Cost Management - Documentation

Understanding Workflow Costs

Workflows consume AI credits based on what they do. The main cost drivers are:

AI Agent Nodes

The biggest expense in most workflows. Costs depend on:

Model used: GPT-4 costs more than GPT-3.5, Claude Opus more than Haiku
Input length: How much data you send to the agent
Output length: How much the agent generates
Tool usage: Web searches, code execution, and integrations add costs

Action Nodes

Generally low cost or free:

Integration actions: Usually free (no AI involved)
HTTP requests: Free within your workflow execution
Notifications: Free

Other Costs

Web Search nodes: Small fee per search
Code nodes: Free (no AI usage)
Condition/Loop nodes: Free (just logic)

You can view exact costs for each node after a test run. Click on the node and check the Usage tab.

[IMAGE_COST_BREAKDOWN_CHART] 💰 GRAPHIC SUGGESTION: Bar chart or pie chart showing typical cost breakdown for a sample workflow (e.g., 85% agent nodes, 10% web search, 5% other).

Monitoring Costs

Per-Run Costs

After each workflow run, you can see:

Go to the Runs tab
Click on any run
View total cost and per-node breakdown
Check which nodes consumed the most credits

Workflow-Level Costs

Track spending over time:

Go to workflow settings
View the Usage section
See daily, weekly, and monthly costs
Download detailed usage reports

[IMAGE_USAGE_DASHBOARD] 📊 GRAPHIC SUGGESTION: Screenshot of the workflow usage dashboard showing cost trends over time, with daily breakdowns and node-level details.

Setting Cost Limits

Protect yourself from unexpected charges by setting spending limits:

Monthly Limit

Set a maximum spending cap for the entire workflow:

Go to workflow settings
Set Monthly Limit (e.g., $100)
Workflow automatically pauses when limit is reached
You’ll receive notifications at 50%, 75%, and 90%

Per-Execution Limit

Prevent runaway costs from a single run:

Set Execution Limit (e.g., $5 per run)
Workflow stops if a single run exceeds this amount
Useful for preventing issues with loops or retries

Alert Thresholds

Get notified before hitting limits:

Add custom alert amounts (e.g., $25,$ 50, $75)
Receive notifications when crossing each threshold
Team members can be added as notification recipients

When a workflow hits its spending limit, it pauses automatically. You’ll need to increase the limit or wait until the next month to resume.

[IMAGE_COST_LIMIT_CONFIGURATION] ⚙️ GRAPHIC SUGGESTION: Screenshot of the cost limit configuration interface with monthly limits, per-execution limits, and alert thresholds clearly labeled.

Optimization Strategies

Choose the Right Model

Don’t use premium models for simple tasks: Over-powered:

Task: Extract email from text
Model: GPT-4 ❌ (expensive)

Right-sized:

Task: Extract email from text
Model: GPT-3.5 Turbo ✅ (fast and cheap)

Model Selection Guide:

Simple extraction, categorization: GPT-3.5 Turbo, Claude Haiku
Complex reasoning, analysis: GPT-4, Claude Sonnet
Creative tasks, long contexts: GPT-4 Turbo, Claude Opus

Optimize Agent Prompts

Shorter, clearer prompts cost less and work better: Inefficient:

Please analyze the following customer feedback and provide a detailed
analysis including sentiment, main topics discussed, urgency level,
customer satisfaction, key issues mentioned, suggested actions, and
any other relevant insights you can gather from the text...

[2000 words of context]

Efficient:

Analyze this feedback. Return:
- Sentiment: positive/neutral/negative
- Urgency: low/medium/high
- Key issue (1 sentence)

Feedback: {{trigger.message}}

Use structured outputs. They’re more reliable and prevent the model from generating unnecessary explanatory text.

Batch Processing

Process multiple items together instead of one at a time: Expensive:

Loop: For each of 100 customers
  → Agent: Analyze customer ❌ (100 agent calls)

Cost-effective:

Code: Group customers into batches of 10
Loop: For each batch
  → Agent: Analyze 10 customers ✅ (10 agent calls)

You save roughly 90% on AI costs! [IMAGE_LOOP_COST_COMPARISON]

Use Code for Simple Transformations

Don’t use AI for tasks that code can handle: Expensive:

Agent: "Convert this date to YYYY-MM-DD format" ❌

Free:

# Code Node ✅
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

Cache Results

Don’t re-process the same data:

# Store results to avoid re-processing
cache = {}

def get_analysis(text_id, text):
    # Check if we've already analyzed this
    if text_id in cache:
        return cache[text_id]

    # Only call AI if not cached
    result = call_agent(text)
    cache[text_id] = result
    return result

Limit Loop Iterations

Always set a maximum iteration count:

Loop Settings:
- Max Iterations: 100 ✅

Without limit: Infinite loop = infinite costs ❌

Cost-Effective Patterns

Smart Filtering

Filter data before sending to AI:

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

Conditional AI Usage

Only use AI when necessary:

Trigger → Condition: Is complex analysis needed?
       → YES: Agent (expensive)
       → NO: Code (free)

Progressive Enhancement

Start cheap, escalate only if needed:

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

Tiered Model Approach

Use different models for different tasks:

Input → Fast categorization (GPT-3.5)
     → [Simple category] → Template response (no AI)
     → [Complex category] → Deep analysis (GPT-4)

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:

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

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

Add 20% buffer: $156

4. Set Appropriate Limits

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

[IMAGE_COST_ESTIMATION_WORKSHEET] 📈 GRAPHIC SUGGESTION: Worksheet or calculator mockup showing the cost estimation formula with sample numbers filled in.

