Best practices to accelerate inference for large-scale production workloads

When a user asks an AI assistant to analyze a 50-page document, answers that take seconds aren't fast enough. This expectation is reshaping infrastructure requirements across the AI industry. The competitive environment and pace of innovation in AI-native products has fundamentally changed what users consider acceptable — and what businesses must deliver to remain competitive.

This shift isn't just about user experience — it's economics. Inference costs account for the majority of operational expenses in AI-native applications achieving product-market fit and starting to scale. Higher throughput means serving more requests per GPU. Faster token generation means shorter end-to-end response times, which means handling more concurrent users on the same infrastructure. For companies running AI at scale, optimizing time to first token (TTFT) and throughput measured in tokens per second (TPS) can be the difference between sustainable unit economics and burning capital on excess hardware.

Speed is a competitive moat. Faster responses improve retention, enable new use cases, and reduce cost per request. The companies delivering the fastest inference aren't optimizing one layer of the stack — they're rethinking how models, runtimes, and hardware work together. Off-the-shelf inference frameworks leave substantial performance on the table, and closing that gap requires either deep expertise across the full stack — or partnering with platforms that have already invested in it.

Understanding inference economics for AI-native apps

When you're AI native, inference isn't a line item, it's the foundation of your P&L. Unlike traditional SaaS that enjoys near-zero marginal costs, AI applications often face per-request costs that scale with usage. A typical SaaS company targets 80% gross margins; an AI-native product might operate closer to 40-60%.

The compound effect of model orchestration destroys margins faster than most teams anticipate. For example, a coding assistant might trigger many model calls for a single user request: understanding intent, searching documentation, generating code, checking syntax, writing tests, explaining the solution. The features users love most — deeper reasoning, more thorough analysis, cross-referencing multiple sources — are precisely the ones that burn through margins.

The path to profitability requires fundamental improvements in inference efficiency, not just user growth. Every percentage point of optimization directly impacts your bottom line. The technical strategies we'll explore aren't academic exercises. They're the difference between products that scale and products that don't.

Balancing speed and economics in AI inference

Running AI in production means delivering on two fronts: Customer expectations and business fundamentals. These requirements aren't in tension, they're interconnected. Meeting one without the other leads to products users love but can't afford to scale, or efficient systems no one wants to use.