OptimToken

io.github.OptimNowPublished by GitHub account OptimNowv0.4.2Updated Oct 24, 2026

Compare LLM API and cloud compute pricing, estimate workload costs, pick a model within budget.

VerifiedStreamable HTTPWeb executableCloud & InfrastructureFinanceAI & ML

Overview

AI-generated overview

Hosted MCP server that lets an assistant look up current LLM API and cloud compute prices, estimate workload costs, and recommend a model within budget.

What you can do
  • Browse and filter the LLM catalogue by price, quality (ELO), efficiency and capabilities
  • Compare 2-4 named models across eight use-case profiles at a chosen monthly volume
  • Estimate per-request and monthly cost from your own token counts, cache hit rate and batch eligibility
  • Recommend a ranked top 3 for one workload under constraints such as budget, minimum ELO or required capability
  • Compare Linux on-demand compute instance prices across AWS, Azure, GCP, DigitalOcean, OCI, OVH and Alibaba by region and category
When to use it
Useful when you need dated, sourced pricing rather than a model's remembered figures: sizing an LLM workload budget, choosing between named models, or picking a cloud instance. All tools are read-only lookups, so it suits planning and comparison work.
Requirements
A remote streamable HTTP endpoint; nothing to install. Add the hosted URL exactly as given, including the /mcp path and no trailing slash, since interactive widgets only render for that form. No credentials or environment variables are needed.
Before you install
Prices come from a third-party catalogue and fall back to OpenRouter or an embedded snapshot when the primary source is unreachable; lower tiers serve uncorrected prices, so check the provenance fields and any notice before relying on a figure. The embedded fallback is refreshed manually and can age. Queries are sent to the provider's servers.

Installation

In SourceWeft

  1. Open OptimToken in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/io.github.OptimNow%2Fai-pricing-hub/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

{
  "mcpServers": {
    "optimtoken": {
      "type": "http",
      "url": "https://optimtoken-mcp.optimnow.io/mcp"
    }
  }
}

README

OptimToken MCP

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.

[CI] [MCP Server] [ChatGPT Apps] [Node] [Prices] [License: MIT] [GitHub Stars]


Connect in 30 seconds

The server is hosted, so there is nothing to install.

https://optimtoken-mcp.optimnow.io/mcp

Paste it exactly as written, with /mcp and no trailing slash. The widgets render only for that form: the host checks a hash of the URL you entered.

ClientHow to add it
[Claude Code]claude mcp add --transport http optimtoken https://optimtoken-mcp.optimnow.io/mcp
[Claude.ai / Desktop]Settings → Connectors → Add custom connector, paste the URL above
[ChatGPT]Settings → Connectors → Add, paste the URL. Comparisons render as interactive widgets
[Cursor] [Windsurf] [VS Code]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?"


Why this exists

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:

  • Live prices, not remembered ones. Every LLM figure comes from the OptimToken catalogue, which tracks 250+ models and refreshes daily.
  • Corrected prices. OptimToken keeps a committed price archive, a verified overrides table checked against vendor pricing pages, and an anomaly check that alarms on the half-price and double-price breaks upstream feeds occasionally publish. This server asks that catalogue rather than re-deriving prices itself.
  • Cost per request, not cost per million tokens. Price-per-token comparisons hide the thing you actually pay for. The tools apply your token shape, your cache hit rate and batch eligibility, and return a figure per request and per month.
  • Every answer carries its provenance. Which tier served it, and whether the prices were verified.

Tools

ToolWhat 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.


Where the numbers come from

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.

ToolTier 1Tier 2Tier 3
LLM toolsGET /api/llm-modelsOpenRouter directembedded snapshot
Compute toolGET /api/pricing?region=not availableembedded 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.


Local development

Requires Node.js 24+.

bash
npm installnpm run dev              # Skybridge dev server + MCP inspector at localhost:3000npm test                 # schema conformance, serialisation precision, data sourcesnpm run build            # widgets + server

The static fallback catalogue is refreshed by hand, not on a schedule:

bash
npm run refresh-fallback

Because it is manual, check its dataAsOf before trusting a tier-3 response. An unrefreshed fallback ages silently.

text
ai-pricing-hub-mcp/├─ server/src/index.ts              # tool + widget registrations├─ server/src/lib/optimtoken-api.ts # the one base URL constant, fetch and timeout discipline├─ server/src/lib/                  # ranking, efficiency scoring, provenance, normalisation├─ server/src/data/                 # static fallback pricing + region maps└─ web/src/widgets/                 # React widgets rendered in the client

Built with Skybridge, deployed on Fly.io.


The rest of the family

OptimTokenThe web app. Same catalogue, full UI, an AI advisor and a public JSON API.
AI ROI CalculatorDoes the AI business case pay for itself. Same prices, plus harness costs and value modelling.
cloud-finops-skillsFinOps knowledge for AI agents: AWS, Azure, GCP, AI inference, SaaS.
finops-mcp-resourcesMCP servers, tutorials and client guides for cloud cost work.

