# LLM API Cost Calculator MCP for AI Agents MCP

> LLM API Cost Calculator helps you figure out exactly what your AI features are costing you. It lets you project monthly spends, compare different model pricing tiers, and see how much prompt caching actually saves you money.

## Overview
- **Category:** productivity
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_kJ7vtlOfVbB3mYe7tCKK0Sz4MTZKFnSeYxrrgdxC/mcp
- **Tags:** llm, cost-estimation, token-pricing, api-usage, budgeting

## Description

Stop guessing how much your AI features will cost once you go live. It is easy to lose track of token usage when you are juggling different providers and models. This MCP gives your AI client a way to pull real-time pricing data to make smart financial choices. You can ask your agent to run the numbers on a specific request or look at a 30-day forecast based on your daily traffic estimates. If you are trying to decide between two different models, it will tell you which one hits the sweet spot for your budget. It even breaks down the impact of prompt caching so you know if that optimization is actually paying off. Finding these answers is a lot faster when you have it all in one place through the Vinkius catalog.

## Tools

### compute_request_cost
Get the exact dollar amount for a single interaction based on input and output tokens. This helps you understand the unit cost of your features.

### estimate_monthly_spend
Project your total 30-day expenditure using your average daily request volume. Use this to set realistic budgets for your stakeholders.

### calculate_cache_impact
See how much money you save when your prompt caching hits a high success rate. It helps you justify your technical optimizations with real numbers.

### compare_providers
Look at a side-by-side analysis of different pricing tiers to find the cheapest option. This makes it easier to swap models without budget surprises.

## Prompt Examples

**Prompt:** 
```
How much would it cost to run 1,000 requests a day with 2k input and 1k output tokens on a mid-tier model?
```

**Response:** 
```
Based on a standard mid-tier pricing of $0.0003 per input token and $0.0006 per output token, here is the breakdown:

| Metric | Calculation | Cost |
| :--- | :--- | :--- |
| **Single Request** | (2,000 * 0.0003) + (1,000 * 0.0006) | **$1.20** |
| **Daily Total** | 1,000 * $1.20 | **$1,200.00** |
| **Monthly Total** | $1,200 * 30 | **$36,000.00** |
```

**Prompt:** 
```
Compare the cost of 50k requests a month between a high-end model and a budget-friendly one.
```

**Response:** 
```
Here is the monthly cost comparison for 50,000 requests (assuming 1k input / 500 output tokens each):

*   **High-End Model:** $1,250.00 / month
*   **Budget-Friendly Model:** $375.00 / month

**Total Savings:** $875.00 per month by switching to the budget-friendly tier.
```

**Prompt:** 
```
I am using prompt caching. If my hit rate is 80% on a 10k token prompt, how much do I save?
```

**Response:** 
```
With an 80% cache hit rate on a 10,000 token prompt, your costs drop significantly:

*   **Standard Cost:** $0.50 per request
*   **With 80% Cache Hit:** $0.10 per request
*   **Total Savings:** **$0.40 per request (80% reduction)**
```

## Capabilities

### Calculate exact price for a single API call
Get the precise dollar amount for one interaction based on your specific input and output tokens.

### Forecast your monthly spending
Project your total 30-day expenditure using your average daily request volume.

### Compare pricing across different providers
Look at a side-by-side analysis of different model pricing tiers to find the cheapest option.

### Measure the actual savings from prompt caching
See how much money you save when your prompt caching hits a high success rate.

## Use Cases

### Validating feature profitability
A product manager wants to know if a new feature will be profitable. They ask the agent to compare costs for 10,000 requests across three different models.

### Model swapping for cost savings
A dev team wants to know if they should switch to a smaller model. They use the tool to see the price difference for their specific token volume.

### Managing startup burn rates
A startup is hitting a spending limit. They ask the agent to find the most economical tier for their current daily traffic.

### Auditing prompt caching ROI
An engineer wants to know if prompt caching is working. They run a calculation to see the savings on a high-volume request.

## Benefits

- Stop overspending on high-tier models by using compare_providers to find cheaper alternatives that still meet your needs.
- Build accurate budgets for your team using estimate_monthly_spend to see your 30-day forecast.
- Know the exact cost of every interaction with compute_request_cost so you can price your own products accurately.
- Verify your optimization wins by using calculate_cache_impact to see the real dollar value of your caching strategy.
- Make faster architectural decisions by getting side-by-side pricing data instead of jumping between different pricing pages.

## How It Works

The bottom line is you get clear, data-backed spending projections instead of rough guesses.

1. Connect the MCP to your AI client via Vinkius.
2. Provide your estimated daily request volume and token counts.
3. Get a detailed breakdown of projected costs and provider comparisons.

## Frequently Asked Questions

**Can the LLM API Cost Calculator help me set a monthly budget?**
Yes. It uses your estimated daily request volume to project your total 30-day expenditure so you can set accurate budgets for your team.

**How does this tool help me choose between different AI models?**
It provides a side-by-side analysis of different pricing tiers, making it easy to see which model hits the best price point for your specific token usage.

**Can I see how much I save with prompt caching?**
Yes. It calculates the specific dollar impact of your cache hits so you can see the real-world ROI of your technical optimizations.

**Is this for real-time billing?**
No, this tool is for projection and comparison. It helps you plan your spending and compare models rather than monitoring live usage.

**How accurate are the cost projections?**
The projections are highly accurate as they are based on the specific pricing data provided by the model providers you choose to analyze.

**Can I use this to see the cost of a single user interaction?**
Yes. You can break down the exact cost of a single request based on your specific input and output token counts.

**How is the monthly cost calculated?**
The tool calculates the cost of a single request based on your input and output tokens, then multiplies that by your estimated daily requests and 30 days.

**Can I compare different models like GPT-4o and Claude?**
Yes, by using the `compare_providers` tool, you can input multiple tier IDs to see a ranked list of costs from cheapest to most expensive.

**Does the calculator account for prompt caching?**
Yes, you can use `calculate_cache_impact` to determine how much a specific cache hit ratio will reduce your input token costs.