Most AI teams start with APIs.
They're fast to adopt, easy to scale, and remove infrastructure from the critical path. For early usage, the economics make sense.
But as systems mature and workflows deepen, something changes.
Teams don't self-host just for control or data privacy. They self-host because API economics stop working.
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.
Why APIs Make Sense Early
At small scale, APIs are the right choice:
**No infrastructure overhead** No GPUs to purchase. No clusters to manage.
**Pay-per-use pricing** Spend tracks usage cleanly.
**Operational reliability** Uptime, scaling, and maintenance are abstracted away.
This model works well for:
- Prototyping and experimentation
- Low-volume production traffic
- Single-turn or shallow interactions
Cost is intuitive: requests × tokens × price per token
For a while, nothing feels broken.
Where the Economics Start to Shift
As usage scales, the cost structure changes in ways APIs don't smooth over.
Three forces drive the shift:
**Volume discounts plateau** Discounts help, but they don't fundamentally change cost curves.
**Workflow depth multiplies spend** One user interaction becomes many API calls.
**Context processing dominates** Long contexts make prefill the primary cost driver—not output tokens.
A single user request might now trigger:
- 10 steps
- 10 API calls
- The same 50K tokens of context reprocessed each time
Cost scales linearly with depth.
Convenience vs. Predictability
APIs optimize for convenience. They do not optimize for execution visibility.
With APIs:
**Cost depends on workflow depth** Hard to forecast without knowing step counts.
**Execution is opaque** You can't see what's being reprocessed.
**Optimization is constrained** Redundant execution can't be eliminated from the outside.
As workflows deepen, spend becomes harder to predict—even if request volume stays flat.
What Changes When Inference Moves In-House
When teams self-host, inference stops being a service and becomes infrastructure.
That changes everything.
**Costs become more predictable** Fixed infrastructure replaces variable per-call pricing.
**Execution becomes visible** Teams can see exactly where compute is going.
**Optimization becomes structural** Redundant work can be eliminated, not just made faster.
This is when teams start investing in:
- GPU infrastructure
- Inference optimization
- Execution-layer efficiency
New Optimization Priorities Emerge
Self-hosted teams still care about:
- Throughput
- Latency
But a third priority becomes unavoidable:
execution efficiency.
Teams deploy optimized runtimes like vLLM and TensorRT-LLM to make individual calls faster.
Then they realize something else matters just as much:
How much work are we repeating across steps?
API providers optimize per-call efficiency. They can't optimize execution between calls.
Self-hosting makes that possible.
The Real Transition Point
Teams usually self-host when:
- Volume amortizes fixed infrastructure cost
- Workflows are deep and sequential
- Execution-layer control becomes necessary
This isn't about saving pennies per token.
It's about moving from:
variable cost to
controllable cost structure
And from:
opaque execution to
optimizable execution
The Stack Teams End Up Building
Self-hosted systems naturally layer into:
**Inference layer** Optimized runtimes and kernels.
**Execution layer** Eliminating redundant computation across steps.
**Orchestration layer** Managing workflow logic and control flow.
Inference optimization is mature. Execution efficiency is not.
That gap is why so many teams self-host and still struggle with cost.
Why This Matters Now
As AI systems shift from single-turn interactions to multi-step workflows, more teams will hit this transition point.
Understanding why API economics break helps teams plan:
- Infrastructure investments
- Architecture choices
- Long-term cost structure
The decision isn't just about APIs vs. self-hosting.
It's about whether you control execution—or pay repeatedly for the same work.
The shift to **self-hosted LLM inference** happens when LLM workflow cost becomes unpredictable. Teams need LLM execution efficiency that APIs can't provide. Understanding the hidden cost of reprocessing context makes the economics clear.
CLC Labs is focused on the execution layer—where self-hosting actually becomes economically meaningful.