One-click deployment from code to live API

Upload your Python or R code, and UbiOps automatically wraps it into a scalable API endpoint. The platform handles containerisation, load balancing, and request routing. Teams can deploy from a local machine, a notebook, or a CI/CD pipeline.

Business outcome: Reduce model deployment time from weeks to minutes, freeing engineers to focus on model improvements.

Automatic scaling based on real-time demand

UbiOps scales deployments up and down automatically based on incoming request volume. You set minimum and maximum instances, and the platform handles the rest. No idle server costs during low-traffic periods.

Business outcome: Pay only for compute you actually use, with zero manual scaling effort.

Built-in monitoring and logging for every deployment

Every model deployment comes with request logging, latency tracking, error rates, and resource utilisation metrics. Teams can set up alerts for performance degradation or model drift without additional monitoring tools.

Business outcome: Catch production issues early with out-of-the-box observability, reducing mean time to resolution.

Model versioning with instant rollback

Each deployment can have multiple versions running simultaneously. Route a percentage of traffic to a new version for A/B testing, then promote or roll back instantly. All versions are stored and accessible.

Business outcome: Deploy new model versions with confidence, knowing you can revert in seconds if performance drops.

REST API and SDK for seamless integration

Every deployed model gets a unique REST API endpoint. UbiOps also provides Python and R SDKs for programmatic management. This makes it straightforward to connect models to existing applications, dashboards, or data pipelines.

Business outcome: Embed AI predictions into any application or workflow without custom integration code.

Enterprise-grade security and access control

UbiOps offers project-based access control, API key authentication, and encryption in transit and at rest. Audit logs track every action. The platform is GDPR compliant and runs on European cloud infrastructure.

Business outcome: Meet compliance requirements for sensitive data while giving teams the flexibility to deploy models independently.

• Ease of deployment for non-DevOps teamsData scientists can deploy models without learning Docker or Kubernetes, dramatically reducing time-to-production.
• Automatic scaling reduces costsThe platform scales to zero when not in use, so you only pay for compute when models are actually serving requests.
• Built-in monitoring and loggingNo need to set up separate monitoring stacks — request logs, latency metrics, and error rates are available immediately.
• Multi-framework supportWorks with Python, R, TensorFlow, PyTorch, scikit-learn, and custom images, making it flexible for diverse ML stacks.

• Limited control over infrastructureTeams that need custom GPU drivers, specific kernel versions, or bare-metal performance may find the platform too restrictive.
• Not for real-time latency-critical applicationsCold starts and auto-scaling latency mean this is better suited for batch inference or near-real-time use cases.
• Smaller community compared to major cloudsFewer tutorials, community packages, and third-party integrations compared to AWS SageMaker or Google Vertex AI.
• Professional RealityUbiOps is excellent for teams that want to ship models fast without infrastructure headaches, but it is not a replacement for a full MLOps platform with experiment tracking, feature stores, or data versioning built in.

Startup shipping an AI-powered SaaS feature

A startup building a document classification API can deploy their model to UbiOps in minutes, get a REST endpoint, and integrate it into their web app. The auto-scaling keeps costs low during early traction.

Data science team deploying models for internal dashboards

An analytics team builds a demand forecasting model in Python. They deploy it to UbiOps and connect it to their Tableau dashboard, refreshing predictions daily without manual intervention.

Enterprise standardising model deployment across business units

A large organisation uses UbiOps as a central platform where different teams deploy models for fraud detection, churn prediction, and pricing optimisation — all with consistent monitoring and access control.

Consultancy delivering AI solutions to clients

An AI consultancy deploys custom models for each client on UbiOps, giving clients a secure API endpoint without exposing the underlying infrastructure or code.

Sign up for a free UbiOps account and create a new project.

Write or upload your Python/R model code — include a predict function that accepts input and returns output.

Configure your deployment (choose instance size, set scaling limits, add environment variables).

Click deploy, wait 2-3 minutes for the build, then test your live API endpoint with sample requests.