OptimToken: AI and Compute Economics

io.github.OptimNow/ai-pricing-hubVersion 0.11.0

For FinOps: what an AI workload costs, where to buy it, when to self-host, what moved. All sourced.

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Responds132 msNov 14, 2026, 14:41 UTCAnswered our handshake without sign-in.

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Last checked: Nov 14, 2026, 14:41 UTC
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https://optimtoken-mcp.optimnow.io/mcpResponds132 msNov 14, 2026, 14:41 UTC

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Claude Code

claude mcp add --transport http ai-pricing-hub https://optimtoken-mcp.optimnow.io/mcp

Claude Desktop and claude.ai

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Settings > Connectors > Add custom connector
Name: ai-pricing-hub
Remote MCP server URL: https://optimtoken-mcp.optimnow.io/mcp

Cursor

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

VS Code

code --add-mcp '{"name":"ai-pricing-hub","type":"http","url":"https://optimtoken-mcp.optimnow.io/mcp"}'

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Tools

8 tools

Tools of OptimToken: AI and Compute Economics
	Description		Read-only	Idempotent	Open world
`compare-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. Use this for 'the cheapest 8 vCPU / 32 GiB instance in Europe', and for what a commitment saves: each row gives the Linux list price in USD per hour on demand, on spot, and under a 1-year and 3-year commitment (savings plan, Google CUD, reservation) as an effective hourly rate, each with its saving against that row's on-demand rate. A row with no commitment rate says so. 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`.	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. Use this for 'the cheapest 8 vCPU / 32 GiB instance in Europe', and for what a commitment saves: each row gives the Linux list price in USD per hour on demand, on spot, and under a 1-year and 3-year commitment (savings plan, Google CUD, reservation) as an effective hourly rate, each with its saving against that row's on-demand rate. A row with no commitment rate says so. 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`.	No	Yes	Not declared	Yes
`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', 'the ten cheapest models for a coding task'. The table is ordered by Arena ELO unless `sortBy` asks for efficiency, cost or price, and `limit` keeps the first rows of that order. 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.	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', 'the ten cheapest models for a coding task'. The table is ordered by Arena ELO unless `sortBy` asks for efficiency, cost or price, and `limit` keeps the first rows of that order. 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.	No	Yes	Not declared	Yes
`compare-model-hosts`Who sells a model, and at what price. Most models are sold by several hosts (the model maker, a cloud platform such as Amazon Bedrock, Azure or Vertex, or an independent host) at different rates, and every other tool here quotes ONE of them. For 1-4 named models this returns how many hosts sell each, the cheapest, the dearest, the model maker's own rate, the spread between them, any promotional listing, and what a workload costs per request and per month at each. Use it for 'where is Llama 3.3 70B cheapest', 'is the price you quoted the maker's own', 'how much does the choice of host matter for this model'. To cost a workload at the quoted price with caching and batch, use estimate-llm-cost. Read the caveats it returns: the cheapest host may serve a quantized build, which is not the same product, or a promotional rate that ends. It also lists every host with its rate, cheapest first, the platforms the model is listed on (Direct API, AWS Bedrock, Azure AI Foundry, Google Vertex, OpenRouter: a hand-kept estimate for the major models) and the AWS regions where Amazon Bedrock lists it. `hostList.available` says whether that full list was read: without it only the cheapest, the dearest, the maker's own and promotional listings are known, and the text says so. A missing platform or region is not known, never 'not sold there'. Per-host prices are list prices read nightly and dated by `provenance.hostPricingAsOf`, and a model with no host data says so rather than reading as sold by one host.	Who sells a model, and at what price. Most models are sold by several hosts (the model maker, a cloud platform such as Amazon Bedrock, Azure or Vertex, or an independent host) at different rates, and every other tool here quotes ONE of them. For 1-4 named models this returns how many hosts sell each, the cheapest, the dearest, the model maker's own rate, the spread between them, any promotional listing, and what a workload costs per request and per month at each. Use it for 'where is Llama 3.3 70B cheapest', 'is the price you quoted the maker's own', 'how much does the choice of host matter for this model'. To cost a workload at the quoted price with caching and batch, use estimate-llm-cost. Read the caveats it returns: the cheapest host may serve a quantized build, which is not the same product, or a promotional rate that ends. It also lists every host with its rate, cheapest first, the platforms the model is listed on (Direct API, AWS Bedrock, Azure AI Foundry, Google Vertex, OpenRouter: a hand-kept estimate for the major models) and the AWS regions where Amazon Bedrock lists it. `hostList.available` says whether that full list was read: without it only the cheapest, the dearest, the maker's own and promotional listings are known, and the text says so. A missing platform or region is not known, never 'not sold there'. Per-host prices are list prices read nightly and dated by `provenance.hostPricingAsOf`, and a model with no host data says so rather than reading as sold by one host.	No	Yes	Not declared	Yes
`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`.	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`.	No	Yes	Not declared	Yes
`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. The presets carry their own cache-hit and batch assumptions; a custom token shape carries none unless cacheHitRate and/or batchEligible are passed with it, and each cost entry states which applied in `optimizationBasis`. 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.	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. The presets carry their own cache-hit and batch assumptions; a custom token shape carries none unless cacheHitRate and/or batchEligible are passed with it, and each cost entry states which applied in `optimizationBasis`. 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.	No	Yes	Not declared	Yes
