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.
什么是 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.
UbiOps 能做什么？
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.
使用场景
“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.
常见问题
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.
快速信息
- 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
UbiOps 流量分析
流量数据仅供参考。
- 域名评分
- 54
UbiOps 替代品
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Chutes
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Chutes is a decentralized, serverless AI inference platform for deploying, running, and scaling open-source models. Developers can access hosted models through an API or deploy private custom workloads on confidential GPU capacity.
SGLang
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SGLang is an open-source serving framework for large language and multimodal models. It helps developers deploy model servers and query them through OpenAI-compatible endpoints across supported GPU, CPU, TPU, NPU, and XPU platforms.
Fireworks AI
fireworks.ai
A model training and inference platform for teams building and serving specialized AI.