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Version: 8.10 (unreleased)

Camunda-provided LLM

Run AI agents quickly in Camunda SaaS with Camunda-provided LLM.

About​

Camunda-provided LLM is a Camunda-managed LLM provider option that comes with automatically configured credentials. With it, you can run AI agents in your processes right away without additional setup.

Camunda-provided LLM is only available in Camunda 8 SaaS. It is not available in Camunda 8 Self-Managed.

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Camunda-provided LLM is free to use within the provided budget, and is intended for testing and experimentation. When you're ready for production or need more control, switch to a customer-managed provider.

Camunda-provided LLM is available in Camunda SaaS for:

  • SaaS trial organizations: Includes Camunda-managed credentials and a free budget.
  • SaaS enterprise organizations: Includes a larger budget to support multiple proofs of concept. You must explicitly enable AI features. When you enable them, Camunda-provided LLM is enabled automatically. If Camunda-provided LLM is unavailable, disable AI features and then re-enable them.
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Availability, budgets, and UI may vary by environment and rollout stage.

See Trial vs. enterprise budgets for more details.

Set up Camunda-provided LLM​

Once Camunda-provided LLM is available in your organization, its credentials are populated automatically as cluster secrets.

  • If you are using an AI agent blueprint, no additional configuration is needed in most cases. Explore selected AI agent blueprints in the Camunda Marketplace.
  • If you are building your own agent from scratch, enable Camunda-provided LLM by configuring your AI Agent connector with the following parameters:
    • Provider: OpenAI Compatible.
    • API endpoint: {{secrets.CAMUNDA_PROVIDED_LLM_API_ENDPOINT}}.
    • API key: {{secrets.CAMUNDA_PROVIDED_LLM_API_KEY}}.
    • Model: Select a model from the list of supported models. For example amazon.nova-pro-v1.

Supported models​

Camunda-provided LLM supports multiple Bedrock-backed models. When using the AI Agent connector, set the Model field to one of the following values:

ModelValue to set in ModelWhat it's good for
Amazon Nova Pro v1amazon.nova-pro-v1Best for balanced quality and cost across general-purpose AI agent scenarios.
Anthropic Claude Haiku 4.5anthropic.claude-haiku-4-5Best for lightweight assistants, short interactions, and lower-cost tasks that still need good instruction following.
Anthropic Claude Opus 4.5anthropic.claude-opus-4-5Best for advanced analysis and challenging multi-step tasks where maximum quality is the priority.
Anthropic Claude Sonnet 4.6anthropic.claude-sonnet-4-6Best for complex, high-stakes agent tasks where strong reasoning and reliable tool use matter most.
DeepSeek v3.2deepseek.v3.2Best for technical and coding-heavy workflows that need strong reasoning at moderate cost.
OpenAI GPT-OSS 120Bopenai.gpt-oss-120bBest for higher-quality results than small open models while still controlling cost.
OpenAI GPT-OSS 20Bopenai.gpt-oss-20bBest for budget-conscious experimentation and simpler automations with lower complexity.
Qwen Qwen3 235Bqwen.qwen3-235bBest for advanced reasoning and coding use cases where you want strong performance with good cost efficiency.
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When selecting a model, consider your process requirements, expected usage volume, and token budget. For model selection guidelines, see how to choose the right LLM.

Trial vs. enterprise budgets​

The budgets, measured in dollars (USD) spent, differ depending on your SaaS plan:

  • Trial: A smaller budget intended for quick evaluation and early experiments by individuals and small teams.
  • Enterprise: A larger budget intended for broader team experimentation and proofs of concept.
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Budgets are topped up automatically and enforced at the organization level (not per user). This means multiple users in the same organization draw from the same budget.

What the budget cover​

The Camunda-provided LLM budget covers LLM provider calls during AI agent execution:

  • Trial budget: Allows for a hundred to a few thousand agent runs, depending on the model used and the agent complexity.
  • Enterprise budget: Is significantly larger to support more extensive experimentation.

Other Camunda AI features, such as Camunda Copilot, do not consume your Camunda-provided LLM budget and can be used independently.

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The total cost of an agent run depends on how many LLM calls it makes, which can vary based on the agent’s design and task complexity. Cost also depends on the model used, since different models have different per-token pricing.

When budget is exhausted​

When your organization reaches its Camunda-provided LLM budget cap:

  • Additional LLM calls are blocked.
  • Your process execution may fail with an “out of budget” error, such as COST_LIMIT_EXCEEDED, depending on how your process handles errors.
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If your process model doesn’t handle LLM failures, an exhausted budget may result in incidents or failed instances. Consider adding BPMN error handling to provide a user-friendly fallback path.

Switch away from Camunda-provided LLM​

As you move from evaluation to production, you may want to switch to your own LLM provider. This gives you:

  • Direct control over provider choice.
  • Your own billing and quota management.
  • The ability to scale beyond the Camunda-provided LLM budget caps.
Before you begin
  • Ensure your organization has access to the LLM provider you plan to use.
  • Gather credentials and any required configuration.
  • Identify where your current AI agent models rely on Camunda-provided LLM defaults.

To switch away, follow these steps:

  1. Add your LLM provider credentials in the appropriate Camunda location for managing secrets and credentials.
  2. Update your AI Agent connector configuration to use the new LLM provider.
  3. Re-deploy your process.
  4. Test a process instance end-to-end and verify results.