Model Hosting Documentation
Everything you need to know about deploying and scaling your AI models on Neural Nexus.
Documentation
Hosting Overview
Neural Nexus Hosting Platform
The Neural Nexus hosting platform provides a secure, scalable, and high-performance environment for deploying AI models in production. Our infrastructure is optimized for machine learning workloads, with support for various hardware accelerators including GPUs and TPUs.
Key Features
- Serverless deployment with automatic scaling
- Dedicated instances for consistent performance
- Enterprise-grade security and compliance
- Comprehensive monitoring and analytics
- Global edge deployment for low-latency inference
- Cost optimization tools and recommendations
Hosting Architecture
Frontend Layer
API gateway, load balancing, and request routing for optimal performance.
Compute Layer
Distributed inference servers with GPU/TPU acceleration and automatic scaling.
Management Layer
Monitoring, logging, and analytics for tracking model performance and usage.
Our hosting platform is designed to handle various model types and workloads, from simple inference APIs to complex multi-model systems requiring orchestration and coordination.
Getting Started
Deploying Your First Model
Follow these steps to deploy your first model on the Neural Nexus hosting platform:
1. Prepare Your Model
Package your model according to our format specifications. We support models from all major frameworks including PyTorch, TensorFlow, JAX, and ONNX.
# Example directory structure model/ ├── model.onnx # Your model in ONNX format ├── config.json # Model configuration ├── preprocessor.py # Optional preprocessing code └── requirements.txt # Python dependencies
2. Create a Deployment
Use the dashboard or API to create a new deployment, specifying your model package and desired configuration options.
# Using the Neural Nexus CLI neuralnexus deploy create \ --name "my-first-model" \ --model ./model \ --compute-type gpu-t4 \ --min-replicas 1 \ --max-replicas 5
3. Test Your Deployment
Once your model is deployed, you can test it using the provided API endpoint.
curl -X POST \ https://api.neuralnexus.ai/v1/models/my-first-model/predict \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{"inputs": {"text": "Hello, Neural Nexus!"}}'
4. Monitor Performance
Use the dashboard to monitor your model's performance, including latency, throughput, and error rates.
Quick Tip
Start with our Serverless option for most use cases. It provides automatic scaling and you only pay for what you use. For more consistent workloads, consider dedicated instances.
Deployment Options
Choose Your Deployment Type
Neural Nexus offers multiple deployment options to meet your specific requirements for performance, cost, and control.
Serverless
Auto-scaling serverless deployments with pay-per-use pricing. Ideal for variable workloads.
- ✓Zero cold-start optimization
- ✓Pay only for compute used
- ✓Scales to zero when idle
- ✓Automatic load balancing
Dedicated Instances
Reserved compute resources with consistent performance. Ideal for predictable workloads.
- ✓Guaranteed compute resources
- ✓Predictable monthly pricing
- ✓No cold starts
- ✓Custom hardware configurations
Private Deployment
Deploy in your own infrastructure or VPC for maximum control and compliance.
- ✓Data sovereignty compliance
- ✓Integration with existing systems
- ✓Custom security policies
- ✓Dedicated support team
Hardware Options
Choose from a variety of hardware options to optimize for your specific model requirements.
Hardware Type | Recommended For | Memory | Price |
---|---|---|---|
CPU Standard | Small models, pre/post-processing | 4-16 GB | $0.10/hour |
T4 GPU | Medium-sized models, inference | 16 GB | $0.60/hour |
A100 GPU | Large language models, high throughput | 40-80 GB | $3.00/hour |
TPU v4 | Specialized ML workloads | 32 GB | $2.50/hour |
Deployment Recommendation
For most users, we recommend starting with a Serverless deployment on CPU or T4 GPU, then scaling up or switching to Dedicated Instances as your needs grow. Contact our solution architects for personalized guidance on the optimal setup for your specific use case.
Scaling & Performance
Optimizing Model Performance
Neural Nexus provides multiple tools and techniques to optimize your model's performance and scale efficiently as your traffic grows.
