Heads-up: In January 2026 this provider will undergo a major rewrite. Releases will track LiteLLM versions directly (provider version = targeted LiteLLM version), with monthly updates on stable LiteLLM releases only. Expect breaking changes during this transition.
This Terraform provider allows you to manage LiteLLM resources through Infrastructure as Code. It provides support for managing models, teams, team members, and API keys via the LiteLLM REST API.
- Manage LiteLLM model configurations
- Associate models with specific teams
- Create and manage teams
- Configure team members and their permissions
- Set usage limits and budgets
- Control access to specific models
- Specify model modes (e.g., completion, embedding, image generation)
- Manage API keys with fine-grained controls
- Support for reasoning effort configuration in the model resource
To use the LiteLLM provider in your Terraform configuration, you need to declare it in the terraform block:
terraform {
required_providers {
litellm = {
source = "ncecere/litellm"
version = "~> 0.3.11"
}
}
}
provider "litellm" {
api_base = var.litellm_api_base
api_key = var.litellm_api_key
}Then, you can use the provider to manage LiteLLM resources. Here's an example of creating a model configuration:
resource "litellm_model" "gpt4" {
model_name = "gpt-4-proxy"
custom_llm_provider = "openai"
model_api_key = var.openai_api_key
model_api_base = "https://api.openai.com/v1"
base_model = "gpt-4"
tier = "paid"
mode = "chat"
reasoning_effort = "medium" # Optional: "low", "medium", or "high"
input_cost_per_million_tokens = 30.0
output_cost_per_million_tokens = 60.0
}For full details on the litellm_model resource, see the model resource documentation.
Here's an example of creating an API key with various options:
resource "litellm_key" "example_key" {
models = ["gpt-4", "claude-3.5-sonnet"]
allowed_routes = ["/chat/completions", "/keys/*"]
max_budget = 100.0
user_id = "user123"
team_id = "team456"
max_parallel_requests = 5
tpm_limit = 1000
rpm_limit = 60
budget_duration = "monthly"
key_alias = "prod-key-1"
duration = "30d"
metadata = {
environment = "production"
}
allowed_cache_controls = ["no-cache", "max-age=3600"]
soft_budget = 80.0
aliases = {
"gpt-4" = "gpt4"
}
config = {
default_model = "gpt-4"
}
permissions = {
can_create_keys = "true"
}
model_max_budget = {
"gpt-4" = 50.0
}
model_rpm_limit = {
"claude-3.5-sonnet" = 30
}
model_tpm_limit = {
"gpt-4" = 500
}
guardrails = ["content_filter", "token_limit"]
blocked = false
tags = ["production", "api"]
}The litellm_key resource supports the following options:
models: List of allowed models for this keyallowed_routes/allowed_passthrough_routes: Restrict which LiteLLM proxy routes this key may callmax_budget: Maximum budget for the keyuser_idandteam_id: Associate the key with a user and teamservice_account_id: Create a team-owned service account key (defaultskey_aliasand injects metadata)max_parallel_requests: Limit concurrent requeststpm_limitandrpm_limit: Set tokens and requests per minute limitsbudget_duration: Specify budget duration (e.g., "monthly", "weekly")key_alias: Set a friendly name for the keyduration: Set the key's validity periodmetadata: Add custom metadata to the keyallowed_cache_controls: Specify allowed cache control directivessoft_budget: Set a soft budget limitaliases: Define model aliasesconfig: Set configuration optionspermissions: Specify key permissionsmodel_max_budget,model_rpm_limit,model_tpm_limit: Set per-model limitsguardrails: Apply specific guardrails to the keyblocked: Flag to block/unblock the keytags: Add tags for organization and filtering
When team_id is set and models is omitted, the provider automatically allows the key to use all models in that team by sending ["all-team-models"] to the API.
For full details on the litellm_key resource, see the key resource documentation.
litellm_model: Manage model configurations. Documentationlitellm_team: Manage teams. Documentationlitellm_team_member: Manage team members. Documentationlitellm_team_member_add: Add multiple members to teams. Documentationlitellm_key: Manage API keys. Documentationlitellm_mcp_server: Manage MCP (Model Context Protocol) servers. Documentationlitellm_credential: Manage credentials for secure authentication. Documentationlitellm_vector_store: Manage vector stores for embeddings and RAG. Documentation
litellm_credential: Retrieve information about existing credentials. Documentationlitellm_vector_store: Retrieve information about existing vector stores. Documentation
The project is organized as follows:
terraform-provider-litellm/
├── litellm/
│ ├── provider.go
│ ├── resource_model.go
│ ├── resource_model_crud.go
│ ├── resource_team.go
│ ├── resource_team_member.go
│ ├── resource_key.go
│ ├── resource_key_utils.go
│ ├── types.go
│ └── utils.go
├── main.go
├── go.mod
├── go.sum
├── Makefile
└── ...
- Clone the repository:
git clone https://github.com/your-username/terraform-provider-litellm.git- Enter the repository directory:
cd terraform-provider-litellm- Build and install the provider:
make installThe Makefile provides several useful commands for development:
make build: Builds the providermake install: Builds and installs the providermake test: Runs the test suitemake fmt: Formats the codemake vet: Runs go vetmake lint: Runs golangci-lintmake clean: Removes build artifacts and installed provider
To run the tests:
make testContributions are welcome! Please read our contributing guidelines first.
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
- Always use environment variables or secure secret management solutions to handle sensitive information like API keys and AWS credentials.
- Refer to the comprehensive documentation in the
docs/directory for detailed usage examples and configuration options. - Make sure to keep your provider version updated for the latest features and bug fixes.
- The provider now supports AWS cross-account access with
aws_session_nameandaws_role_nameparameters in the model resource. - All example configurations have been consolidated into the documentation for better organization and maintenance.