> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nemo/datadesigner/llms.txt. > For full documentation content, see https://docs.nvidia.com/nemo/datadesigner/llms-full.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/datadesigner/_mcp/server. # Deployment Options: Library vs. Microservice Data Designer is available as both an **open-source library** and a **NeMo Microservice**. This guide helps you choose the right deployment option for your use case. ## Deployment Architectures at a Glance Data Designer supports three main deployment patterns: * **Library + Your LLM Provider** *** Each user runs the library locally and connects to their choice of LLM provider. ![Library with Decentralized Providers](https://files.buildwithfern.com/datadesigner.docs.buildwithfern.com/nemo/datadesigner/a724ad3c5bba93c6e7c51d19d88395eb92c24cadebab0c3d88161dfbe4c0b2a4/images/deployment-library-decentralized.png) * **Library + Enterprise Gateway** *** Users run the library locally but share a centralized enterprise LLM gateway with RBAC and governance. ![Library with Enterprise Gateway](https://files.buildwithfern.com/datadesigner.docs.buildwithfern.com/nemo/datadesigner/10d2711d8c27c520ceba506b1f6ebf4b35fcc329ba8fce83c1a07ba926d084bc/images/deployment-enterprise-gateway.png) * **SDG as a Service (Microservice)** *** A centralized SDG service that multiple users access via REST API. ![SDG Microservice](https://files.buildwithfern.com/datadesigner.docs.buildwithfern.com/nemo/datadesigner/0fcfb18fa2ee3e605ef636122d7920cc9b45384b229375fa639aa739e5566d61/images/deployment-microservice.png) ## Quick Comparison | Aspect | Open-Source Library | NeMo Microservice | | ---------------- | ----------------------------------------------------------------- | -------------------------------------------------------- | | **What it is** | Python package you import and run | REST API service exposing `preview` and `create` methods | | **Best for** | Developers with LLM access who want flexibility and customization | Teams using NeMo Microservices platform | | **LLM Access** | You provide (any OpenAI-compatible API) | Integrated with NeMo Microservices Platform | | **Installation** | `pip install data-designer` | Deploy via NeMo Microservices platform | | **Scaling** | You manage inference capacity | Managed alongside other NeMo services | Same Configuration API Both the library and microservice use the **same `DataDesignerConfigBuilder` API**. Start with the library, and your configurations migrate seamlessly if you later adopt the NeMo platform. ## 📦 When to Use the Open-Source Library The library is the right choice for most users. Choose it if you: ### You Have Access to LLMs ![Library with Decentralized Providers](https://files.buildwithfern.com/datadesigner.docs.buildwithfern.com/nemo/datadesigner/a724ad3c5bba93c6e7c51d19d88395eb92c24cadebab0c3d88161dfbe4c0b2a4/images/deployment-library-decentralized.png) You have API keys or endpoints for LLM inference: * **Cloud APIs**: NVIDIA API Catalog (build.nvidia.com), OpenAI, Azure OpenAI, Anthropic * **Self-hosted**: vLLM, TGI, TensorRT-LLM, or any OpenAI-compatible server * **Enterprise gateways**: Centralized LLM gateway with RBAC, rate limiting, or other enterprise features ```python from data_designer.interface import DataDesigner from data_designer.config import ModelConfig # Use any OpenAI-compatible endpoint model = ModelConfig( alias="my-model", model="nvidia/nemotron-3-nano-30b-a3b", provider="nvidia", # or "openai", or a custom ModelProvider ) dd = DataDesigner() # Your code controls the full workflow ``` ### You Need Maximum Flexibility * **Custom plugins**: Extend Data Designer with custom column generators, validators, or processors * **Local development**: Rapid iteration with immediate feedback * **Integration**: Embed Data Designer into existing Python pipelines or notebooks * **Experimentation**: Research workflows with custom models or configurations ### You Already Have Enterprise LLM Infrastructure ![Library with Enterprise Gateway](https://files.buildwithfern.com/datadesigner.docs.buildwithfern.com/nemo/datadesigner/10d2711d8c27c520ceba506b1f6ebf4b35fcc329ba8fce83c1a07ba926d084bc/images/deployment-enterprise-gateway.png) Library + Enterprise LLM Gateway Many enterprises already have centralized LLM access through API gateways with: * Role-based access control (RBAC) * Rate limiting and quotas * Audit logging * Cost allocation In this case, **use the library** and point it at your enterprise gateway. You get enterprise-grade LLM access while retaining full control over your Data Designer workflows. ```python from data_designer.config import ModelConfig, ModelProvider # Define your enterprise gateway as a provider enterprise_provider = ModelProvider( name="enterprise-gateway", endpoint="https://llm-gateway.yourcompany.com/v1", api_key="ENTERPRISE_LLM_KEY", # Environment variable name (uppercase) or actual key ) # Use the provider in your model config model = ModelConfig( alias="enterprise-llm", model="gpt-4", provider="enterprise-gateway", # References the provider above ) ``` *** ## ☁️ When to Use the Microservice ![SDG Microservice](https://files.buildwithfern.com/datadesigner.docs.buildwithfern.com/nemo/datadesigner/0fcfb18fa2ee3e605ef636122d7920cc9b45384b229375fa639aa739e5566d61/images/deployment-microservice.png) The NeMo Microservice exposes Data Designer's `preview` and `create` methods as REST API endpoints. Choose it if you: ### You're Using the NeMo Microservices Platform The primary value of the microservice is **integration with other NeMo Microservices**: * **NeMo Inference Microservices (NIMs)**: Seamless integration with NVIDIA's optimized inference endpoints * **NeMo Customizer**: Generate synthetic data for model fine-tuning workflows * **NeMo Evaluator**: Create evaluation datasets alongside model assessment * **Unified deployment**: Single platform for your entire AI pipeline ### You Want to Expose SDG as a Team Service If you need to provide synthetic data generation as a shared service: * **Multi-tenant access**: Multiple teams submit generation jobs via API * **Job management**: Queue, monitor, and manage generation jobs centrally * **Resource sharing**: Shared infrastructure for SDG workloads When users can submit configs containing Jinja templates to a shared engine, template rendering becomes a remote code execution concern and part of your security boundary. See [Security](/concepts/security) for guidance on when to keep the default `JinjaRenderingEngine.SECURE` mode. *** ## 🧭 Decision Flowchart ``` ┌─────────────────────────┐ │ Are you using the NeMo │ │ Microservices platform? │ └───────────┬─────────────┘ │ ┌───────────┴───────────┐ ▼ ▼ YES NO │ │ ▼ ▼ ┌───────────────────┐ ┌───────────────────────────┐ │ Use Microservice │ │ Do you need to expose SDG │ │ │ │ as a shared REST service? │ │ Integrates with │ └─────────────┬─────────────┘ │ NIMs, Customizer, │ │ │ Evaluator │ ┌───────────┴───────────┐ └───────────────────┘ ▼ ▼ YES NO │ │ ▼ ▼ ┌─────────────────────┐ ┌─────────────────┐ │ Consider if the │ │ Use the Library │ │ overhead is worth │ │ │ │ it vs. library + │ │ Most flexible │ │ enterprise gateway │ │ option for │ └─────────────────────┘ │ direct use │ └─────────────────┘ ``` *** ## Learn More * **Library**: Continue with this documentation * **Microservice**: See the [NeMo Data Designer Microservice documentation](https://docs.nvidia.com/nemo/microservices/latest/design-synthetic-data-from-scratch-or-seeds/index.html) * **Security model**: See [Security](/concepts/security)