Managed SaaS releases¶
May SaaS feature announcements¶
May 2026
This page provides announcements of newly released features available in DataRobot's SaaS multi-tenant AI Platform, with links to additional resources. From the release center, you can also access past announcements and Self-Managed AI Platform release notes.
Agentic AI¶
OpenAI API-compatible embedding model connections¶
When creating vector databases, as an expansion of the “BYO embedding model" capabilities, you can now connect to a deployed OpenAI API-compatible embedding model directly from DataRobot. This allows you to access your own OpenAI API-compatible model deployment without creating a proxy custom model that redirects requests. Optionally, you can pass additional parameters in the request body when calling an API, during vector database creation, a query, or both.
New and retired LLMs¶
Following is the list of LLM availability changes since v11.8.0. See the availability page for a full list of supported LLMs. As always, you can add an external integration to support specific organizational needs.
The following are newly available:
- Claude Opus 4.7: Amazon Bedrock, Anthropic, Google Gemini Enterprise Agent Platform (formerly Vertex AI)
- Azure OpenAI GPT-5.4: Azure OpenAI
- Claude Haiku 4.5: Amazon Bedrock, Anthropic, Google Gemini Enterprise Agent Platform (formerly Vertex AI)
The following models on Amazon Bedrock are deprecated and will reach end of service on July 7, 2026:
- Meta Llama 3.1 405B Instruct v1
- Meta Llama 3.2 11B Instruct v1
- Meta Llama 3.2 90B Instruct v1
Also on Amazon Bedrock, the following retirements have been announced:
- Mistral Mistral 7B Instruct v0, retirement date May 29, 2026
The following are retired and removed:
- Google Llama 4 Maverick 17B 128E Instruct MAAS
- Mistral Mixtral 8x7B Instruct v0
Azure service principal and managed identity for the LLM gateway¶
The LLM gateway now supports Microsoft Entra ID service principal and managed identity (including Kubernetes workload identity) as authentication methods for Azure OpenAI model credentials, in addition to API keys. These approaches let you adopt Entra-based access controls and reduce reliance on long-lived API keys stored in DataRobot.
To use a service principal, include tenant_id, client_id, client_secret, and optionally azure_scope alongside the standard api_type, api_base, and api_version fields in each endpoint object of the [GenAI] Azure OpenAI LLM Credentials secure configuration. For managed identity, omit those fields entirely; the gateway reads tokens from the AZURE_TENANT_ID, AZURE_CLIENT_ID, and AZURE_FEDERATED_TOKEN_FILE environment variables injected on the LLM gateway pods. In all cases, assign the Cognitive Services OpenAI User role to the service principal or managed identity on the Azure AI Foundry resource. When multiple credential types are present on a single endpoint, the gateway selects them in this order: API key, then service principal, then managed identity.
For more information, see Azure OpenAI service principal and managed identity in the Generative AI service configuration reference, and the LLM gateway model configuration reference.
MLOps and predictions¶
Prediction environment tags¶
You can now attach optional key-value tags to prediction environments in Console. These tags let teams annotate environments with meaningful metadata—such as team: fraud or region: eu—to make it easier to identify the right infrastructure at a glance.
Once tags are applied, you can filter the Deployments inventory by prediction environment tags using the new Prediction environment tags filter. Autocomplete suggests existing keys and values, and when multiple tag pairs are selected, deployments are included if their prediction environment matches any of them. Note that prediction environment tags are distinct from deployment tags, which describe the deployment itself rather than the environment it runs on.
For more information, see Use prediction environment tags.
Deployment capacity settings¶
Deployment owners can now configure throughput and usage limits on the Capacity tab in deployment settings. Set deployment-wide capacity (requests or tokens per minute) and a utilization threshold that controls when default quota rules tighten as load increases. Optionally reserve capacity for specific agent deployments, users, or groups so entitled consumers keep a guaranteed share when utilization is high.
You can still define default rate limits and per-entity exceptions for requests, tokens, input sequence length, and concurrent requests. Policy changes may take up to five minutes to apply while the gateway refreshes its quota cache.
Runtime parameters improvements¶
This release improves the runtime parameters experience across custom models, custom applications, and custom jobs. Runtime parameters can now be defined directly in the UI, without modifying the metadata.yaml file. In the Runtime parameters section for models, applications, and jobs, you can configure a name, type, value, and optional description. Parameters defined through the UI persist and merge when you upload new code versions.
Parameters are injected as standard environment variables, accessible via os.getenv without the datarobot-drum library. Legacy prefixed (MLOPS_RUNTIME_PARAM_*) and JSONified formats remain supported for backward compatibility.
Automatic log collection for custom models¶
This release implements automatic log collection for custom models without requiring explicit OpenTelemetry (OTEL) instrumentation. Users can now seamlessly capture logs from classical ML and agent-based models.
Data¶
Microsoft OneDrive support¶
DataRobot now features out-of-the-box support for Microsoft OneDrive, allowing you to securely and seamlessly connect to your OneDrive data stores. Designed specifically for unstructured data, this new connector streamlines the process of ingesting OneDrive files directly into DataRobot for vector database creation and GenAI workflows.
For more information, see the OneDrive reference documentation.
Databricks now supports Azure service principal authentication¶
You can now use Azure service principals for authentication when connecting to the Databricks native connector in DataRobot. For information on connecting to Databricks using Azure service principal, see the documentation for the Databricks connector.
Add dynamic datasets from JDBC connections¶
You can now add dynamic datasets from JDBC driver connections, as well as perform Feature Discovery on that data. Note that to view previews or wrangle your data in either Wrangler or the SQL Editor, you must first create a snapshot of the dataset. If you do not create a snapshot, these two options will be disabled in the Actions menu of the dataset.
Faster re-ingestion for unstructured connectors¶
Re-ingesting files from unstructured connections is now significantly faster because DataRobot tracks each file’s external identifier, size, and last-modified timestamp. When the files are ingested again, DataRobot detects unchanged files and reuses their existing storage instead of downloading and processing them again. This change applies to both folder and single-file ingestion (with the exception of ZIP archive files) for most unstructured connectors, with safeguard to keep detection efficient, even on resource-constrained queues—meaning faster refreshes for large datasets and lower loads on connected storage.
Code-first¶
Python client v3.16¶
Python client v3.16 is now generally available. For a complete list of changes introduced in v3.16, see the Python client changelog.
DataRobot REST API v2.45¶
DataRobot's v2.45 for the REST API is now generally available. For a complete list of changes introduced in v2.45, see the REST API changelog.
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