AI infrastructure is full of optimizations.
Faster runtimes. Smarter batching. Lower-precision weights. Prompt caching.
All of these matter. None of them solve the same problem.
This post is part of a series on the economics of multi-step AI workflows. We examine why inference costs scale with depth, why verification is disabled in production, and why existing optimizations fail to eliminate redundant execution across workflow steps.
Understanding the difference between inference optimization and execution efficiency is one of the most important architectural distinctions teams miss—and it explains why costs still explode even after "everything is optimized."
What Inference Optimization Actually Improves
Inference optimizations focus on individual model calls.
They answer the question:
How fast and cheaply can we run a single inference?
Common examples include:
**Optimized runtimes (vLLM, TensorRT-LLM)** Faster token generation, better memory scheduling, higher throughput.
**Batching** Running many requests together to improve GPU utilization.
**Quantization** Reducing precision to fit larger models or increase concurrency.
**Prompt caching** Avoiding reprocessing identical prompts.
These optimizations are mature, well understood, and widely deployed. They lower per-token cost and improve throughput.
They work exactly as advertised.
Where Inference Optimization Stops Helping
All inference optimizations share a core assumption:
Each inference call is independent.
They optimize:
- How fast tokens are generated
- How efficiently GPUs are used
- How well concurrent requests are batched
They do not optimize:
- Redundant computation across sequential steps
- Repeated processing of shared context
- Reuse of execution state between steps
That assumption holds for chat. It breaks for workflows.
Why Multi-Step Workflows Are a Different Problem
Agentic systems don't look like independent requests.
They look like:
- Planner → Executor → Verifier
- Analyze → Act → Check → Retry
- Read → Reason → Refine
These workflows have three defining properties:
**Shared context** Every step references the same documents, constraints, or prior reasoning.
**Sequential execution** Steps depend on each other. They can't be batched.
**Dependent computation** Later steps build directly on earlier ones.
Inference optimization can make each step faster. It cannot prevent each step from re-doing the same work.
If step two reprocesses the same context as step one, faster inference just makes redundant work happen faster.
The Architectural Choice Teams Face
Infrastructure teams are usually solving one of two problems:
**Inference optimization** Make individual calls faster and cheaper.
**Execution efficiency** Eliminate redundant computation across steps.
These are complementary, not competitive. But they are different problem classes with different solutions.
Optimizing one does not automatically fix the other.
The Real Optimization Stack
Production systems that scale tend to layer:
**Inference optimization** Improves per-call performance.
**Execution efficiency** Eliminates repeated work across steps.
**Orchestration** Manages control flow, retries, and logic.
Each layer solves a different constraint.
Most stacks are strong on the first and third—and weak in the middle.
When Each One Matters Most
Inference optimization dominates when:
- Throughput is the bottleneck
- Latency per call is critical
- Concurrency is high
Execution efficiency dominates when:
- Workflows are deep
- Context is large and reused
- Cost scales with depth, not concurrency
As systems become more agentic, the second case becomes the norm.
The Missing Layer
The ecosystem has largely solved inference optimization.
**LLM execution efficiency**—how work carries forward across steps—remains underdeveloped.
That gap didn't matter when AI systems were shallow. It matters a lot now.
As context windows grow and workflows deepen, redundant execution—not raw inference speed—becomes the dominant cost driver.
Understanding why multi-step AI workflows create different cost structures helps teams make better infrastructure decisions. The problem becomes clear when you see the hidden cost of reprocessing context across workflow steps.
CLC Labs is focused on the execution layer: eliminating redundant work across steps so inference optimizations can actually compound, not just repeat.