Deploy models as APIs
Serve AI and machine-learning models as services through managed APIs, with version management and rollback to support model updates.
Model serving and orchestration for AI teams deploying workloads across cloud and private infrastructure.
UbiOps is an AI model-serving and orchestration platform for deploying and operating machine-learning workloads. It provides a central control plane for running models and workflows across local, on-premises, private-cloud, public-cloud, hybrid, and multi-cloud environments, so teams can manage deployments without directly operating Kubernetes infrastructure.
The platform supports the path from model deployment to production application: models can be exposed as APIs, grouped into modular workflows, and managed with versioning, scaling, monitoring, and access controls. UbiOps is offered as a hosted cloud service connected to a chosen compute environment, or as a privately installed and managed deployment.
Serve AI and machine-learning models as services through managed APIs, with version management and rollback to support model updates.
Connect local, cloud, and private infrastructure into a shared pool of compute resources; the control plane coordinates workloads and capacity.
Combine deployments into pipelines with a separate API for each workflow. Workflow components run as isolated services and can scale independently; pipelines support conditional logic and reusable sub-pipelines.
Use automated scaling, traffic load balancing, and scale-to-zero for model inference, while allocating compute for longer-running training jobs.
Review logs, usage and performance metrics, configure alerts, and use auditing, permissions, and access controls to manage deployments and workflows.
Deploy foundation models and private LLM services, with support described for streaming, concurrency, retrieval-augmented generation (RAG), fine-tuning, and agents.
Deploy vision models as services for applications that need responsive inference and changing compute demand, including workloads that use GPUs.
Run generative AI models on selected private, on-premises, or hybrid infrastructure when teams need to control where data is processed.
Create pipelines that route data through multiple model deployments, add conditional logic, and combine reusable components behind a workflow API.
Give AI and IT teams a managed path to deploy, version, monitor, and scale models as operational services rather than maintaining deployment infrastructure themselves.
UbiOps describes deployments across local, on-premises, public-cloud, private-cloud, hybrid, and multi-cloud environments. It can orchestrate workloads across Kubernetes, virtual machines, and bare-metal infrastructure.
UbiOps Cloud is hosted by UbiOps and can connect to a selected compute environment. UbiOps Private is installed and managed in the customer's private environment.
Yes. Models can be deployed as inference APIs, and each pipeline workflow receives its own API.
Yes. The product describes deployment of foundation models and private LLM services, with functionality for streaming, concurrency, RAG systems, fine-tuning, and agents.
The pricing page describes UbiOps Cloud with on-demand compute billed per use. It does not publish a specific price in the supplied page text; UbiOps Private is presented through an expert contact flow.
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