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Provider-specific notes

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Anthropic

Supports extended thinking mode for compatible models. Configure this in the model settings when creating the model.

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AWS Bedrock

  • Credentials are optional — if left empty, the default AWS credential chain is used (e.g., workload identity)
  • Supports assume-role configuration for cross-account access
  • Supports inference profiles

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GCP Vertex AI

Requires a service account JSON key for authentication.

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Databricks

Requires a workspace URL and access token.

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Snowflake Cortex

Supports two authentication methods:
  • JWT authentication
  • Key-pair authentication (requires an encrypted PKCS#8 PEM private key)

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Custom OpenAI-compatible

Points an agent at any endpoint that implements the OpenAI chat completions API — for example, an internal LLM gateway.
  • Model and base URL are free text; the base URL must use http or https and may not target a loopback or link-local host
  • API key is optional — leave it blank if your endpoint authenticates requests some other way, such as a custom header
  • You can add custom HTTP headers sent with every request. Header values may include placeholders resolved per request from the caller’s identity: {{userId}}, {{userExternalId}}, {{email}}, {{username}}, {{tenantId}}, {{tenantName}}, {{tenantUrl}}, {{deploymentId}}, and {{agentId}}. A placeholder with no value for a request — {{userExternalId}} for a user who did not arrive through signed embedding, for example — is sent through as literal text

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Custom Anthropic-compatible

Points an agent at any endpoint that implements the Anthropic messages API. Configuration is the same as Custom OpenAI-compatible: a free-text model and base URL, an optional API key, and optional custom headers with the same placeholders.

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Troubleshooting

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Rate limit errors

If you see rate limit errors, the limits are enforced by your model provider, not by Cube. Check your provider’s rate limits and usage quotas.

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Authentication errors

Verify that the API key or credentials configured for the model are valid and have the necessary permissions.

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Model not found

Ensure the model ID configured in Cube matches a valid model offered by your provider. Model availability may vary by region.