Deployo
Deploy trained machine-learning models as scalable APIs on cloud or on-premises infrastructure.
About Deployo
Deployo is an AI infrastructure platform for taking trained machine-learning models into production as scalable APIs. It suits teams looking to reduce deployment scripting and infrastructure work: users can upload their own models, create custom inference endpoints, obtain Swagger endpoint documentation, and use automatic resource allocation and autoscaling. Deployo supports for public-cloud and on-premises environments, with no hard dependency on one provider, and presents compatibility with common model frameworks. Production-oriented capabilities include GPU-optimized performance, network policy management, deployment strategies, analytics, and automatic version compatibility. Deployo has an Early Access Beta User plan at $0. That plan includes model deployments, real-time and advanced analytics, scalable endpoints, and an inference API. Role-based organization management is listed but marked as coming soon, and the homepage also marks parts of its versatility and security offering as coming soon. Teams evaluating Deployo should therefore confirm that the specific cloud, framework, governance, and security capabilities they need are available before depending on them.
Best for
Best use cases
Create a custom inference API from an uploaded model
Scale model endpoints automatically as demand changes
Deploy model serving without locking infrastructure to one cloud
Compare before choosing
Compare required framework support before migrating existing models
Check whether coming-soon governance and security controls meet production deadlines
Evaluate the early-access plan against the support and reliability needed for live workloads
Features
Model-to-API deployment
Turn an uploaded machine-learning model into a live inference API with Swagger endpoint documentation.
Flexible infrastructure
Deploy across public clouds or on-premises virtual machines without a hard dependency on one provider.
Scalable serving
Use automatic resource allocation, autoscaling endpoints, GPU-optimized performance, and deployment strategies for production traffic.
Pros
- The $0 early-access plan lowers the cost of testing model deployment workflows.
- Autoscaling, resource allocation, and GPU optimization reduce manual serving-infrastructure work.
- Cloud-agnostic and on-premises options can limit dependence on a single provider.
Cons
- The public offer is an early-access beta, which adds rollout and reliability uncertainty for production teams.
- Role-based organization management is marked as coming soon, limiting current team-governance workflows.
- Parts of the advertised versatility and security offering are marked as coming soon, so required controls need confirmation.
Pricing Plans
Early Access Beta User
- Upload your own models
- Real-time and advanced analytics
- Autoscaling endpoints
- Custom inference API endpoints
- Role-based organization management (coming soon)
* Pricing information is for reference only. Please refer to the latest announcement on Deployo's official website.