Deploy Models To Production

Ship computer vision models to serverless infrastructure. Auto-scaling, zero cold starts, and full observability out of the box.

Uptime SLA

99.9%

Latency P95

<100ms

Auto-scaling

0→∞

Deploy Your First Model Documentation

Infrastructure

Serverless model serving

Deploy models without managing servers. Picsellia handles container orchestration, GPU allocation, load balancing, and auto-scaling automatically.

GPU & CPU Inference

Choose the right compute for your model — from T4 GPUs to cost-efficient CPU instances.

Container Orchestration

Automatic containerization with optimized runtimes for ONNX, TensorRT, and PyTorch.

Secure Endpoints

API key authentication, rate limiting, and encrypted traffic by default.

Deployment Architecture

  • Managed infrastructure
  • API Gateway
  • Load balancer + Auth
  • HTTPS
  • API Keys
  • Rate Limiting
  • Inference Servers
  • Auto-scaled replicas

Model Registry

  • Versioned artifacts
  • Monitoring
  • Predictions logged

Developer Experience

Deploy in a few lines of code

Use the Python SDK to deploy, update, and manage models programmatically. Full API access for CI/CD integration.

Deploy A Model

# Connect and get deployment
from picsellia import Client

client = Client()

# Create deployment with model
deployment = client.create_deployment(
  name="prod-v3"
)
deployment.set_model(model_version)

Run Inference

# Run prediction from file path
result = deployment.predict(
  "image.jpg"
)

# Run prediction from bytes
result = deployment.predict_bytes(
  "image.jpg",
  raw_image
)

# Send to monitoring
deployment.monitor("image.jpg")

REST API

# Direct API call
curl -X POST "https://serving.picsellia.com/v1/predict" \
  -H "Authorization: Bearer $API_KEY" \
  -F "image=@photo.jpg" \
  -F "deployment_id=dep_abc123"

Auto-Scaling

Active

Replica count over 24h

Time Replicas
06:00 1
09:00 3
12:00 6
15:00 4
18:00 2
22:00 1
  • Min: 1 replica
  • Max: 6 replicas
  • Cost optimized

Scale to match demand

Automatically scale from zero to thousands of requests per second. Pay only for the compute you use, with intelligent scaling policies.

  • Scale-to-zero for cost efficiency
  • CPU and request-based scaling policies
  • Configurable min/max replica bounds
  • Cold-start optimization with warm pools
  • Multi-region deployment support

Built for Production

Everything you need to serve models

From model registry to production endpoint, Picsellia handles the entire deployment lifecycle with enterprise-grade reliability.

Model Registry Integration

  • Deploy any model version from your registry.
  • Full lineage from experiment to production endpoint.
  • Version management
  • Artifact tracking
  • Rollback support

Runtime Optimization

  • Automatic model optimization with ONNX Runtime, TensorRT, or custom serving containers.
  • ONNX Runtime
  • TensorRT acceleration
  • Custom containers

Monitoring Built-In

Every prediction is logged. Track latency, throughput, and anomalies from day one.

  • Real-time dashboards
  • Anomaly detection
  • Drift tracking

Ready to deploy your models?

Go from trained model to production endpoint in minutes. Serverless, scalable, and fully managed.

Start Free Trial Request Demo