Monitoring After Launch

Daily Spot Checks

For the first week, check daily:

Are costs matching estimates?
Any unexpectedly expensive runs?
Which nodes are the biggest cost drivers?

Weekly Reviews

After the first week, review weekly:

Total spending vs. budget
Cost per run trends
Identify optimization opportunities

Monthly Analysis

Each month, analyze:

Total cost and cost per run
Compare to previous months
ROI: Time saved vs. cost
Decide if optimizations are needed

Common Cost Pitfalls

Unbounded Loops

Problem:

Loop through API pages until no more data
→ If API pagination is broken, loops forever

Solution:

Max iterations: 50
Add condition: Stop if no new data received

Verbose Agent Outputs

Problem:

Agent instruction: "Provide a detailed explanation..."
→ Agent generates 1000 words when you needed 50

Solution:

Use structured output with specific fields
Add: "Be concise" to instructions

Processing Duplicate Data

Problem:

Processing the same records multiple times

Solution:

Add deduplication check
Store processed IDs
Skip already-processed items

Over-Engineering

Problem:

Using AI for everything, even simple tasks

Solution:

Ask: "Could code do this just as well?"
Reserve AI for tasks that need intelligence

[IMAGE_OPTIMIZED_VS_UNOPTIMIZED_WORKFLOW] ⚠️ GRAPHIC SUGGESTION: Side-by-side comparison showing a cost-heavy workflow vs. an optimized version of the same workflow, with cost savings highlighted.

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

ROI Calculation

Manual approach:
- 2 hours per day × 20 days = 40 hours/month
- At $50/hour = $2,000 in time cost

Workflow approach:
- $200/month in AI credits
- 1 hour setup/maintenance

ROI: $1,800/month saved (90% reduction)

Getting Help

If costs are higher than expected:

Review this guide’s optimization strategies
Check the Best Practices page
Contact support at support@langdock.com for audit and suggestions

Next Steps

Best Practices

Optimization patterns and techniques

Error Handling

Prevent costly error loops

Agent Node

Understand agent node costs and optimization

Getting Started

Build your first workflow

Getting started

Chat

Assistants

Integrations

Workflows

​Understanding Workflow Costs

​AI Agent Nodes

​Action Nodes

​Other Costs

​Monitoring Costs

​Per-Run Costs

​Workflow-Level Costs

​Setting Cost Limits

​Monthly Limit

​Per-Execution Limit

​Alert Thresholds

​Optimization Strategies

​Choose the Right Model

​Optimize Agent Prompts

​Batch Processing

​Use Code for Simple Transformations

​Cache Results

​Limit Loop Iterations

​Cost-Effective Patterns

​Smart Filtering

​Conditional AI Usage

​Progressive Enhancement

​Tiered Model Approach

​Estimating Costs Before Launch

​1. Count Expected Runs

​2. Test with Real Data

​3. Calculate Monthly Estimate

​4. Set Appropriate Limits

​Monitoring After Launch

​Daily Spot Checks

​Weekly Reviews

​Monthly Analysis

​Common Cost Pitfalls

​Unbounded Loops

​Verbose Agent Outputs

​Processing Duplicate Data

​Over-Engineering

​Cost vs. Value

​When to Spend More

​ROI Calculation

​Getting Help

​Next Steps

Best Practices

Error Handling

Agent Node

Getting Started

Understanding Workflow Costs

AI Agent Nodes

Action Nodes

Other Costs

Monitoring Costs

Per-Run Costs

Workflow-Level Costs

Setting Cost Limits

Monthly Limit

Per-Execution Limit

Alert Thresholds

Optimization Strategies

Choose the Right Model

Optimize Agent Prompts

Batch Processing

Use Code for Simple Transformations

Cache Results

Limit Loop Iterations

Cost-Effective Patterns

Smart Filtering

Conditional AI Usage

Progressive Enhancement

Tiered Model Approach

Estimating Costs Before Launch

1. Count Expected Runs

2. Test with Real Data

3. Calculate Monthly Estimate

4. Set Appropriate Limits

Monitoring After Launch

Daily Spot Checks

Weekly Reviews

Monthly Analysis

Common Cost Pitfalls

Unbounded Loops

Verbose Agent Outputs

Processing Duplicate Data

Over-Engineering

Cost vs. Value

When to Spend More

ROI Calculation

Getting Help

Next Steps