Deployment

Deploy Models
To Production

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

99.9%
Uptime SLA
<100ms
Latency P95
0→∞
Auto-scaling
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 ARCHITECTUREManaged infrastructure
API GatewayLoad balancer + Auth
HTTPSAPI KeysRate Limiting
Inference ServersAuto-scaled replicas
replica-1
replica-2
replica-3
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 MODELPython SDK
# 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 INFERENCEPython SDK
# 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 APIcURL
# 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
Replicas
Traffic
1
06:00
3
09:00
6
12:00
4
15:00
2
18:00
1
22:00
Min: 1 replicaMax: 6 replicasCost optimized
1-10
Replica range
<30s
Scale-up time
70%
CPU threshold
Auto-Scaling

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.