Should we use managed AI services like AWS Bedrock or build our own LLM infrastructure on EC2?

All answers to this question.

In 2025, unless you are a specialized AI company, hosting your own LLM is a massive waste of resources. The overhead of managing GPU clusters, handling scaling during peak hours, and updating the models is immense. Managed services like Bedrock provide "Serverless" AI, meaning you only pay for the tokens you use. You get enterprise-grade security and the ability to swap models (Claude, Titan, Llama) with a single API change. Focus on building features for your users, not on being an infrastructure provider.

   Answered 2025-06-12 by Sandra Mendez

Sandra, what about data privacy? If we use a managed service, is there a risk that the provider will use our proprietary company data to train their next-generation models?

   Answered 2025-06-14 by Jeffrey King

• Jeffrey, that’s a common misconception. Enterprise versions of these services explicitly state that your data is not used to train the base models. Your data remains in your VPC. If you are worried, you can use "VPC Endpoints" to ensure the data never even touches the public internet while communicating with the AI service. The privacy controls in 2024 are much more robust than they were a year ago.

     Commented 2025-06-15 by Sandra Mendez

Managed services are great for MVP, but keep an eye on your "Token" costs. If your app becomes a viral hit, those per-token fees can suddenly become much higher than a flat-rate GPU instance.

   Answered 2025-06-17 by Maria Lopez

• Great point, Maria. It's always a balance between speed to market and long-term cost. Starting managed and migrating to self-hosted later is a valid strategy once you prove the use case.

     Commented 2025-06-18 by Ryan Miller