Where to run your inference workloadsÂ
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.Â
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!
How to build function calling and JSON mode for open-source and fine-tuned LLMs
Use a state machine to generate token masks for logit biasing to enable function calling and structured output at the model server level.
Export your model inference metrics to your favorite observability tool
Export model inference metrics like response time and hardware utilization to observability platforms like Grafana, New Relic, Datadog, and Prometheus.
How to build function calling and JSON mode for LLMs
In this webinar we'll dive deep into how to implement function calling and JSON mode for LLMs: defining schemas and tools, building a state machine, and more.
Baseten partners with Google Cloud to deliver high-performance AI infrastructure to a broader audience
Baseten is now on Google Cloud Marketplace, empowering organizations with the tools to build and scale AI applications effortlessly.
Introducing Baseten Hybrid: control and flexibility in your cloud and ours
Baseten Hybrid is a multi-cloud solution that enables you to run inference in your cloud—with optional spillover into ours.
Where to run your inference workloads
Read our white paper on how to choose a hosting option for AI model inference comparing cloud, self-hosted, and hybrid deployment options.
Building high-performance compound AI applications with MongoDB Atlas and Baseten
Using MongoDB Atlas and Baseten’s Chains framework for compound AI, you can build high-performance compound AI systems.
Introducing function calling and structured output for open-source and fine-tuned LLMs
Add function calling and structured output capabilities to any open-source or fine-tuned large language model supported by TensorRT-LLM automatically.
The best open-source image generation model
Explore the strengths and weaknesses of state-of-the-art image generation models like FLUX.1, Stable Diffusion 3, SDXL Lightning, and Playground 2.5.
How to double tokens per second for Llama 3 with Medusa
We observe up to a 122% increase in tokens per second for Llama 3 after training custom Medusa heads and running the updated model with TensorRT-LLM