Automatic Scaling
Our platform automatically scales your model based on incoming traffic, ensuring optimal performance while minimizing costs.
Scaling Parameters
- min_replicas: Minimum number of instances (default: 1)
- max_replicas: Maximum number of instances (default: 10)
- target_concurrency: Target requests per replica (default: 30)
- scale_down_delay: Time to wait before scaling down (default: 300s)
Model Optimization Techniques
Improve your model's performance with these optimization techniques:
Quantization
Reduce model size and improve inference speed by converting weights from float32 to int8 or other precision formats. Our platform supports post-training quantization and quantization-aware training.
Pruning
Remove unnecessary weights from your model to reduce size and computational requirements while maintaining accuracy.
KV Caching
For transformer models, our platform automatically implements key-value caching to significantly speed up sequential inference operations.
Batching
Our platform implements dynamic batching to process multiple requests together, significantly improving throughput for high-traffic models.
Load Balancing & High Availability
Neural Nexus automatically distributes traffic across multiple replicas and regions to ensure high availability and performance.
- ✓Global load balancing with automatic failover
- ✓Multi-region deployment options for disaster recovery
- ✓Health checks and automatic instance replacement
- ✓99.9% uptime SLA for enterprise customers
Performance Benchmarking
Use our benchmarking tools to test your model's performance across different hardware configurations and optimization settings. This can help you identify the optimal setup for your specific use case. Access the benchmarking tools through the dashboard or via our API.
Security & Compliance
Enterprise-Grade Security
Neural Nexus implements comprehensive security measures to protect your models and data throughout the deployment lifecycle.
Data Protection
- ✓Encryption at rest and in transit (TLS 1.3, AES-256)
- ✓Private VPC deployments for network isolation
- ✓Customer-managed encryption keys (CMEK)
- ✓Secure model artifact storage
Access Control
- ✓Role-based access control (RBAC)
- ✓Multi-factor authentication (MFA)
- ✓API key rotation and management
- ✓Single Sign-On (SSO) integration
Compliance Certifications
Neural Nexus maintains compliance with industry standards and regulations to support your security and compliance requirements.
SOC 2 Type II
Security & Availability
HIPAA
Healthcare Data
GDPR
Data Privacy
ISO 27001
Information Security
Security Best Practices
Follow these best practices to enhance the security of your deployed models:
- 1.Implement API key rotation on a regular schedule
- 2.Use the principle of least privilege when assigning permissions
- 3.Enable audit logging for all API operations
- 4.Set up alerting for suspicious activities or unusual traffic patterns
- 5.Implement input validation to prevent prompt injection attacks
Data Residency & Sovereignty
For customers with specific data residency requirements, we offer region-specific deployments in North America, Europe, and Asia Pacific. Enterprise customers can also implement custom data handling policies to comply with specific regulatory frameworks.
Monitoring & Analytics
Comprehensive Monitoring
Neural Nexus provides powerful monitoring and analytics tools to help you understand your model's performance, usage patterns, and potential issues.
Performance Metrics
Track key performance indicators for your deployed models:
- •Average response time and p95/p99 latency
- •Requests per second and throughput
- •Memory and GPU utilization
- •Error rates and types
Usage Analytics
Understand how your models are being used:
- •Total requests and tokens processed
- •Usage patterns by time and region
- •API key and endpoint usage breakdown
- •Cost analysis and optimization recommendations
Alerting & Notifications
Set up custom alerts to be notified about important events or issues:
Performance Alerts
- High latency thresholds exceeded
- Error rate spikes
- Resource utilization warnings
Usage Alerts
- Budget thresholds reached
- Unusual traffic patterns
- API key usage anomalies
Alerts can be delivered via email, Slack, webhook, or integrated with your existing monitoring systems.
Logging & Debugging
Comprehensive logging to help you troubleshoot issues:
- ✓Request and response logs with configurable verbosity
- ✓Model execution logs with timing breakdowns
- ✓System logs for infrastructure events
- ✓Log retention and export options
Integration with External Monitoring Tools
Neural Nexus monitoring can be integrated with popular monitoring and observability platforms: