lindy-cost-tuning

Lindy Cost Tuning

Overview

Lindy uses a credit-based pricing model. Every task costs credits based on model size, step count, premium actions, and duration. Cost tuning targets: model right-sizing, agent consolidation, trigger optimization, and credit monitoring.

Prerequisites

• Lindy workspace with billing access
• Multiple active agents to evaluate
• Dashboard access to review per-agent task history

Credit Cost Reference

Factor	Credits
Basic model task (Gemini Flash)	1-2
Mid-tier model (GPT-4o-mini, Claude Haiku)	2-5
Large model task (GPT-4, Claude Sonnet)	5-10
Premium model (Claude Opus)	~10+
Phone call (US/Canada)	~20/minute
Phone call (international)	21-53/minute
Premium actions (webhooks)	Additional per action
Minimum per task	1 credit

Plan Costs

Plan	Monthly	Credits	Per Extra Seat
Free	$0	400	N/A
Pro	$49.99	5,000	$19.99
Business	$299.99	30,000	Included
Enterprise	Custom	Custom	Custom

Instructions

Step 1: Audit Agent Credit Consumption

For each active agent, collect:

1. Task count (last 30 days) — from Tasks tab
2. Average credits per task — total credits / task count
3. Model used — from agent settings
4. Trigger frequency — how often the agent fires

Create a cost audit table:

Agent	Tasks/Month	Credits/Task	Model	Monthly Credits	% of Total
Support Bot	500	5	Claude Sonnet	2,500	50%
Lead Router	200	2	GPT-4o-mini	400	8%
Report Gen	30	10	GPT-4	300	6%

Step 2: Right-Size Models

The highest-impact optimization. For each agent, ask:

"Does this task actually need GPT-4/Claude, or would Gemini Flash work?"

Current Setup	Optimized	Savings
Email classify with Claude Sonnet (5 cr)	Gemini Flash (1 cr)	80%
Data extract with GPT-4 (10 cr)	GPT-4o-mini (3 cr)	70%
Simple routing with Claude Opus (10 cr)	Gemini Flash (1 cr)	90%

Test the downgrade: Run 10 tasks with the smaller model. Compare output quality. Most classification, routing, and extraction tasks work identically on smaller models.

Step 3: Consolidate Redundant Agents

Multiple single-purpose agents cost more than one multi-purpose agent:

Before (5 agents, 5 minimum credits per run):

Agent 1: Classify billing emails
Agent 2: Classify technical emails
Agent 3: Classify general emails
Agent 4: Draft billing responses
Agent 5: Draft technical responses

After (1 agent, 1 minimum credit per run):

Support Agent: Classify email → Condition (billing/technical/general)
  → Draft appropriate response → Send

Cost impact: Reducing from 5 agents to 1 saves minimum-credit overhead and simplifies management.

Step 4: Optimize Trigger Frequency

Credits are consumed every time a trigger fires. Reduce unnecessary triggers:

Email Received:

Before: Trigger on ALL emails (300/day) = 300 tasks
After:  Filter: label "support" AND NOT from "noreply@" (40/day) = 40 tasks
Savings: 87% fewer tasks

Schedule trigger:

Before: Every 15 minutes (96/day)
After:  Every 2 hours (12/day)
Question: Does this agent really need to run every 15 minutes?

Slack trigger:

Before: Any message in #general (200/day)
After:  Messages containing "@support-bot" (10/day)
Savings: 95% fewer tasks

Step 5: Reduce Steps Per Task

Each action in a workflow costs credits. Eliminate unnecessary steps:

• Combine multiple LLM calls into one (see lindy-performance-tuning)
• Use Set Manually instead of AI Prompt for known values
• Remove debug/logging steps in production
• Simplify condition branches

Step 6: Optimize Knowledge Base Usage

KB search costs credits per query. Optimize:

• Reduce Max Results from 10 to 4 (sufficient for most queries)
• Use specific query instructions to get relevant results in one search
• For small datasets (<100 entries), consider putting data directly in the prompt

Step 7: Budget Monitoring Setup

1. Check credit usage weekly in Settings > Billing
2. Set internal alerts for high-consumption agents:
   • 50% of budget: Warning — review usage
   • 80% of budget: Alert — optimize or upgrade
   • 95% of budget: Critical — pause non-essential agents

Step 8: Deactivate Idle Agents

Review agents monthly:

• No tasks in 30 days → Pause the agent
• No tasks in 90 days → Delete or archive
• Lindy only charges for active agent execution, not idle agents

Monthly Cost Optimization Checklist

• Review per-agent credit consumption
• Identify agents using large models for simple tasks
• Check for redundant agents that could be consolidated
• Review trigger filter effectiveness
• Remove unused integrations from agents
• Verify no loops or runaway agent steps
• Compare actual spend to budget

Error Handling

Issue	Cause	Solution
Unexpected credit spike	Trigger filter removed or loosened	Review and restore trigger filters
Agent consuming 10x normal	Looping agent step	Add exit conditions, check task history
Credits exhausted mid-month	Under-budgeted or spike	Upgrade plan or pause non-critical agents
Model downgrade hurts quality	Task needs larger model	Selectively upgrade only that step

Resources

• Lindy Pricing
• Lindy Documentation

Next Steps

Proceed to lindy-reference-architecture for production architecture patterns.