Understanding ML Inference Costs
Deploying machine learning models for inference is often more expensive than training them, especially at scale. Understanding the cost factors and deployment options is crucial for building cost-effective ML systems. This guide helps you navigate inference costs and choose the right infrastructure for your needs.
Inference costs depend on many factors: model size, latency requirements, traffic patterns, and infrastructure choices. Whether you choose serverless, dedicated instances, or a hybrid approach significantly impacts both cost and performance.
Deployment Options Compared
Serverless Inference
Serverless platforms like AWS Lambda, Google Cloud Functions, or specialized ML platforms handle infrastructure automatically:
- AWS SageMaker Serverless: Pay per inference, automatic scaling
- Google Cloud Run: Container-based, scales to zero
- Azure Container Instances: On-demand containers
- Replicate/Banana: GPU serverless for ML models
Pros:
- No infrastructure management
- Automatic scaling
- Pay only for what you use
- Scale to zero during idle periods
Cons:
- Cold start latency
- Higher per-request cost at scale
- Limited customization
- Model size restrictions
Dedicated Infrastructure
Running models on dedicated instances provides consistent performance:
- EC2/GCE/Azure VMs: Full control, various GPU options
- Kubernetes: Orchestrated container deployment
- Managed Services: SageMaker Endpoints, Vertex AI
Pros:
- Consistent latency
- Lower cost at high volumes
- Full customization
- No cold starts
Cons:
- Pay for idle capacity
- Manual scaling configuration
- Operational overhead
GPU vs CPU Inference
When to Use GPU
- Large neural networks (transformers, CNNs)
- Batch processing with high throughput needs
- Real-time latency requirements for complex models
- Models with GPU-optimized operations
When to Use CPU
- Small models (<100MB)
- Low traffic (<10 requests/second)
- Cost-sensitive applications
- Models optimized for CPU (ONNX, quantized)
Cost Optimization Strategies
1. Model Optimization
- Quantization: Reduce model size by 4x with INT8
- Pruning: Remove unnecessary weights
- Distillation: Train smaller models to mimic larger ones
- ONNX Runtime: Optimize for faster inference
2. Smart Autoscaling
- Scale based on request queue length, not just CPU
- Use predictive scaling for known traffic patterns
- Implement scale-to-zero for development environments
- Set appropriate min/max instance counts
3. Request Batching
Group multiple requests for efficient GPU utilization. Batching can improve throughput by 5-10x while reducing per-request cost.
4. Caching
- Cache frequent predictions
- Use embedding caches for retrieval models
- Implement feature caches for preprocessing
5. Spot/Preemptible Instances
Use spot instances for non-critical inference workloads or as overflow capacity during peak traffic.
Pricing Reference
| Service | Type | Approx. Cost |
|---|---|---|
| AWS SageMaker Serverless | Serverless | $0.20/1K requests |
| AWS Lambda + API Gateway | Serverless | $0.05/1K requests |
| Google Cloud Run | Serverless | $0.04/1K requests |
| EC2 c6i.xlarge (CPU) | Dedicated | $0.17/hour |
| EC2 g5.xlarge (GPU) | Dedicated | $1.01/hour |
| Replicate (GPU) | Serverless GPU | $0.0023/second |
Choosing the Right Architecture
Low Volume (<1M requests/month)
Use serverless for simplicity and cost-effectiveness. Cold start latency is acceptable for most use cases at this scale.
Medium Volume (1M-100M requests/month)
Consider hybrid approach: dedicated baseline capacity with serverless overflow for traffic spikes.
High Volume (>100M requests/month)
Dedicated infrastructure typically wins. Focus on autoscaling optimization and efficient instance utilization.
Conclusion
ML inference costs can vary by orders of magnitude based on architecture choices. Use our ML Inference Cost Calculator to estimate costs for your specific workload and compare serverless vs dedicated options. Remember to factor in development and operational costs, not just infrastructure pricing.