Reduce Your LLM Costs by Up to 40%
AI workflows can get expensive fast. Understanding where your tokens go — and how to spend fewer of them — is the key to scaling AI operations sustainably.
Why LLM costs grow fast
Large language models charge per token — both input and output. As workflows scale from a single prompt to multi-step pipelines with dozens of skills, costs compound quickly. A workflow that costs $0.02 per run becomes $200 at 10,000 executions. Without visibility into per-step costs, teams often discover budget overruns after they happen.
The problem is not that LLMs are expensive — it is that most teams lack the tooling to understand where their tokens are going and which steps are driving the majority of spend. Cost efficiency starts with visibility.
5 biggest cost drivers
These are the most common reasons AI workflow costs spiral out of control.
Oversized models for simple tasks
Redundant or repeated API calls across workflows
Unoptimized prompts that waste tokens on every request
No visibility into per-step token consumption
Scaling workflows without cost simulation first
8 practical strategies to reduce LLM costs
Apply these strategies across your AI workflows to lower token usage and overall spend.
- 1.
Right-size your models
Use smaller, cheaper models for low-complexity tasks like classification or extraction. Reserve large models for nuanced generation.
- 2.
Reduce prompt length
Trim system prompts, remove redundant instructions, and keep few-shot examples minimal to lower input token counts.
- 3.
Cache repeated results
Store outputs from deterministic steps and reuse them instead of calling the LLM again for the same input.
- 4.
Batch similar requests
Group related tasks into a single prompt where possible, reducing per-call overhead and total token usage.
- 5.
Use Eco mode
Switch to cost-efficient model tiers for non-critical workflow steps to cut costs without sacrificing quality where it matters.
- 6.
Simulate before executing
Run cost simulations on your entire workflow to identify expensive steps before committing real API spend.
- 7.
Optimize workflow topology
Restructure your workflow to eliminate unnecessary steps, parallelize independent tasks, and reduce sequential depth.
- 8.
Monitor and iterate
Track cost-per-workflow over time and continuously refine prompts, model choices, and workflow structure.
For a deeper dive into workflow structure and topology, see our AI Workflow Optimization guide. For self-hosted alternatives, explore the Open Workflow Ecosystem.
How Cromus helps
Cromus gives you the tools to understand, simulate, and optimize LLM costs before you spend a single token.
Cost Simulator
Estimate token usage and dollar costs for every step in your workflow across 5 simulation modes — including Open Source for self-hosted stacks — before making a single LLM inference call.
Optimization Engine
Get actionable recommendations to reduce cost and complexity. See your optimization score improve as you apply changes.
Workflow Intelligence
Understand execution order, model requirements, and savings opportunities across your entire workflow graph.
Stop guessing. Start simulating.
See exactly what your AI workflows will cost before you run them. View pricing or jump straight in.