Compare LLM API pricing, estimate workload costs, and benchmark cloud compute. By OptimNow.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ or use 1-click editor setup below.
๐ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Built by OptimNow. Ask an AI assistant what a model or an instance actually costs, and get a dated, sourced figure instead of a number the model remembers from its training data.
The server is hosted, so there is nothing to install.
| Client | How to add it |
|---|---|
claude mcp add --transport http optimtoken https://ai-pricing-hub-mcp-9604f763.alpic.live/ | |
| Settings โ Connectors โ Add custom connector, paste the URL above | |
| Settings โ Connectors โ Add, paste the URL. Comparisons render as interactive widgets | |
| Add an HTTP MCP server entry pointing at the URL |
Then just ask:
"We send 200k support tickets a month at about 1,500 input tokens each. Which model gives me the best quality per euro, and what would it cost?"
Model prices change weekly, and a language model's idea of them is frozen at its training cutoff. Ask one what Claude or GPT costs and you get a confident answer that was true some months ago, with no date attached and no way to tell. The same applies to cloud instance rates, which additionally vary by region in ways nobody memorises.
This server replaces recall with a lookup:
| Tool | What it answers |
|---|---|
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. |
All five are read-only and take no credentials. Nothing you send is stored.
Use case profiles ship with realistic token shapes, so you do not have to invent them: Support Ticket, Knowledge Q&A, Meeting Summary, Marketing Content, Coding Task, Invoice Processing, Call Summary, Agent Workflow.
optimtoken.optimnow.io is the single source of truth. When it cannot be reached, the
server degrades in tiers rather than failing, and says which tier it used.
| Tool | Tier 1 | Tier 2 | Tier 3 |
|---|---|---|---|
| LLM tools | GET /api/llm-models | OpenRouter direct | embedded snapshot |
| Compute tool | GET /api/pricing?region= | not available | embedded snapshot (137 rows) |
Tiers 2 and 3 serve uncorrected prices, and that matters more than it sounds. An
upstream feed once published a frontier model at half its real list price, which halves
every monthly figure derived from it. So every response carries a provenance object
with pricesVerified, and the lower tiers put a notice at the top of the answer. A
fallback should never quietly downgrade correctness.
Tier 1 is accepted only when the catalogue reports that it is itself serving fresh upstream data. If the site is on its own fallback, it carries no corrections, and this server treats it accordingly.
Requires Node.js 24+.
The static fallback catalogue is refreshed by hand, not on a schedule:
Because it is manual, check its dataAsOf before trusting a tier-3 response. An
unrefreshed fallback ages silently.
Built with Skybridge, deployed on Alpic.
| OptimToken | The web app. Same catalogue, full UI, an AI advisor and a public JSON API. |
| AI ROI Calculator | Does the AI business case pay for itself. Same prices, plus harness costs and value modelling. |
| cloud-finops-skills | FinOps knowledge for AI agents: AWS, Azure, GCP, AI inference, SaaS. |
| finops-mcp-resources | MCP servers, tutorials and client guides for cloud cost work. |
Released under the MIT License.
Prices served by this server come from third-party sources and are provided as is, without warranty. Verify against vendor pricing pages before committing spend.
Questions about your own AI or cloud bill? Talk to OptimNow.
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