Inference Cost Control
Run multi-step AI workflows
without runaway inference costs
CLC Runtime reduces repeated model computation across workflow steps—without changing your agents or models.
- →Deeper workflows without exponential cost
- →Lower latency on shared-context pipelines
- →Predictable spend for verifier and retry loops
Why inference costs spiral
If you've reviewed an inference bill for multi-step workflows, these patterns are familiar.
Same context reprocessed every step
Each step in a workflow re-reads the full context from scratch. Ten steps means ten times the compute for shared history.
Verifier and retry loops double spend
Quality control loops that check or retry outputs multiply your baseline cost with no corresponding value increase.
Long prompts dominate latency
Processing large context windows takes disproportionately longer than generating outputs. Users wait; compute resources are consumed.
Increased capability amplifies the problem
Moving to more capable systems makes context processing even more expensive. Costs scale faster than capabilities.
What changes with CLC Runtime
Shared context avoids repeated recomputation
Common context across steps avoids redundant computation, reducing repeated work.
Subsequent steps build on prior computation
Follow-on steps leverage prior computation instead of starting over.
Cost scales with new work, not history
You pay for incremental computation, not full re-processing.
Safety-first execution with fallback
The system prioritizes correctness—falling back to full execution when needed.
What changes in practice
| Scenario | Traditional Execution | With CLC Runtime |
|---|---|---|
| Multi-step agent | Context reprocessed every step | Context reused across steps |
| Verifier loop | Doubles compute cost | Incremental cost only |
| Retry on failure | Full recomputation | Resume from prior computation |
| Long document pipeline | Input dominates latency | Output dominates latency |
How we measure impact
Metrics designed for infrastructure and finance review, not research papers.
Avoided Prefill Ratio
How much repeated computation is eliminated
Measures the fraction of redundant context processing removed across workflow steps.
avoided_tokens / baseline_tokensLatent Reuse Depth
How deep workflows run before cost explodes
Maximum consecutive steps that build on prior computation instead of starting over.
max consecutive reuse stepsFallback Rate
How often correctness is prioritized over savings
Frequency of safety-first decisions to recompute rather than reuse.
fallback_events / total_stepsSpeedup
Time saved on end-to-end workflows
Relative reduction in total workflow latency compared to baseline execution.
(baseline - clc) / baselineEnergy Avoidance
Physical compute cost reduction
Estimated energy savings when hardware reporting is available. Best-effort measurement.
Reuse Stability
Production reliability indicator
Consistency of reuse success across runs—important for capacity planning.
How CLC Runtime differs from existing inference optimizations
Existing optimizations improve individual inference calls. CLC Runtime targets redundant execution across workflow steps—a different layer addressing a different source of waste.
| Optimization approach | What it optimizes | When it helps | What it doesn't address |
|---|---|---|---|
| Optimized runtimes | Single inference execution | Throughput, decode speed | Repeated context across steps |
| Batching | Many requests at once | High concurrency | Sequential workflows |
| Quantization | System size & memory | Memory-constrained systems | Redundant prompt processing |
| Request-level optimization | Identical requests | Static prompts | Agent-to-agent reuse |
| CLC Runtime | Cross-step execution reuse | Multi-step workflows | Single-turn inference |
Compatible with existing optimizations. CLC Runtime stacks with optimized runtimes, quantization, and batching—it's not a replacement, but a complementary layer typically deployed alongside them.
If your costs are dominated by single inference calls, optimize inference. If costs explode as workflows add steps, execution reuse becomes the limiting factor.
Why provider optimizations aren't enough
Limitations of existing optimizations
- •Opaque behavior—no visibility into what's optimized or why
- •Not shared across agent steps—each call treated independently
- •No control over reuse decisions—provider decides, not you
- •No safety-aware execution—optimization without correctness guarantees
What's different with CLC Runtime
- •Runtime-level visibility—you observe reuse behavior
- •Predictable reuse behavior—consistent, not probabilistic
- •Measurable outcomes—metrics you can verify and report
- •Correctness-first design—fallback when reuse isn't safe
Is this right for you?
Best fit
- ✓Multi-step agent workflows
- ✓Shared context across pipeline stages
- ✓Rising or unpredictable inference spend
- ✓Self-hosted or hybrid inference deployment
- ✓Verifier loops, retry logic, or quality checks
Not a fit
- ✗Single-turn chat applications
- ✗Stateless, independent prompts
- ✗No workflow depth or context reuse
- ✗API-only usage without infrastructure control
Not sure if this applies?
If your AI workflows reuse the same context across steps—and inference cost or latency matters—CLC Runtime is worth evaluating. It's most valuable once workflows have multiple steps and shared context, whether you're a 5-person team or a platform org.
What adoption looks like
Designed for incremental rollout, not rip-and-replace.
Introduce execution-layer runtime
Add an execution-layer runtime alongside existing inference. No agent code changes required.
Keep agents and prompts
Your existing workflows run unchanged. Same inputs, same outputs.
Compare baseline vs CLC
Run identical workflows both ways. Measure latency, cost, and correctness.
Roll out incrementally
Expand to more workflows as you validate results. No big-bang migration.
See if your agent depth is artificially capped
Run a baseline vs CLC comparison on your actual workflows. 14-day trial, local installation, no data leaves your environment.