Deploy models as APIs
Serve AI and machine-learning models as services through managed APIs, with version management and rollback to support model updates.
UbiOps
Freemium
Model serving and orchestration for AI teams deploying workloads across cloud and private infrastructure.
O que é UbiOps?
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.
O que UbiOps pode fazer?
Orchestrate across compute environments
Connect local, cloud, and private infrastructure into a shared pool of compute resources; the control plane coordinates workloads and capacity.
Build modular workflows
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.
Scale workloads dynamically
Use automated scaling, traffic load balancing, and scale-to-zero for model inference, while allocating compute for longer-running training jobs.
Monitor and govern deployments
Review logs, usage and performance metrics, configure alerts, and use auditing, permissions, and access controls to manage deployments and workflows.
Run generative AI workloads
Deploy foundation models and private LLM services, with support described for streaming, concurrency, retrieval-augmented generation (RAG), fine-tuning, and agents.
Casos de uso
“Serve computer-vision models”
Deploy vision models as services for applications that need responsive inference and changing compute demand, including workloads that use GPUs.
“Operate private LLM applications”
Run generative AI models on selected private, on-premises, or hybrid infrastructure when teams need to control where data is processed.
“Connect multi-step AI applications”
Create pipelines that route data through multiple model deployments, add conditional logic, and combine reusable components behind a workflow API.
“Move models from pilot to production”
Give AI and IT teams a managed path to deploy, version, monitor, and scale models as operational services rather than maintaining deployment infrastructure themselves.
Perguntas frequentes
Which infrastructure can UbiOps run on?
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.
How is UbiOps offered?
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.
Can a model or workflow be exposed through an API?
Yes. Models can be deployed as inference APIs, and each pipeline workflow receives its own API.
Does UbiOps support generative AI workloads?
Yes. The product describes deployment of foundation models and private LLM services, with functionality for streaming, concurrency, RAG systems, fine-tuning, and agents.
How is pricing handled?
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.
Informações rápidas
- Product category
- AI model serving and orchestration platform
- Primary users
- AI, machine-learning, and IT teams
- Deployment options
- UbiOps Cloud or privately installed UbiOps
- Infrastructure scope
- Local, on-premises, public cloud, private cloud, hybrid, and multi-cloud
- Workflow output
- Model services and pipeline APIs
- Cloud billing
- On-demand compute, billed per use
Análise de tráfego de UbiOps
Os dados de tráfego são apenas para referência.
- Classificação do domínio
- 54
Alternativas ao UbiOps
CoreWeave
www.coreweave.com
CoreWeave is an AI-focused cloud platform that combines GPU infrastructure, storage, networking, orchestration, and operational tooling for training and serving AI workloads. It supports teams moving from model experiments to production systems, including reinforcement-learning and agent-development workflows.
Inference.net
inference.net
Inference.net is infrastructure for running, monitoring, evaluating, training, and deploying production AI workloads. It helps AI-native teams observe model behavior, route traffic across providers, and improve cost, latency, reliability, and quality through connected workflows.
Hyperbolic
hyperbolic.xyz
Hyperbolic is an open-access GPU and AI cloud for deploying on-demand H100, H200, B200, and other GPU capacity. It supports experimentation, training, fine-tuning, inference, and production workloads through on-demand instances, reserved clusters, and Private Cloud infrastructure.
Baseten
baseten.co
Baseten is an inference platform for deploying, serving, and scaling open-source, custom, and fine-tuned AI models. It supports managed cloud, self-hosted, hybrid, and dedicated deployments for teams running production AI workloads.
dstack
dstack.ai
A control plane for provisioning and orchestrating GPU workloads across cloud, Kubernetes, and on-prem infrastructure.
FriendliAI
friendli.ai
FriendliAI is an inference cloud for deploying frontier open-weight and custom AI models in production. It offers serverless Model APIs, dedicated GPU endpoints, and BYOG options for agent, multimodal, and other high-throughput workloads.