Used For

• +**Your custom AI chatbot crashes every weekend** → Stable microservices that handle traffic spikes without your phone buzzing
• +**You're manually setting up GPU clusters and it takes weeks** → Automated operators deploy everything in hours, not months
• +**Your AI models give different answers every time** → Consistent inference engines that your customers can actually rely on
• +Builds AI agents that can act autonomously - not just answer questions but actually do tasks
• +Handles the security patches and compliance stuff that open-source leaves to you

Best For

• >Your AI models work in the lab but fall apart when real users touch them
• >You have a pile of NVIDIA GPUs and need them to actually make money
• >Compliance team won't let you deploy open-source AI tools in production

Not For

• -Solo developers or teams under 50 people — you're paying enterprise prices for GPU infrastructure you don't have
• -Companies without NVIDIA GPUs — this is useless on regular servers or other hardware
• -Anyone wanting plug-and-play AI — this requires Kubernetes expertise and dedicated DevOps time

Pairs With

• *Kubernetes (where all the GPU magic actually happens and you'll spend most of your setup time)
• *AWS or VMware (to host your expensive GPU clusters without buying physical servers)
• *TensorFlow or PyTorch (for training your models before NVIDIA takes over the deployment part)
• *Helm (to actually install all the operators without manually editing YAML files)
• *Prometheus (to monitor your GPU usage and justify the massive hardware costs to your CFO)
• *Slack (where your DevOps team will complain about driver updates and your data scientists will ask why inference is slow)

The Catch

• !You need serious GPU hardware first — we're talking $30K+ per H100 GPU before you even start paying for the software
• !Requires real Kubernetes knowledge — not something your junior developer can figure out from YouTube tutorials
• !No public pricing means custom enterprise quotes that start around $4,500 per GPU per year

Bottom Line

For when your AI prototypes need to become real products that don't crash during board meetings.