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.
Supported providers
| Provider | Chat models | Embedding models |
|---|
| Anthropic | Yes | No |
| OpenAI | Yes | Yes |
| AWS Bedrock | Yes | Yes |
| GCP Vertex AI | Yes | No |
| Databricks | Yes | No |
| Snowflake Cortex | Yes | No |
Configuration
Step 1: Add a model
Before assigning a BYOM model to an agent, you need to register it in the
admin panel:
- Navigate to Admin > Models
- Click Add Model
- Provide a name for the model
- Select the model type (LLM or Embedding)
- Choose a provider and model
- Enter the required credentials for the provider
Step 2: Assign the model to an agent
Once a model is registered, reference it in the agents YAML configuration by
name or ID:
agents:
- name: sales-analyst
llm:
byom:
name: "my-anthropic-model"
embedding_llm:
byom:
name: "my-bedrock-embeddings"
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.
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.
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.
Provider-specific notes
Anthropic
Supports extended thinking mode for compatible models. Configure this in the
model settings when creating the model.
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
GCP Vertex AI
Requires a service account JSON key for authentication.
Databricks
Requires a workspace URL and access token.
Snowflake Cortex
Supports two authentication methods:
- JWT authentication
- Key-pair authentication (requires an encrypted PKCS#8 PEM private key)
Troubleshooting
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.
Authentication errors
Verify that the API key or credentials configured for the model are valid and
have the necessary permissions.
Model not found
Ensure the model ID configured in Cube matches a valid model offered by your
provider. Model availability may vary by region. 