License

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.

Source: README.md at commit 84296b5

Tools

5

compare-llm-models

Compare LLM Models

Browse and filter the whole LLM catalogue and get back a ranked table: price, quality (ELO), efficiency and capabilities. Use this when the user wants to SEE THE FIELD — 'show me models under $1/1M', 'which providers have vision models', 'list open-weight models above ELO 1300'. For a single PICK under a budget use recommend-llm-model; to weigh 2-4 NAMED models against each other use compare-models-side-by-side. Prices come from optimtoken.optimnow.io where reachable; the response's `provenance` says which tier served them and whether they are vendor-verified. Filter by provider, price tier (category), openness, capability, price range, or minimum ELO score. Optionally enrich with business metrics for a use case. Price tier and openness are independent: a model can be Frontier-priced and open-weight at once. Reports both list-price cost and the optimized cost achievable with prompt caching and the batch API. Prices, costs and scores are final as returned: rounded and ranked server-side, and dated by `provenance`. The text block is a numbered table of the same figures.

ParameterslimitminElocategoryopennessprovidercapabilityvolumePresetmaxInputPriceuseCasePresetmaxOutputPrice

estimate-llm-cost

Estimate LLM Cost

Cost a workload with EXACT numbers the caller supplies: arbitrary token counts per request and any monthly volume, not just the 10k/100k/1m presets the other cost tools use. Use this for 'about 800 in and 200 out, 4 million calls a month', or to price one named model across every use-case profile. To compare 2-4 named models like for like at a preset volume, use compare-models-side-by-side instead. Provide a model name to get detailed cost breakdowns, or compare costs across all use case presets. Each figure comes twice: list price, and the optimized price achievable with prompt caching and the batch API. Cost figures are final as returned: rounded server-side and dated by `provenance`, with the lever behind each saving, or the reason there is none, named in the text.

ParametersmodelNamemonthlyVolumeuseCasePresetcustomInputTokenscustomOutputTokens

compare-models-side-by-side

Compare Models Side by Side

Compare 2-4 named LLM models against all 8 use-case profiles at a chosen monthly volume, showing list and optimized cost for each. Use when the user names specific models to weigh against each other, rather than filtering the whole catalogue. If they also supply their own token counts, or a volume outside 10k/100k/1m, use estimate-llm-cost instead. Every name is resolved against the catalogue and the outcome is reported in `resolution` and in the text: a name that matched nothing, matched several models, or duplicated an earlier pick is stated explicitly, and such a name gets no column rather than a zero cost. Prices and costs are final as returned: rounded server-side and dated by `provenance`.

ParametersmodelsrequiredvolumePreset

recommend-llm-model

Recommend an LLM Model

Pick a model. Returns a ranked top 3 for one workload under optional constraints, each with a per-constraint satisfied/violated breakdown as the evidence. Use this when the user wants an ANSWER rather than a table — 'what should I use for support tickets under $500 a month'. To browse or filter the whole catalogue instead, use compare-llm-models. Constraints: (monthly budget, minimum ELO, required capability, self-hostability). Returns a top 3 as structured facts — efficiency rank, ELO, list and optimized cost, FinOps flag, volatility, and a per-constraint satisfied/violated breakdown. When nothing satisfies every constraint the query is reported as over-constrained, `recommendations` is empty, and the nearest misses are returned in `nearMisses` instead, each carrying the constraint it failed and flagged as a miss in the text. The ranking is computed server-side and is final.

ParametersminEloopennessvolumePresetuseCasePresetrequiredmaxMonthlyBudgetrequiredCapability

compare-compute-pricing

Compare Cloud Compute Pricing

Compare cloud compute instance pricing across AWS, Azure, GCP, DigitalOcean, OCI, OVH, and Alibaba. Filter by region, provider, vCPUs, memory, category, processor, or use case. All prices are Linux on-demand list prices in USD. Not every price column is live: `provenance.priceTypes` says which of each provider's price columns come from a live API, which are static constants, and which are unavailable, and `provenance.staticPriceColumns` lists the non-live ones outright; a savings-plan or reserved rate that appears there is a static estimate, not a live quote. Prices are returned as the catalogue serves them and dated by `provenance`.

ParameterslimitregionsortByuseCasecategorymaxVCPUsminVCPUsprovidermaxMemoryminMemoryprocessor

Version history

4
  1. v0.4.2LatestOct 8, 2026
  2. v0.4.1Oct 7, 2026
  3. v0.4.0Oct 5, 2026
  4. v0.2.0Sep 16, 2026

Score

100% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 daysRequired25 pts
  • Auth declaredRequired10 pts
  • Tools listedRequired15 pts
  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts