Subvolts take Clarifai's CEO explains how to win the AI inference race for speed and cost. Nov 28, 2025 - Developers
- Learn how to optimize AI inference for faster responses and reduced GPU costs across different environments.
- Business
- Understand how enterprises and governments use Clarifai for secure, scalable AI deployment and cost-effective operations.
This episode features a key executive from a company focused on AI operations, discussing critical trends in inference efficiency.
#304 Matt Zeiler: Why Government And Enterprises Choose Clarifai For AI Ops
Audio streams from Craig S. Smith. Your place is remembered on this device.
From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
Craig Smith interviews Matt Zeiler, founder and CEO of Clarifai, on the Eye on AI podcast. They discuss why AI inference is becoming the new battleground, surpassing training in importance. The conversation explores how Clarifai optimizes AI operations for speed, cost, and scalability across cloud, on-premise, and edge environments, particularly for enterprises and government use.
This episode covers how companies like Clarifai make AI run faster and cheaper after it's built. It's important because making AI work well in the real world, for many users, is the next big challenge.
This episode is for AI practitioners, enterprise IT leaders, and government officials. It explains the critical shift towards optimizing AI inference, a key area where companies are seeking efficiency and cost savings. Listeners learn how a unified compute orchestration layer can improve LLM and agentic system performance. This directly relates to frontier labs' focus on making AI more accessible and cost-effective for real-world deployment, addressing bottlenecks in speed, cost, and scalability.
- HostCraig S. Smith
- GuestMatt Zeilerfounder and CEO, Clarifai
- Guest: Matt Zeiler, founder and CEO of Clarifai
- Show: Eye on AI
- Host: Craig S. Smith
- Length: 55 minutes
- Topic: AI inference is the new battleground for speed, cost, and scalability.
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Why is AI inference becoming the new battleground for speed, cost, and real world scalability, and how are companies like Clarifai reshaping the AI stack by optimizing every token and every deployment?
In this episode of Eye on AI, host Craig Smith sits down with Clarifai founder and CEO Matt Zeiler to explore why inference is now more important than training and how a unified compute orchestration layer is changing the way teams run LLMs and agentic systems.
We look at what makes high performance inference possible across cloud, on prem, and edge environments, how to get faster responses from large language models, and how to cut GPU spend without sacrificing intelligence or accuracy. Learn how organizations operate AI systems in regulated industries, how government teams and enterprises use Clarifai to deploy models securely, and which bottlenecks matter most when running long context, multimodal, or high throughput applications.
You will also hear how to optimize your own AI workloads with better token throughput, how to choose the right hardware strategy for scale, and how inference first architecture can turn models into real products. This conversation breaks down the tools, techniques, and design patterns that can help your AI agents run faster, cheaper, and more reliably in production. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI
- AI Ops
- Matt Zeiler
- managing and running AI systems
- inference
- Matt Zeiler
- when an AI model makes a prediction or generates output
- LLMs
- Matt Zeiler
- large language models, like ChatGPT
- agentic systems
- Matt Zeiler
- AI systems that can take actions and make decisions autonomously
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WithAndrii Hrytseniuk
Questions people ask
- Why is AI inference more important than training now?
- AI inference is becoming the new battleground for speed, cost, and real-world scalability, reshaping the AI stack by optimizing every token and deployment.
- How can organizations improve AI performance and cut costs?
- A unified compute orchestration layer can enable faster responses from large language models and reduce GPU spend without sacrificing intelligence or accuracy.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Craig S. Smith.