📍Shared vs Private LLMs: How to Unlock Improved Latency, Cost, and Control

Open-source LLMs have changed the game, but shared endpoints won’t carry you past MVP. The moment traffic surges or your compliance team starts to ask questions, cracks appear: sluggish latency, ballooning costs, and zero control of data flows.

Join us at live webinar on May 30 @ 10AM PT / 1PM ET to see why private LLM deployments are the only way to scale AI with speed, security, and savings.

What you’ll take home

• Blueprint PDF with architectures your infra team will love.

• Benchmark sheet showing real-world latency and cost numbers across configurations.

• Replay link so you can rewatch – or forward to your boss – on your own schedule. 

What you’ll learn:

• Why shared endpoints implode at scale: sneaky costs, privacy blind spots, rigid configs, and latency swings that ruin user experience.

• Lock-tight architectures: exploring VPC architectures that isolate control/data planes to keep every prompt and model weight inside your walls.

• Cost math that wins budgeting battles: side-by-side numbers for shared vs. dedicated endpoints and how techniques like turbo, caching, and smart autoscaling cut spend.

• Performance benchmarks: latency and reliability metrics showing how dedicated endpoints crush rate limits, throughout, and production SLAs.

• Do-it-yourself demo: step-by-step guide to choosing hardware and spinning up your own private LLM endpoint.

Whether you're an ML engineer shipping product or a technical lead owning infra – this is how you take back control.