proxiML now offers a dedicated LLM job type that lets you deploy pre-configured large language models as managed inference endpoints in minutes. Select a model family and size from the platform and get an OpenAI-compatible endpoint with no custom serving commands, Docker images, or manual checkpoint setup required. Currently supported families include Gemma 4, Qwen 3.5, and Qwen 3.6.

proxiML now supports Endpoint Authorizers, letting you control access to your deployed ML serving endpoints. Choose between API Key authentication for simple shared-secret access or OIDC (OpenID Connect) for enterprise-grade JWT-based authentication with configurable issuers, audiences, and required claims.

By default, uploaded artifacts use the <job_name>.zip convention (or <job_name>_<worker_number>.zip for multi-worker jobs), as described in the job form data section. You can now override those default artifact names (including per-worker names where applicable) from the job form and from the SDK via output_options.

The local connection capability has been redesigned for clearer workflows: connecting to jobs, datasets, checkpoints, and models that use Local storage or runtime access from your machine is now easier to discover and operate from the CLI and web UI.

CloudBender clean room stacks can now be managed through a clearer version upgrade workflow, so you can adopt platform fixes and capabilities in your CloudBender nodes on a controlled schedule.

New job environments based on Python 3.13 are now available for supported frameworks.

You can now populate datasets from the Hugging Face Hub using the same Hugging Face third-party key you may already use for checkpoints from Hugging Face models.

proxiML now supports Azure as a CloudBender provider. You can provision and run proxiML Clean Rooms in your Azure regions alongside existing provider options.

Customers with GCP CloudBender regions can now configure datastores that directly mount Google Cloud Storage buckets for regional workload use.

Customers with AWS CloudBender regions can now configure datastores that directly mount S3 buckets for regional workload use.