7 methods to deploy a custom large language model

As artificial intelligence continues to reshape the business landscape, organizations face a critical decision: how to effectively deploy Large Language Models (LLMs) into their operations. From simple chatbot implementations to sophisticated custom model development, the spectrum of deployment options has grown significantly in recent years. Whether you’re a small startup taking your first steps into AI or an enterprise looking to expand your existing capabilities, understanding these deployment methods is crucial for making informed decisions about your AI strategy. This comprehensive guide explores seven key approaches to LLM deployment, helping you navigate the trade-offs between complexity, cost, and capability to find the solution that best fits your organization’s needs.

1. Chatbots

• • Represents the easiest entry point into generative AI implementation
  • Available as both free public options and enterprise-grade solutions
  • Currently utilized by 96% of organizations implementing generative AI
  • Best for: Organizations looking to start with minimal technical overhead

2. API Integration

• • Involves adding LLM functionality to existing corporate platforms via APIs
  • Offers a low-risk, cost-effective approach to implementing generative AI features
  • Requires minimal technical expertise while providing robust functionality
  • Best for: Companies wanting to enhance existing systems with AI capabilities

3. Vector Databases with RAG (Retrieval Augmented Generation)

• • Currently the most widely adopted method for LLM customization
  • Uses vector databases to provide relevant context for user queries
  • Combines the power of LLMs with organization-specific knowledge
  • Best for: Organizations needing to leverage their proprietary data

4. Local Open Source Model Deployment

• • Involves running open source LLMs like Meta’s Llama locally
  • Provides greater control over data privacy and processing
  • Requires more technical expertise and computational resources
  • Best for: Organizations with strict data privacy requirements

5. Fine-Tuning Existing Models

• • Adapts pre-trained LLMs with additional data for specific use cases
  • Particularly effective for customer service applications
  • Requires significant domain-specific training data
  • Best for: Companies with unique use cases requiring specialized responses

6. Building Custom Models

• • Represents the most complex and costly approach
  • Example: GPT-3 cost $4.6 million to train, GPT-4 exceeded $100 million
  • Rarely implemented due to extensive resource requirements
  • Best for: Large organizations with unique needs and substantial resources

7. Model Gardens

• • Involves maintaining multiple curated models for different use cases
  • Suitable for organizations with mature AI operations
  • Requires sophisticated model management and governance
  • Best for: Advanced enterprises with diverse AI applications