Infrastructure teams measure inference costs obsessively.
Tokens processed. GPU hours consumed. API spend per request.
But there's a major cost driver that rarely shows up in dashboards until it's too late:
redundant context reprocessing.
In multi-step workflows, this cost quietly becomes dominant—and most teams don't realize it until the bill arrives.
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.
What "Shared Context" Actually Means in Practice
Modern AI workflows are rarely stateless.
Multiple steps often rely on the same information:
- A long document several agents analyze
- A codebase reasoned over in stages
- A conversation history referenced repeatedly
- A knowledge base reused across decisions
That information is shared logically—but not computationally.
If a workflow has ten steps and each step references the same 50,000-token context, that context is processed ten separate times.
Nothing about it changes. The cost repeats anyway.
Why Reprocessing Dominates Inference Cost
For large models and long contexts, most of the cost lives in context processing, not generation.
**LLM prefill cost**—the step where the model reads and encodes context—often accounts for 70–90% of total inference cost.
Token generation (decode) is comparatively cheap.
So a multi-step workflow looks like this:
**Step 1** Process 50,000 tokens of context Generate 500 tokens → Cost: $X
**Step 2** Process the same 50,000 tokens again Generate 500 tokens → Cost: $X
**Step 3** Process the same 50,000 tokens again Generate 500 tokens → Cost: $X
The shared context dominates every step.
Ten steps means ten full context prefill passes—even though the information itself never changed.
Why Caching Only Helps at the Margins
At first glance, this looks like a caching problem.
And caching does help—when requests are identical.
But multi-step workflows don't send identical requests.
Each step:
- Adds new tool outputs or decisions
- Changes instructions or intent
- Builds on prior reasoning
The context may be shared, but the request is not.
Provider-level caching keys on full prompt equivalence. Slight differences invalidate the cache.
So the system reprocesses the same context again and again—correctly, but inefficiently.
The Operational Consequences Infra Teams Feel
This cost structure creates problems that show up downstream:
**Unpredictable spend** Cost scales with workflow depth, not request volume.
**Budget surprises** Per-request estimates fail once workflows deepen.
**Artificial limits** Teams cap steps to control cost, not because depth isn't useful.
**Quality tradeoffs** Verification and retries get disabled to stay within budget.
And it only gets worse as:
- Context windows expand (128K → 1M+ tokens)
- Workflows deepen (5 steps → 20+)
- Models grow more capable—and more expensive
Why This Cost Is Hard to See
Most infrastructure metrics hide the problem:
**Tokens processed** Doesn't distinguish first-time work from redundant work.
**GPU hours** Doesn't show what was reprocessed.
**API spend** Aggregates cost without attributing it to depth.
Teams see total spend rise—but can't easily isolate how much is caused by repeated execution of the same context.
That makes optimization reactive instead of intentional.
Why This Matters Now
As AI systems shift from single-turn interactions to multi-step workflows, redundant context reprocessing becomes the dominant cost driver.
Teams that don't account for it run into the same wall:
- Scaling depth becomes uneconomical
- Costs rise faster than value
- Quality features get cut to survive
Understanding this hidden cost is the first step toward fixing it.
This is why LLM inference cost explodes as workflows get deeper. The problem becomes structural when context window cost dominates execution. Teams eventually consider self-hosted LLM inference to regain visibility and control.
CLC Labs is focused on execution-layer infrastructure that eliminates redundant context reprocessing—so workflow depth doesn't automatically mean runaway cost.