`estimate-self-hosting-breakeven`API or own capacity: the monthly volume at which renting GPU instances costs the same as paying a model's API per token. An API bill grows with every request, a rented instance costs the same whether it is busy or idle, so below the break-even the API is cheaper and above it the fleet is. Give a model, a workload and either an instance from the compute catalogue (instanceType) or an hourly rate of your own (hourlyRate). The answer costs the fleet at every rate the catalogue lists (on demand, spot, 1- and 3-year commitments) against the API at list price, at the optimized price with caching and batch, and at the cheapest host known, and compares them at your volume. Use it for 'at what volume does self-hosting Llama 3.3 70B on a g6e.4xlarge pay off' or 'is our 2M requests a month enough to rent our own GPUs'. It assumes neither a throughput nor an operations cost. Without outputTokensPerSecond each break-even states the output tokens per second the fleet would have to sustain around the clock; with it, the share of the fleet's capacity that volume uses, and whether it is out of reach. Without monthlyOperationsCost the fleet figures are hardware rent only: engineer time, storage, egress and failover are left out, and the text says so. It does not check that the instance can hold the model, and it reports whether the model's weights are published at all. Figures are final as returned and dated by `provenance` (API prices) and `computeProvenance` (instance rates).	API or own capacity: the monthly volume at which renting GPU instances costs the same as paying a model's API per token. An API bill grows with every request, a rented instance costs the same whether it is busy or idle, so below the break-even the API is cheaper and above it the fleet is. Give a model, a workload and either an instance from the compute catalogue (instanceType) or an hourly rate of your own (hourlyRate). The answer costs the fleet at every rate the catalogue lists (on demand, spot, 1- and 3-year commitments) against the API at list price, at the optimized price with caching and batch, and at the cheapest host known, and compares them at your volume. Use it for 'at what volume does self-hosting Llama 3.3 70B on a g6e.4xlarge pay off' or 'is our 2M requests a month enough to rent our own GPUs'. It assumes neither a throughput nor an operations cost. Without outputTokensPerSecond each break-even states the output tokens per second the fleet would have to sustain around the clock; with it, the share of the fleet's capacity that volume uses, and whether it is out of reach. Without monthlyOperationsCost the fleet figures are hardware rent only: engineer time, storage, egress and failover are left out, and the text says so. It does not check that the instance can hold the model, and it reports whether the model's weights are published at all. Figures are final as returned and dated by `provenance` (API prices) and `computeProvenance` (instance rates).	No	Yes	Not declared	Yes
`recommend-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. A model whose maker has announced its retirement is never recommended: it is left out, counted in `retiredExcludedCount`, and the ones that would have ranked are named in `retiredNotRecommended` with the notice. The ranking is computed server-side and is final.	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. A model whose maker has announced its retirement is never recommended: it is left out, counted in `retiredExcludedCount`, and the ones that would have ranked are named in `retiredNotRecommended` with the notice. The ranking is computed server-side and is final.	No	Yes	Not declared	Yes
`track-llm-price-changes`What moved. Every other tool here quotes today's price; this one says whether it has just changed, from the price archive optimtoken.optimnow.io keeps (its first day is `provenance.trackingStart`). Without `models` it returns the moves of the last 7 days across the catalogue: each model whose quoted price moved by 1% or more, with the price before and after and what the move does to a workload's monthly bill. With `models` (1-4 names) it returns each model's quoted price since tracking began, change by change. Use it for 'did any LLM price change this week', 'which models got cheaper', 'has the price of X moved since the summer', 'is this price stable enough to budget on'. Read the flags: most models are sold by several hosts and one of them is quoted, so a move flagged `hostSwitch` is a change of the host quoted, not a repricing, and `outlierHost` is an outlier listing. The moves with no flag are returned apart, as `priceChanges`; no flag means none was established, which is not proof that the model maker repriced. There is no fallback for this data: when the archive cannot be read the tool returns an error, never an empty list that would read as 'nothing moved'.	What moved. Every other tool here quotes today's price; this one says whether it has just changed, from the price archive optimtoken.optimnow.io keeps (its first day is `provenance.trackingStart`). Without `models` it returns the moves of the last 7 days across the catalogue: each model whose quoted price moved by 1% or more, with the price before and after and what the move does to a workload's monthly bill. With `models` (1-4 names) it returns each model's quoted price since tracking began, change by change. Use it for 'did any LLM price change this week', 'which models got cheaper', 'has the price of X moved since the summer', 'is this price stable enough to budget on'. Read the flags: most models are sold by several hosts and one of them is quoted, so a move flagged `hostSwitch` is a change of the host quoted, not a repricing, and `outlierHost` is an outlier listing. The moves with no flag are returned apart, as `priceChanges`; no flag means none was established, which is not proof that the model maker repriced. There is no fallback for this data: when the archive cannot be read the tool returns an error, never an empty list that would read as 'nothing moved'.	No	Yes	Not declared	Yes

Details

Registry status: Active
Published: Nov 12, 2026
Updated: Nov 12, 2026
Website: https://optimtoken.optimnow.io/mcp⁠ (opens in a new tab)
Repository: https://github.com/OptimNow/ai-pricing-hub-mcp⁠ (opens in a new tab)
Categories (suggested): AI models Cloud and infrastructureSuggested categories are assigned automatically from the registry text and may be wrong. Verified operators can correct them.
GDPR: Not declared

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