How Much Do AI Agents Cost to Run

How the Credit System Works

The platform uses a credit-based billing system. 1,000 credits equals $1.00, so each credit costs $0.001. When your agent runs, it consumes credits for each AI model call, plus a small platform fee per execution. Credits are deducted from your account balance in real time as the agent processes data.

Each agent run consists of one or more steps. Non-AI steps (database reads, database writes, conditional checks, variable assignments) cost 1-2 credits each. AI steps cost more because they include the underlying model provider's charges plus the platform fee. The total cost per run is the sum of all step costs.

You can monitor your credit usage through the admin dashboard, which shows daily and monthly spending breakdowns by app and by agent. This helps you identify which agents are costing the most and whether adjustments are needed.

Cost Per AI Call by Model

The biggest cost factor is which AI model your agent uses. Here are the approximate costs per call with typical input/output lengths:

• GPT-5-nano: About 1 credit per call ($0.001). Best for simple classification, yes/no decisions, and keyword extraction. Handles straightforward tasks where the answer is obvious from the input.
• GPT-4.1-mini: About 4 credits per call ($0.004). The workhorse model for most agent tasks. Handles data extraction, multi-category classification, response generation, and summarization.
• Claude Sonnet: About 4-8 credits per call ($0.004-$0.008). Alternative mid-range model from Anthropic. Good for tasks requiring careful instruction following and structured output.
• GPT-4.1: About 15 credits per call ($0.015). Premium model for complex analysis, multi-factor reasoning, and tasks where accuracy justifies the higher cost.
• Claude Opus: About 20-30 credits per call ($0.02-$0.03). Most capable model for deep reasoning and long document analysis. Use only when other models are not accurate enough.
• GPT-5.2 (reasoning): About 20-50+ credits per call ($0.02-$0.05+). Chain-of-thought reasoning model. Costs vary significantly based on problem complexity. Use only for tasks requiring multi-step logical analysis.

These are approximate costs with moderate input and output sizes. Longer prompts and longer responses cost more because AI pricing is based on token count. A classification call with a short input costs less than a summarization call that processes a full document.

If you bring your own API key from OpenAI or Anthropic, AI model costs are billed directly by the provider at their rates. The platform charges no markup on your own API key usage.

Calculating Your Agent's Cost

To estimate your agent's monthly cost, multiply three numbers: cost per AI call, number of records per run, and number of runs per month.

Formula

Monthly cost = (credits per AI call) x (records per run) x (runs per month) / 1,000

This gives you the cost in dollars. Add 10-20% for platform fees (database operations, execution overhead) to get the total.

Example Calculation

A data processing agent using GPT-4.1-mini (4 credits per call) that processes 50 records per run and runs once per day:

• Per run: 4 credits x 50 records = 200 credits ($0.20)
• Per month: 200 credits x 30 days = 6,000 credits ($6.00)
• With platform overhead: approximately $7.00/month

The same agent using GPT-5-nano instead of GPT-4.1-mini:

• Per run: 1 credit x 50 records = 50 credits ($0.05)
• Per month: 50 credits x 30 days = 1,500 credits ($1.50)
• With platform overhead: approximately $2.00/month

Real-World Cost Examples

Customer Support Classifier

Classifies 75 support tickets per day into 5 categories. Uses GPT-5-nano because the categories are distinct and the classification is straightforward.

• Per day: 75 tickets x 1 credit = 75 credits ($0.075)
• Per month: 75 x 30 = 2,250 credits ($2.25)
• Replaces: 30 minutes of manual triage per day (about 15 hours/month of human labor)

Lead Qualification Agent

Scores 30 new leads per day based on form data, company info, and stated needs. Uses GPT-4.1-mini because scoring requires weighing multiple factors.

• Per day: 30 leads x 4 credits = 120 credits ($0.12)
• Per month: 120 x 30 = 3,600 credits ($3.60)
• Replaces: Sales team spending 45 minutes per day reviewing and prioritizing leads manually

Email Processing Agent

Reads and categorizes 100 incoming emails per day, generates draft responses for 40% of them. Uses GPT-4.1-mini for classification (100 calls) and GPT-4.1-mini for response generation (40 calls).

• Per day: (100 + 40) x 4 credits = 560 credits ($0.56)
• Per month: 560 x 30 = 16,800 credits ($16.80)
• Replaces: 1-2 hours per day of email sorting and response drafting

Content Moderation Agent

Screens 500 user posts per day. Uses GPT-5-nano for initial screening (500 calls), with 10% flagged for deeper analysis with GPT-4.1-mini (50 calls).

