Where to run your inference workloads
From startups to enterprises, businesses of all sizes are quickly realizing that using custom, fine-tuned, or open-source AI models has become essential for building competitive products.
At the same time, using these models in production presents challenges around reliability, security, and performance. While AI providers like Anthropic and OpenAI can serve as a starting point, they fall short for enterprise-grade solutions. Organizations need greater control over model behavior, data privacy, and scalability—without sacrificing performance.
In this guide, we dive deep into the different hosting options for AI model inference, including:
Cloud, self-hosted, and hybrid hosting options; their advantages and disadvantages
How your inference hosting solution plays a key role in successful AI initiatives
How to select the right hosting solution for your organization
Download our guide now and learn what hosting option is best suited for your AI model inference!
Create custom environments for deployments on Baseten
Test and deploy ML models reliably with production-ready custom environments, persistent endpoints, and seamless CI/CD.
Introducing canary deployments on Baseten
Our canary deployments feature lets you roll out new model deployments with minimal risk to your end-user experience.
Evaluating NVIDIA H200 Tensor Core GPUs for LLM inference
Are NVIDIA H200 GPUs cost-effective for model inference? We tested an 8xH200 cluster provided by Lambda to discover suitable inference workload profiles.
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Building Compound AI with Baseten Chains (SF Tech Week)
Join us for a presentation about building compound AI for next-generation AI products, followed by a happy hour!
