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Building GenAI solutions on Amazon EKS
- Self-hosting AI solutions on Amazon EKS provides more control over sensitive data and hybrid deployment, enhancing quality and productivity for teams through integration of existing data sources.
- Customizing models through self-hosting allows tuning to specific needs, resulting in competitive performance with cost efficiency.
- Hosting models on the same platform as applications reduces latency, ensuring better user experience, particularly for applications with strict latency requirements.
- Deploying generative AI applications in production requires complementary components like prompt caching, guardrails for safe usage, observability tools, and model evaluation capabilities.
- A self-hosting stack requires multiple components, leveraging open-source software to self-host models in a cost-efficient manner with Amazon EKS.
- Key components include Model Gateway Service, Model Hosting Services, Model Evaluation Services, and Model Observability Services, all running as containers using Amazon EKS.
- Model Gateway enables centralized access to models, hybrid inferencing, and secure model access, while Model Hosting Services expose model weights as APIs with efficient inferencing capabilities.
- Model Evaluation Services assist in choosing the right model through rigorous testing, real-time evaluation, and continuous optimization of models and prompts.
- Model Observability Services capture essential metrics like latency and payload data, enabling better model assessment and optimization for changing business needs.
- Open-source frameworks such as LiteLLM, vLLM, LangFuse, and others fulfill the roles of key components for self-hosting AI solutions on Amazon EKS.
- LangFuse offers observability by tracking various data points, while also assisting in model evaluation through benchmarking and automated evaluation methods.
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