This text discusses the comparison between self-hosted LLMs and OpenAI in terms of cost, text generation quality, development speed, privacy, and control. It highlights the importance of considering factors such as cost, deployment requirements, and extra expenses when using self-hosted LLMs. The cost of generating text using the OpenAI API is also calculated. The text emphasizes the difference in quality between open-source models and GPT-3.5 and GPT-4, and suggests using the OpenAI API for optimal results. It also mentions the advantages of using the OpenAI API for quick prototyping and testing, as well as the privacy concerns associated with using external APIs. The importance of control and customization is discussed, and the text concludes by suggesting a combination of approaches using both self-hosted models and the OpenAI API.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Comparison of self-hosted LLMs and OpenAI | Evaluation of cost, quality, speed, privacy | Improved models, cost efficiency | Advancements in AI technology, market competition |
| Use of OpenAI API vs deploying own model | Cost, convenience, control | Increased adoption of OpenAI API | Simplified development process, cost-effectiveness |
| Importance of considering costs and expenses | Cost analysis | Improved cost optimization | Financial efficiency, resource allocation |
| Quality difference between open-source models and GPT-3.5 and GPT-4 | Improved model quality | Higher model accuracy, community support | Community involvement, technological advancements |
| Time to market considerations | Development speed | Faster deployment, reduced complexity | Rapid prototyping, hypothesis testing |
| Privacy concerns | Data privacy, control | Increased emphasis on self-hosted LLMs | Data security, compliance requirements |
| Control considerations | System control, dependency management | Enhanced control and transparency | Reliability, customization requirements |
| No clear-cut answer on best approach | Decision-making process | Customized utilization of LLMs | Specific needs, resources, priorities |
| Combination of approaches | Hybrid approach | Enhanced functionality and flexibility | Customization, performance optimization |