Run agents where your data lives, not where a hyperscaler says it has to.
Unify your AI to drive innovation and unlock powerful use cases. Most AI platforms make an assumption that breaks at deployment: that your organization is fully committed to a single public cloud. For regulated industries, sovereign data requirements, classified environments, and hard-won on-premise infrastructure investments, that assumption rules most platforms out. The deployment question is usually the first question, and for most platforms it’s the end of the conversation.
Get started fast with fully managed notebooks. Build with managed, zero-configuration notebooks in seconds, without the need to set up underlying infrastructure, to ensure that all your AI projects and artifacts are well-governed, organized, and easy-to-share.
Draw from a library of agentic AI apps and templates. Accelerate AI development with a library of customizable code-first agentic apps and templates. Each template contains reusable code and built-in best practices, and are fully integrated with GitHub. Or, develop your own shareable custom templates. Either way, you can deliver AI applications in hours, not weeks.
Work with your preferred tools. Our open engine, extensive APIs, broad partner ecosystem, and customizable development environments allow you to integrate your existing tools and leverage the latest technologies.
import datarobot as dr
from datarobot.models.genai.custom_model_llm_validation import CustomModelLLMValidation
import CustomModelLLMValidationUse our developer suite of coding tools. Develop and deliver agents and AI projects with the precision, control, and reproducibility of code. Discover insights, build features, develop models and tools, and programmatically schedule jobs, all within our hosted notebooks. Customizable codespace environments enable your team to ship production-ready agents and applications.
Collaborate within use cases and codespaces. Access all project data, experiments, models, AI applications, and notebooks in one easily accessible location. Use reproducible custom environments to seamlessly collaborate across hosted notebooks and codespaces.
Store, share, and manage all AI assets grouped by business problem
Share and collaborate within notebooks on the same codebase
Maintain full visibility across all AI assets. Centralize all AI assets, including agents, models, data catalogs, applications, and use cases, in our unified Registry so your entire team can access, share, and collaborate on AI projects, regardless of where they were built.
Simplify handoffs between teams. Support easy collaboration across all stakeholders involved in your AI projects and accelerate the process of bringing AI from idea to application.
Keep MRM and compliance teams audit-ready
Notify ITOps for swift resolution
Enable business teams to provide early feedback
Streamline handoffs from AI developers to ITOps
Maintain control while encouraging teamwork. Ensure that only authorized personnel can make changes to production models with role-based approval workflows (RBAC). Easily track and audit those changes with built-in versioning and lineage tracking.
Harness your data in any format or environment. Easily connect to any data source and harness data in any format—structured or unstructured. Leverage text, images, geospatial, audio, and video data from cloud warehouses, object storage, or local files using our open engine and robust ecosystem of connectors and APIs.
Accelerate AI development and deployment. Deploy agents and models with one click and integrate their output into downstream tools. Use pre-built, customizable templates to quickly build and share AI-powered applications for business users.
Simplify your AI application infrastructure. Focus on delivering AI projects without worrying about resource management, machine provisioning, or relying on IT for extra capacity. Securely share AI applications with managed authentication and reduce latency while autoscaling tasks like updating vector databases.
Optimize model serving efficiency. Choose the best deployment method for your needs with diverse options including real-time, batch scoring, edge serving, or streaming for dynamic generative AI. Simplify model serving with automated tools, APIs, and an intuitive interface, enabling efficient predictions and easy maintenance of AI models in production.
import datarobot as dr
project_id = '5506fcd38bd88f5953219da0'
model_id = '5506fcd98bd88f1641a720a3'
model = dr.Model.get(project=project_id,
model_id=model_id)
bp_chart = model.get_model_blueprint_chart()
print(bp_chart.to_graphviz())Gain consistent performance insight. Assess and analyze your agents and the generative and predictive models they rely on, with standard out-of-the-box metrics, real-time performance tracking, and deep drift insights. Ensure that only high-quality agents and models reach production, no matter where they were built.
Every environment. One platform. Most platforms treat deployment flexibility as an afterthought. DataRobot builds for it from the start, architected to run on-premise, air-gapped, sovereign, and hybrid, with no data leaving your environment. That’s not a configuration option. It’s the architecture. And through the Dell integration, it’s a jointly validated, production-ready path to full-stack on-premise AI.
Scale cost-effectively with GPUs. The Agent Workforce Platform is co-engineered with NVIDIA AI Enterprise – a partner and a customer. Purpose-built for inference-heavy agent workloads where GPU performance and cost management matter.
Maximize user adoption. Weave AI into the fabric of your organization to amplify impact and enable AI-driven decision-making across more users. Integrate with your existing business intelligence and chat platforms, putting agentic, generative, and predictive AI applications directly in the hands of your business stakeholders.
Run a shared inference layer, not a pile of endpoints. The intelligent inference gateway manages self-hosted model fleets and third-party models through a single layer. Production-critical traffic stays protected, costs are attributed across teams, and every workload is optimized for cost or latency inside your own environment. Token quotas are enforced per application, agent, or user, allocated like a cloud provider would, but inside your infrastructure.
Reduce infrastructure complexity and costs. Running a workforce of agents on GPU-intensive workloads creates real cost exposure. Get right-sizing recommendations per workload, cost monitoring and attribution across the agent fleet, and resource optimization across mixed compute environments. Every GPU cycle is governed for business value, with budget controls you can delegate to each department.
Achieve greater tool and infrastructure flexibility. Any cloud provider, hardware vendor, or model. Bring your existing stack and run it on your infrastructure, your cloud, your terms.
“When we apply really good predictive AI on top of generative AI, now we get a deep level of precision and a deep level of personalization at scale. Those two things coming together is going to make so many things possible.”
“We didn’t have to build anything special in order to use DataRobot. We just took the data as we had it and were able to feed that to DataRobot and start joining those sources together to come up with models.”
“Our competitors are probably 10 times bigger than us in terms of team size. With the time we save with DataRobot, we now have the opportunity to get ahead of them.”