Cost Management

from datetime import datetime
date = datetime.strptime(trigger.date, "%m/%d/%Y")
return {"date": date.strftime("%Y-%m-%d")}

When to use code instead of AI:

• Date/time formatting

• Mathematical calculations

• Data filtering and sorting

• String manipulation

• JSON parsing/formatting

Cost-Effective Patterns

Smart Filtering

Filter data before sending to AI:

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Ask AI

Trigger (100 items) → Code: Filter relevant items (20 items)
                   → Agent: Process 20 items (not 100)

Progressive Enhancement

Start cheap, escalate only if needed:

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Ask AI

Data → Quick check (regex/code) → [SIMPLE] → Done
                                → [COMPLEX] → AI analysis

Estimating Costs Before Launch

Before activating a workflow, estimate monthly costs:

1

Count expected runs

How often will this workflow trigger?

• Forms: Expected submissions per month

• Scheduled: Runs per day × 30

• Webhooks: Events per month from integration

2

Test with real data

Run 5–10 tests with realistic data and check costs:

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Ask AI

Example:
- Test run 1: $0.12
- Test run 2: $0.15
- Test run 3: $0.11
- Average: $0.13 per run

3

Calculate monthly estimate

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Ask AI

Average cost per run: $0.13
Expected monthly runs: 1,000
Estimated monthly cost: $130

Add 20% buffer: $156

4

Set appropriate limits

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Ask AI

Set monthly limit: $200 (includes buffer)
Set per-run limit: $1.00 (catches anomalies)

Cost vs. Value

Remember: The goal isn’t to spend zero - it’s to get maximum value for your spending.

When to spend more

It’s worth paying for:

• Time savings: If it saves hours of manual work

• Quality improvements: Better AI models for critical decisions

• Scalability: Automating tasks that don’t scale manually

Next Steps

• Core Concepts — Understand workflow execution and optimization

• Variable Usage — Optimize data handling in workflows

• Agent Node — Understand agent node costs and optimization

• Getting Started — Build your first workflow