Define policy once. Enforce it everywhere.
Built for agent estates, not individual agents. Manual oversight works for a handful of agents, but it breaks down across an estate of hundreds built by different teams in different environments. Define policies once and propagate them automatically, so governance stays consistent, enforceable, and reviewable no matter how many agents are running or who built them. That’s the difference between governance features and governance infrastructure.
AI governance across across cloud, private cloud, hybrid, on-prem, and edge environments
Your choice of DataRobot UI or code-first
Central hub for models, LLMs, agents, tools, apps, and vector databases
Deploy anywhere from DataRobot
Documentation your compliance team can actually use. Compliance documentation, generated automatically and structured for the regulatory requirements your industry actually faces, not generic output that needs manual interpretation. Financial services teams get audit-ready model governance built around real audit workflows. Federal and defense get air-gapped deployment with security controls and audit trails for classified environments. Life sciences get research traceability and validation documentation for regulated R&D.
AI compliance: EU AI Act, NYC Law No. 144, Colorado Law SB21-169, California Law AB-2013, SB-1047
Standards & guidelines: EEOC AI Guidance, DIU Responsible AI Guidelines
AI management frameworks: NIST AI RMF, SR-117
Industry best practices such as The Data & Trust Alliance
Not just AI governance. Every layer, one pane of glass. AI governance alone is not enough at enterprise scale. Infrastructure governance, IT governance, and agentic governance come together in a single system, so security posture is visible to every stakeholder from the CISO to the department lead. That’s the full spectrum CIOs and CISOs require, managed across tens of departments and thousands of agents while keeping control centralized.
Real-time intervention and moderation. Protect your AI deployments from OWASP-identified vulnerabilities like PII leakage, prompt injection, and hallucinations using a full library of shields and guards. Tap into a suite of ready-to-use and customizable guards from NVIDIA, Microsoft, and others to monitor, detect, and resolve threats – before they impact your business.
PII leakage, privacy infringement
Veering off-topic, hallucinations, off-policy
Toxicity, bias, disinformation
Rouge-1, faithfulness
Protect any AI asset, built anywhere by anyone. Secure and govern any AI asset—models, agents, or applications—no matter where it’s hosted, with just two lines of code. Apply real-time monitoring and moderation with comprehensive governance policies and custom metrics. Automate these defenses to ensure robust protection across all your deployments.
Pre-deployment AI red-teaming. Ensure your agents and models are robust and secure by red-teaming your AI before deployment. Test with synthetic or custom datasets to spot jailbreaks, bias, inaccuracies, toxicity, and compliance issues to identify and address vulnerabilities early. Once in production, maintain protection with a broad library of guards for ongoing security.
Custom alerts and notification policies. Get real-time metrics and customized alerts in your data science applications or SIEM tools so the right teams can act fast.
Reduce deployment complexity. Deploy predictive and generative AI models, agents, and apps faster from a single organized registry. Automatically package pipelines, vector databases, and prompting strategies into a production-ready REST API with one click.
Cloud deployment
Edge Deployment
Embed into business applications
On-prem and air-gapped
Optimize compute resources in real time. Maximize performance and cost efficiency with a serverless environment that automates compute provisioning and deallocation to eliminate waste. Simply set priorities like cost, latency, or availability, and skip the DevOps overhead. Accelerate vector database updates, improve guard performance, and reduce latency without manual adjustments.
gpu_ex = cc.CloudExecutor(num_cpus = 124,
num_gpus = 4,
gpu_type = "h100")
@ct.electron(executor = gpu)
def fine_tune_llm(**data):
# fine tune model with requested GPU
...
return modelDeploy agents with NVIDIA. Skip the setup. Eliminate manual complexity and rapidly deploy pre-configured and GPU-optimized NVIDIA NIM using the new NVIDIA NIM Gallery. Customize any DataRobot agentic app to leverage NIM microservices, and secure all your AI apps with NeMo guardrails.
# Create prediction environment for the Agent LLM and tool deployments
prediction_environment = datarobot.PredictionEnvironment(
resource_name="TTMD serverless prediction environment",
platform="datarobotServerless",
)
# Retrieve Llama NIM 3.3 70B model from DataRobot Model Directory
registered_model = datarobot.RegisteredModel.get(
resource_name="NIM - Llama 3.3 70B",
id="6810f0b32e1c63a55912350a",
)
# Create Llama 3.3 70B NIM deployment
deployment = datarobot.Deployment(
resource_name="NIM - Llama 3.3 70B",
registered_model_version_id=registered_model.version_id,
prediction_environment_id=prediction_environment.id,
label="NIM - Llama 3.3 70B",
use_case_ids=[use_case.id],
)Orchestrate AI pipelines at scale. Whether you’re running batch jobs or real-time apps, ensure uninterrupted performance at every stage of the pipeline. Use native Airflow integration to run and monitor batch workflows. Rapidly set up real-time workflows using pre-built customizable blueprints and optimize using dynamic compute orchestration.
@dag(
start_date=datetime(2025, 4, 22),
schedule="@daily",
max_active_runs=1,
tags=["example", "csv", "wrangling", "modeling"],
params={
"dataset_file_path":
"https://s3.amazonaws.com/datarobot_public_datasets/10k_diabetes.csv",
"project_name": "hospital-readmissions-example",
"autopilot_settings": {"target": "readmitted", "mode": "quick",
"max_wait": 3600},
},
)
“DataRobot gives us reassurance that we are accessing generative AI through a well-governed and secure environment.”
“Our ability to leverage data science to help us identify disparities, remove barriers, and enable informed decisions from our data, which has been exploding in terms of variety, volume, and velocity over the years, has been made much easier with DataRobot.”