• Per day: (500 x 1) + (50 x 4) = 700 credits ($0.70)
• Per month: 700 x 30 = 21,000 credits ($21.00)
• Replaces: A part-time moderator reviewing every post manually

Daily Report Agent

Runs once per day. Queries the previous day's data (10 queries), aggregates results, and sends a summary to GPT-4.1-mini for analysis (1 longer call, about 8 credits for the larger context).

• Per day: 8 credits for AI + 20 credits for database operations = 28 credits ($0.028)
• Per month: 28 x 30 = 840 credits ($0.84)
• Replaces: 30 minutes of manual report compilation per day

Website Monitor Agent

Runs every hour, 24 hours per day. Checks website availability and content integrity. Most runs find no issues and cost only database queries (2 credits). When an issue is found (about 5% of runs), calls GPT-5-nano for analysis (1 credit).

• Per day: (24 x 2) + (1.2 x 1) = 49.2 credits ($0.049)
• Per month: 49.2 x 30 = 1,476 credits ($1.48)
• Replaces: Manual uptime checking or a dedicated monitoring service subscription

How to Reduce Agent Costs

Use the Cheapest Viable Model

The single biggest cost lever is model selection. Test your agent with GPT-5-nano first. If accuracy is above 90%, stick with it. Only upgrade to a more expensive model when the cheaper one produces unacceptable results. Many classification tasks that seem complex actually work well with the cheapest model.

Skip Empty Runs

Add an early check in your workflow that exits immediately if there is no new data to process. A scheduled agent that runs every hour but only has new data twice a day should skip the other 22 runs without calling the AI. This simple check eliminates 90% of unnecessary AI calls.

Batch Records Into Single AI Calls

Instead of one AI call per record, combine 5-10 records into a single prompt: "Classify each of the following items..." This reduces the number of API calls by 5-10x. The trade-off is slightly less accurate results for individual items, so test this approach with your specific data before committing.

Use a Two-Stage Filter

Add a cheap first stage that filters out items that do not need expensive processing. A GPT-5-nano call (1 credit) decides whether the input requires detailed analysis. If 60% of inputs are simple cases handled at the first stage, you save 60% of your premium model costs. This is the chaining pattern applied to cost optimization.

Reduce Schedule Frequency

If your agent runs every 15 minutes but processing the data hourly would be just as useful, switch to hourly. That is a 4x cost reduction. Most batch processing agents do not need to run more frequently than every few hours unless the business requires near-real-time processing.

Bring Your Own API Key

If you have your own OpenAI or Anthropic API key, use it. The platform charges no markup on your own key's usage, so you pay the provider's raw rate. This eliminates the platform's AI model fee, though software and execution fees still apply.

Budgeting for AI Agents

When budgeting for AI agents, start by estimating your data volume and choosing a model. Use the formula above to calculate expected monthly costs. Then add a 50% buffer for the first month to account for testing, prompt tuning, and unexpected data volume.

Typical Monthly Budgets by Business Size

• Small business (1-2 agents, low volume): $5-$20/month. A simple classifier and a daily report agent handle most needs.
• Medium business (3-5 agents, moderate volume): $20-$100/month. Multiple agents handling support, leads, content, and reporting.
• Larger operation (5+ agents, high volume): $100-$500/month. Agents processing hundreds of records daily across multiple business processes.

These ranges assume thoughtful model selection (using cheap models where possible) and reasonable scheduling. Costs can be higher if every agent uses premium models or runs more frequently than needed.

Measuring ROI

The return on investment for AI agents is usually clear: compare the agent's monthly cost to the value of the human time it replaces.

Time Savings Calculation

Estimate how many hours per month the manual version of the task takes. Multiply by the hourly cost of the person who would do it (salary, benefits, opportunity cost). That is the value the agent provides. Compare it to the agent's monthly credit cost.

Example: A support classifier that saves 15 hours/month of manual triage. If the support team's loaded cost is $30/hour, the manual process costs $450/month. The agent costs $2.25/month. The ROI is $447.75/month, or a 199:1 return.

Quality Improvement Value

Beyond time savings, agents often improve quality. Faster ticket routing means faster resolution times. Consistent lead scoring means the sales team focuses on the best prospects. Automated monitoring catches problems that humans would miss. These quality improvements have real value even though they are harder to quantify than time savings.

Scaling Value

Agents scale linearly with data volume. Processing 100 records costs twice as much as processing 50 records, but hiring a second person to handle twice the volume costs far more than twice the original salary (recruitment, training, management overhead). As your business grows, agent costs grow proportionally while human costs grow faster than proportionally.