AI Model Serving
Deploy, version, and serve machine learning models via scalable inference APIs
AI model serving module that provides production-grade infrastructure for deploying trained ML models as REST APIs — with model versioning, canary deployments, A/B testing, auto-scaling, and performance monitoring for any framework including PyTorch, TensorFlow, and ONNX.
Features
What's Included
Model Inference API
One-click deployment of trained models as versioned REST endpoints with automatic request batching, input validation, and JSON/binary response formats.
Model Version Management
Track model lineage with version history, training metadata, accuracy metrics, and rollback capability — never lose a model artifact.
Canary & A/B Deployments
Route a percentage of traffic to new model versions for controlled rollout — compare accuracy, latency, and error rates before promoting to full production.
Auto-Scaling Infrastructure
Automatically scales inference workers based on request queue depth and GPU utilization — from zero replicas during idle to dozens during peak load.
Performance Monitoring
Real-time dashboards tracking inference latency (p50/p95/p99), throughput, error rates, and GPU memory utilization per model endpoint.
Multi-Framework Support
Serves models from PyTorch, TensorFlow, ONNX, scikit-learn, and custom Python — with containerized isolation ensuring dependency compatibility.
Plans
Feature Comparison
See what's included at every level — each tier builds on the previous one.
| Feature | Basic | Advanced | Expert | Enterprise |
|---|---|---|---|---|
| Single model REST API deployment | ||||
| Basic model upload and versioning | ||||
| Request logging and error tracking | ||||
| Web-based model management console | ||||
| Multi-model concurrent serving | — | |||
| A/B testing with traffic splitting | — | |||
| Auto-scaling (CPU-based) | — | |||
| Webhook notifications on deployment | — | |||
| Canary deployments with auto-rollback | — | — | ||
| GPU-accelerated inference | — | — | ||
| Performance monitoring dashboard (p95 latency) | — | — | ||
| Custom pre/post-processing pipelines | — | — | ||
| On-premise GPU cluster deployment | — | — | — | |
| Multi-tenant model isolation | — | — | — | |
| SLA-backed latency guarantees | — | — | — | |
| Air-gapped environment support | — | — | — |
Basic
4 features- Single model REST API deployment
- Basic model upload and versioning
- Request logging and error tracking
- Web-based model management console
- — Multi-model concurrent serving
- — A/B testing with traffic splitting
- — Auto-scaling (CPU-based)
- — Webhook notifications on deployment
- — Canary deployments with auto-rollback
- — GPU-accelerated inference
- — Performance monitoring dashboard (p95 latency)
- — Custom pre/post-processing pipelines
- — On-premise GPU cluster deployment
- — Multi-tenant model isolation
- — SLA-backed latency guarantees
- — Air-gapped environment support
Advanced
8 features- Single model REST API deployment
- Basic model upload and versioning
- Request logging and error tracking
- Web-based model management console
- Multi-model concurrent serving
- A/B testing with traffic splitting
- Auto-scaling (CPU-based)
- Webhook notifications on deployment
- — Canary deployments with auto-rollback
- — GPU-accelerated inference
- — Performance monitoring dashboard (p95 latency)
- — Custom pre/post-processing pipelines
- — On-premise GPU cluster deployment
- — Multi-tenant model isolation
- — SLA-backed latency guarantees
- — Air-gapped environment support
Expert
12 features- Single model REST API deployment
- Basic model upload and versioning
- Request logging and error tracking
- Web-based model management console
- Multi-model concurrent serving
- A/B testing with traffic splitting
- Auto-scaling (CPU-based)
- Webhook notifications on deployment
- Canary deployments with auto-rollback
- GPU-accelerated inference
- Performance monitoring dashboard (p95 latency)
- Custom pre/post-processing pipelines
- — On-premise GPU cluster deployment
- — Multi-tenant model isolation
- — SLA-backed latency guarantees
- — Air-gapped environment support
Enterprise
16 features- Single model REST API deployment
- Basic model upload and versioning
- Request logging and error tracking
- Web-based model management console
- Multi-model concurrent serving
- A/B testing with traffic splitting
- Auto-scaling (CPU-based)
- Webhook notifications on deployment
- Canary deployments with auto-rollback
- GPU-accelerated inference
- Performance monitoring dashboard (p95 latency)
- Custom pre/post-processing pipelines
- On-premise GPU cluster deployment
- Multi-tenant model isolation
- SLA-backed latency guarantees
- Air-gapped environment support
Use Cases
Where This Module Fits
Production ML model deployment for SaaS platforms
NLP model hosting for chatbots and text analysis
Technology
Built With
Production-grade technologies trusted by enterprises worldwide.
Related Modules
Works Well With
AI Object Detection
General-purpose computer vision with custom model training and video stream analysis
On-Premise AI Infrastructure
GPU hardware consulting, open-source model hosting, and on-prem AI deployment
Dashboard & Analytics Builder
Drag-and-drop dashboard with charts, KPIs, real-time widgets, and role-based views
Have a project in mind?
Let's discuss how we can build a custom solution tailored to your needs.
Get a Free Consultation