Free AI Tool · Agent Cost · Multi-Step · Workflow · Agentic AI · Tool Calls · Chain Cost
AI Agent Cost Simulator
Estimate the cost of AI agentic workflows. Enter the number of steps, model for each step, and tokens per step. See total run cost, cost per agent execution and monthly projections. Covers tool calls, reasoning chains and multi-model pipelines.
How to Use the AI Agent Cost Simulator
Enter the number of steps in your agentic workflow, select the model, set average tokens per step and runs per day. Furthermore, the simulator calculates cost per step, cost per run, daily, monthly and annual spend. This helps budget for multi-step AI agent workflows before deployment. Additionally, compare different models by changing the selector to see cost impact.
- Set steps per runHow many API calls does one agent execution make? Furthermore, coding agents typically use 5 to 20 steps.
- Select modelChoose the model used for each step. Furthermore, compare by switching models.
- Set tokens per stepAverage input and output tokens per API call in the chain.
- Set daily runsHow many complete agent executions per day.
- View costsSee per-run, daily, monthly and annual cost projections.
Why Agentic AI Costs Multiply
A single chatbot request makes one API call. Furthermore, an AI agent chains 5 to 50 calls per task: planning, tool calling, reading results, reasoning and generating output. Each step consumes tokens independently. Additionally, cumulative input tokens grow with each step because the agent passes conversation history forward. A 5-step agent can consume 10x to 20x the tokens of a single call.
Token accumulation is the hidden cost multiplier. Furthermore, step 1 sends 2,000 tokens. Step 2 sends step 1's output (1,000 tokens) plus its own context (2,000 tokens) for a total of 3,000 input tokens. By step 5, input tokens can reach 6,000 to 10,000 per call. Additionally, this is why agent costs are dramatically higher than simple Q&A costs.
Competitor Gap Analysis
No free tool calculates multi-step agentic workflow costs. Furthermore, existing calculators assume single-call pricing. Agent workflows multiply costs by step count, and cumulative context growth adds a non-linear factor that simple calculators miss.
| Feature | Standard calculators | LazyTools |
|---|---|---|
| Multi-step agent cost | No | 1 to 50 steps |
| Cost per run | No | Steps x tokens x model rate |
| Model comparison | Single model | 6 models in dropdown |
| Daily/monthly/annual | Some | All three projections |
| Copy analysis | No | Full text report |
Common Agent Workflow Profiles
| Workflow | Steps | Tokens/step | Runs/day | Sonnet cost/mo |
|---|---|---|---|---|
| Customer support triage | 3 | 800 in, 400 out | 500 | ~$108 |
| Code review agent | 8 | 3,000 in, 1,500 out | 50 | ~$324 |
| Research assistant | 12 | 4,000 in, 2,000 out | 20 | ~$518 |
| Data pipeline agent | 6 | 2,000 in, 500 out | 200 | ~$378 |
| Content creation chain | 5 | 1,500 in, 2,000 out | 30 | ~$162 |
Cost Optimisation for AI Agents
Use tiered routing within agent chains. Furthermore, use a cheap model (Haiku, GPT-5 Mini) for planning and tool-call steps, then switch to a flagship model (Sonnet, GPT-5.2) for the final synthesis step. This cuts costs by 50 to 70 percent. Additionally, summarise conversation history between steps instead of passing full context to reduce token accumulation.
Implement step budgets. Furthermore, set a maximum token count per step and a maximum number of steps per run. Without guardrails, agents can enter loops that consume thousands of tokens before failing. Additionally, log step-by-step token counts in production to identify which steps are most expensive and optimise them first.
References
1. Anthropic: Tool Use (Function Calling).
2. OpenAI: Function Calling Guide.
3. MorphLLM: AI Coding Costs 2026.
4. MetaCTO: True Cost of AI API.
Frequently Asked Questions
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