Dynamiq Docs
  • Welcome to Dynamiq
  • Low-Code Builder
    • Chat
    • Basics
    • Connecting Nodes
    • Conditional Nodes and Multiple Outputs
    • Input and Output Transformers
    • Error Handling and Retries
    • LLM Nodes
    • Validator Nodes
    • RAG Nodes
      • Indexing Workflow
        • Pre-processing Nodes
        • Document Splitting
        • Document Embedders
        • Document Writers
      • Inference RAG workflow
        • Text embedders
        • Document retrievers
          • Complex retrievers
        • LLM Answer Generators
    • LLM Agents
      • Basics
      • Guide to Implementing LLM Agents: ReAct and Simple Agents
      • Guide to Agent Orchestration: Linear and Adaptive Orchestrators
      • Guide to Advanced Agent Orchestration: Graph Orchestrator
    • Audio and voice
    • Tools and External Integrations
    • Python Code in Workflows
    • Memory
    • Guardrails
  • Deployments
    • Workflows
      • Tracing Workflow Execution
    • LLMs
      • Fine-tuned Adapters
      • Supported Models
    • Vector Databases
  • Prompts
    • Prompt Playground
  • Connections
  • LLM Fine-tuning
    • Basics
    • Using Adapters
    • Preparing Data
    • Supported Models
    • Parameters Guide
  • Knowledge Bases
  • Evaluations
    • Metrics
      • LLM-as-a-Judge
      • Predefined metrics
        • Faithfulness
        • Context Precision
        • Context Recall
        • Factual Correctness
        • Answer Correctness
      • Python Code Metrics
    • Datasets
    • Evaluation Runs
    • Examples
      • Build Accurate vs. Inaccurate Workflows
  • Examples
    • Building a Search Assistant
      • Approach 1: Single Agent with a Defined Role
      • Approach 2: Adaptive Orchestrator with Multiple Agents
      • Approach 3: Custom Logic Pipeline with a Straightforward Workflow
    • Building a Code Assistant
  • Platform Settings
    • Access Keys
    • Organizations
    • Settings
    • Billing
  • On-premise Deployment
    • AWS
    • IBM
  • Support Center
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Deployments

Choose the deployment option that best fits your organization’s needs and infrastructure. Dynamiq offers on-premise, hybrid-cloud, and native-cloud setups, giving you the flexibility to align with your security, compliance, and scalability requirements. Whether you need full control within your own infrastructure, a balanced approach that combines on-site and cloud resources, or the convenience of a fully cloud-native environment, Dynamiq adapts to support your GenAI initiatives effectively and securely.

  • On-Premise: Maintain full control over your data by deploying AI workflows and models within your own infrastructure. Our on-premise deployment capabilities allow you to implement custom security measures, ensuring adherence to regulatory compliance requirements and protecting your proprietary information.

  • Cloud-native: Run Dynamiq entirely in your preferred cloud environment, such as AWS, Azure, or GCP. With this setup, you benefit from rapid deployment, simplified maintenance, and easy scalability, all while meeting the security and compliance standards of a single-tenant cloud solution.

  • Hybrid-Cloud: Leverage both on-premise and cloud resources seamlessly. This approach lets you keep sensitive data on-site while taking advantage of the scalability and processing power of the cloud, making it easier to adapt to changing workloads and optimize resource use.

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Last updated 5 months ago