# ai-pricing-hub [Health: Active]

**Category:** 💻 Developer Tools  
**Repository:** https://github.com/OptimNow/ai-pricing-hub-mcp  
**GitHub Stars:** 0  
**Views:** 0  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/ai-pricing-hub

## Description
Compare LLM API pricing, estimate workload costs, and benchmark cloud compute. By OptimNow.

## Tools
Capabilities this server exposes over MCP:

- **compare-llm-models** — "What is out there?" Browse and filter the catalogue on price, quality (Chatbot Arena ELO), efficiency and capabilities, with a self-hostability read from the licence.
- **recommend-llm-model** — "Just tell me which one." A ranked top 3 for one workload under your constraints (budget, minimum ELO, required capability, self-hostability), each with a per-constraint satisfied or violated breakdown as the evidence. Over-constrained queries return the nearest misses, labelled as such.
- **compare-models-side-by-side** — "How do these specific ones compare?" 2 to 4 named models across all 8 use case profiles at a chosen monthly volume, list and optimized cost for each.
- **estimate-llm-cost** — "What will this cost us per month?" Per-request and monthly cost for your own volume, token shape, cache hit rate and batch eligibility.
- **compare-compute-pricing** — "What should we run it on?" Compute instance rates across AWS, Azure, GCP, OCI, OVH, DigitalOcean and Alibaba, by region and category.

## Claude Desktop Quick Installation
Remote MCP endpoint (confidence: high). Install path detected from listing signals. Add as a URL/SSE server in your client:

```json
"mcpServers": {
  "ai-pricing-hub": {
    "url": "https://optimtoken-mcp.optimnow.io/mcp"
  }
}
```

## Documentation

## What ai-pricing-hub does

The ai-pricing-hub MCP server gives AI assistants structured pricing and comparison tools for language models and cloud compute. Its LLM data comes from the OptimToken catalogue, which tracks more than 250 models and is refreshed daily. Results can include price, Chatbot Arena ELO, efficiency, capabilities, self-hostability inferred from licensing, and whether the returned figures were verified.

The server is read-only and does not require credentials. It is intended for decisions such as choosing a model for a defined workload, comparing named models at a monthly volume, estimating request and monthly spend, or reviewing compute rates by provider and region.

## How it works

The hosted MCP endpoint is available at `https://optimtoken-mcp.optimnow.io/mcp`. LLM tools first request data from the OptimToken API. If that source is unavailable, they may use OpenRouter data or an embedded snapshot. The compute comparison uses the pricing API first and an embedded snapshot as its fallback. Responses identify the source tier through a provenance object and expose whether prices were verified.

Cost calculations use the workload details supplied to the tool, including request volume, input and output token shape, cache hit rate, and batch eligibility. The catalogue also provides eight predefined workload profiles: Support Ticket, Knowledge Q&A, Meeting Summary, Marketing Content, Coding Task, Invoice Processing, Call Summary, and Agent Workflow.

## Setup and configuration

For normal use, add the hosted endpoint as an HTTP MCP server. Claude Code can use:

```bash
claude mcp add --transport http optimtoken https://optimtoken-mcp.optimnow.io/mcp
```

Claude.ai or Claude Desktop can add the URL through Settings and custom connectors. Cursor, Windsurf, and VS Code can add an HTTP MCP server entry pointing to the same address. The URL must include `/mcp` and omit a trailing slash for the hosted widgets to render correctly.

Local development requires Node.js 24 or newer:

```bash
npm install
npm run dev
npm test
npm run build
```

The fallback catalogue can be regenerated with `npm run refresh-fallback`. No environment variables or API credentials are specified as required.

## Tools and capabilities

- `compare-llm-models` filters the model catalogue by price, ELO, efficiency, capabilities, and self-hostability.
- `recommend-llm-model` ranks up to three models against budget, ELO, capability, and self-hosting constraints. If no model satisfies every constraint, it returns labelled near matches.
- `compare-models-side-by-side` compares two to four named models across eight workload profiles, including list and optimized cost at a selected monthly volume.
- `estimate-llm-cost` calculates per-request and monthly costs from custom workload assumptions.
- `compare-compute-pricing` compares instance rates from AWS, Azure, GCP, OCI, OVH, DigitalOcean, and Alibaba by region and category.

## Limitations and notes

The ai-pricing-hub MCP server can fall back to uncorrected upstream or embedded prices. Lower-tier data may not include the catalogue's verified corrections, so check the returned provenance and `dataAsOf` value before relying on an embedded snapshot. The fallback data is refreshed manually and can become outdated.

Pricing comes from third-party sources and is provided without warranty. Verify figures against vendor pricing pages before committing spend. Nothing sent to the five tools is stored according to the provided documentation.

_Full upstream README: https://allmcps.com/mcp/ai-pricing-hub/readme_