When it comes to deploying and using Large Language Models (LLMs), you have two main options: using API services or running inference locally. Each approach has its own advantages and trade-offs that we’ll explore in this chapter.
API-based Inference
API-based inference involves making HTTP requests to a service that hosts the model. Popular examples include OpenAI’s GPT API, Anthropic’s Claude API, and Hugging Face’s Inference Endpoints.
Advantages of API-based Inference
No Infrastructure Management
No need to manage hardware or model deployments
Automatic scaling and load balancing
Regular model updates and improvements
Cost-effective for Low Volume
Pay-per-use pricing
No upfront hardware costs
No maintenance overhead
Reliability and Availability
High uptime guarantees
Professional monitoring and support
Redundancy and failover handling
Disadvantages of API-based Inference
Cost at Scale
Can become expensive with high volume
Pricing typically per token or request
Additional costs for data transfer
Limited Control
Fixed model configurations
Limited customization options
Dependent on provider’s availability
Data Privacy Concerns
Data leaves your infrastructure
Compliance challenges
Vendor lock-in
Local Inference
Local inference involves running the model on your own infrastructure, whether it’s on-premises hardware or cloud instances you control.
Advantages of Local Inference
Complete Control
Full customization of model parameters
Ability to fine-tune and modify models
Control over hardware optimization
Data Privacy
Data stays within your infrastructure
Easier compliance with regulations
No dependency on external services
Cost-effective at Scale
Fixed infrastructure costs
No per-token charges
Better economics for high volume
Disadvantages of Local Inference
Infrastructure Management
Need to manage hardware resources
Responsibility for scaling and reliability
Technical expertise required
Upfront Costs
Hardware investment needed
Setup and maintenance time
Operational overhead
Limited Model Access
Not all models are open source
May need to use smaller models
Manual updates and improvements
Making the Choice
Consider these factors when deciding between API and local inference:
Volume and Scale
Low volume: APIs are often more cost-effective
High volume: Local inference may be cheaper long-term
Technical Resources
Limited expertise: APIs are easier to implement
Strong ML team: Local inference offers more control
Data Privacy
Sensitive data: Local inference provides better control
Public data: APIs may be sufficient
Performance Requirements
Low latency: Local inference can be optimized
Flexible latency: APIs work well
Hybrid Approaches
Many organizations adopt a hybrid approach:
Development vs Production
Use APIs for development and testing
Deploy locally for production workloads
Task-based Selection
APIs for non-sensitive, low-volume tasks
Local inference for sensitive or high-volume tasks