Most companies use publicly available APIs to incorporate LLMs into their own products. While this is not a bad approach, especially if you're just starting with LLMs, it also has some drawbacks.

Below is a concise summary of reasons why you might consider training, fine-tuning and deploying your own LLM.

1. Better Performance

• Consistent outputs
• Stop hallucinating
• Stop suggesting your competitors

2. Data Privacy

• Data stays inside VPC or on premise
• Stop data leaking to competitors
• No 3rd party data breaches

3. Cost

• Lower cost per API hit
• Control cost
• Cost transparency

4. Latency

• Lower latency
• Higher throughput
• Control over latency

5. Uptime

• Control uptime
• Stop fighting for usage with others
• Avoid 3rd party outages

6. Ownership

• Your engineering team builds it
• No recurring professional services
• Build Al moat & in-house know-how

7. Flexibility

• Control LLM providers
• Choice over open-source LLMs
• Adapt LLM stack to custom needs

8. Bias

• Control biases with your own data
• Unlearn biases with unbiased data

9. Content Control

• Stop inappropriate content
• Stop unlicensed content
• Your own data-data transparency

You can find the original blog post here.