litellm/terraform/provider/docs/index.md
devin-ai-integration[bot] 592518202c
feat(terraform): add litellm_jwt_key_mapping resource (#38714)
* feat(terraform): add litellm_jwt_key_mapping resource

Adds a Terraform resource for the proxy's JWT to virtual key mappings, so a
JWT client identified by a claim such as client_id, azp or sub maps to a
virtual key and inherits its models, budgets, rate limits and spend tracking.

Covers the four mapping endpoints: /jwt/key/mapping/new, /info, /update and
/delete. is_active is applied through a follow-up update because the create
endpoint always starts a mapping active, a dropped description is sent as an
empty string because the update endpoint ignores absent fields, changing the
mapped key rotates it in place, and changing the claim name or value forces
replacement since the update endpoint cannot change them.

* fix(terraform): revert key on failed jwt_key_mapping update

Classic SDKv2 persists a failed Update's diff-applied values to state
regardless of the error, so a rejected key rotation left the new key in
state while the proxy kept the old one and the next plan falsely converged.
Revert key via GetChange and resync description/is_active/computed fields
from a post-failure Read, since Read alone can't recover key (the proxy
never returns it).

Also drop the case-insensitive "mapping not found" body match: the proxy
raises 404 for all three not-found paths (info, update, delete), so
checking the status code alone is sufficient.

Clarify the docs: referencing a litellm_key resource's write-only key is
not a null-then-400 situation, it's a static "Missing required argument"
error at plan time, in every apply ordering.

* fix(terraform): stop leaving an active mapping behind on failed cleanup

Two issues flagged by review:

- Create has no way to ask the proxy for an inactive mapping, so an
  is_active=false mapping is briefly active while the follow-up
  deactivation runs. If that deactivation call itself fails, the mapping
  used to stay active and untracked. It's now deleted instead, closing
  the exposure rather than leaving it open indefinitely.
- On a failed update, only `key` was reverted before the recovery read.
  If that read also failed, description/is_active kept the rejected
  values, so a later plan could report false convergence. Now all three
  are reverted before the read runs.

Both come with regression tests, mutation-verified against the pre-fix
code.

* fix(deps): bump restrictedpython to 8.5 for GHSA-ffg3-p8fm-mjx2

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* chore: retrigger ci

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* fix(tests): stub anthropic judge credentials in funnel seeding test

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* revert(deps): keep uv.lock unchanged to keep the PR terraform-only

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

---------

Co-authored-by: Fabrice Pont <fabrice.pont@doctolib.com>
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
2026-08-28 17:10:44 -07:00

4.2 KiB

LiteLLM Provider

The LiteLLM provider allows Terraform to manage LiteLLM resources. LiteLLM is a proxy service that standardizes the input/output across different LLM APIs, providing a unified interface for various language model providers.

Example Usage

terraform {
  required_providers {
    litellm = {
      source = "registry.terraform.io/BerriAI/litellm"
    }
  }
}

provider "litellm" {
  api_base = "https://your-litellm-proxy.com"
  api_key  = var.litellm_api_key
}

# Basic model configuration
resource "litellm_model" "gpt4" {
  model_name          = "gpt-4-proxy"
  custom_llm_provider = "openai"
  model_api_key       = var.openai_api_key
  base_model          = "gpt-4"
  tier                = "paid"
  mode                = "chat"
  
  input_cost_per_million_tokens  = 30.0
  output_cost_per_million_tokens = 60.0
}

# Team configuration
resource "litellm_team" "dev_team" {
  team_alias = "development-team"
  models     = [litellm_model.gpt4.model_name]
  max_budget = 100.0
}

Available Resources

The LiteLLM provider supports the following resources:

Available Data Sources

The LiteLLM provider supports the following data sources:

Authentication

The LiteLLM provider requires an API key and base URL for authentication. These can be provided in the provider configuration block or via environment variables.

Environment Variables

  • LITELLM_API_BASE - The base URL of your LiteLLM instance
  • LITELLM_API_KEY - Your LiteLLM API key

Example with Environment Variables

export LITELLM_API_BASE="https://your-litellm-proxy.com"
export LITELLM_API_KEY="your-api-key"
terraform {
  required_providers {
    litellm = {
      source = "registry.terraform.io/BerriAI/litellm"
    }
  }
}

# Provider will automatically use environment variables
provider "litellm" {}

Provider Arguments

The following arguments are supported in the provider block:

  • api_base - (Required) The base URL of your LiteLLM instance. This can also be provided via the LITELLM_API_BASE environment variable.
  • api_key - (Required) The API key used to authenticate with LiteLLM. This can also be provided via the LITELLM_API_KEY environment variable.

Getting Started

  1. Install the provider by adding it to your Terraform configuration
  2. Configure your LiteLLM instance URL and API key
  3. Start creating resources like models, teams, and credentials
  4. Use data sources to reference existing configurations

For detailed examples and configuration options, see the individual resource and data source documentation pages.

Examples

This repository includes an examples/ directory with curated, ready-to-run HCL examples that demonstrate common and advanced usages of the provider. Examples are grouped by resource and illustrate provider-specific configuration, handling of sensitive values, and advanced options such as additional_litellm_params.

See:

  • examples/model_additional_params.tf — demonstrates how to use additional_litellm_params (booleans, integers, floats, and strings).
  • Other example files will be added to examples/ for credentials, vector stores, and MCP servers.

You can reference these examples directly or copy snippets into your Terraform configurations for quick starts.

For detailed examples and configuration options, see the individual resource and data source documentation pages.