Model Deployment Architectures #

This pattern explores different approaches to deploying foundation models in production environments.

Pattern Description #

Model deployment architectures define how foundation models are hosted, scaled, and made accessible to applications. The right architecture depends on your performance requirements, budget constraints, and operational capabilities.

Architecture Options #

1. Cloud API Integration #

Description: Applications connect directly to model providers’ APIs (OpenAI, Anthropic, etc.)

Advantages:

• No model infrastructure to manage
• Automatic scaling
• Always updated to latest models
• Quick implementation

Disadvantages:

• Data leaves your environment
• Higher variable costs at scale
• Limited customization
• Dependency on third-party availability

When to use:

• Rapid prototyping
• Teams with limited ML infrastructure experience
• Variable or unpredictable workloads
• Cases where latest model capabilities are critical

2. Self-Hosted Single Instance #

Description: Deploy open-source models on a single high-powered server or instance.

Advantages:

• Complete data control
• Fixed infrastructure costs
• No per-token fees
• Customizable configuration

Disadvantages:

• Limited to hardware capacity
• Single point of failure
• Requires ML operations expertise
• May struggle with peak loads

When to use:

• Consistent, predictable workloads
• Stringent data privacy requirements
• Budget constraints at higher scales
• Specialized tuning needs

3. Self-Hosted Distributed Inference #

Description: Deploy models across multiple machines using frameworks like vLLM, TGI, or Ray.

Advantages:

• Horizontal scalability
• High availability
• Can handle larger batch sizes
• More efficient resource utilization

Disadvantages:

• Complex setup and maintenance
• Requires specialized expertise
• Higher fixed infrastructure costs

When to use:

• High throughput requirements
• Need for high availability
• Large-scale production deployments

4. Hybrid Approach #

Description: Use a combination of cloud APIs and self-hosted models based on request characteristics.

Advantages:

• Cost optimization
• Flexibility to route by importance/complexity
• Graceful fallback options
• Best of both worlds

Disadvantages:

• More complex implementation
• Requires maintaining multiple integrations
• Potential consistency issues across models

When to use:

• Mixed workloads with varying importance
• Cost-sensitive applications with occasional need for premium capabilities
• Organizations transitioning between approaches

Implementation Considerations #

Containerization #

Using Docker containers for model deployment provides consistency across environments and simplifies scaling.

FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime

RUN pip install transformers accelerate vllm

COPY app.py /app/
COPY models /app/models

EXPOSE 8000

CMD ["python", "/app/app.py"]

Autoscaling #

For cloud deployments, consider implementing autoscaling based on queue depth or inference latency:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: llm-inference
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: llm-inference
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: External
    external:
      metric:
        name: sqs_queue_length
      target:
        type: AverageValue
        averageValue: 10

Related Patterns #