Bedrock, Vertex, Azure or direct: what the channel changes

Published 2026-09-06 · Prose last reviewed 2026-10-20 · Figures recomputed nightly from the 2026-10-22 catalogue snapshot

A FinOps guide by OptimNow · prices from the live OptimToken catalogue

Most models on a shortlist are sold in more than one place. Claude is on Anthropic's own API, on AWS Bedrock and on Google Vertex. GPT-5.6 is on OpenAI's API, on Azure AI Foundry and, since 2026, on Bedrock. This guide calls each of those places a channel. Procurement starts by asking which channel is cheapest. Across the models the hub tracks, that question has a dull answer, and the differences worth negotiating are elsewhere.

How many channels sell a model

The hub keeps a hand-checked list of where each model is sold. It covers 70 of the 316 models in the catalogue. By channel count, 14 are sold through 5 channels, 18 through 4, 27 through 3, 11 through 2. The list says where a model can be bought, and nothing about the price there.

Of the 70, 6 have no direct API from their model maker. Llama 4 Maverick, Llama 4 Scout, Nova Premier 1.0, Nova Micro 1.0 and 2 more are sold only through other channels. Meta publishes Llama weights and runs no service of its own, and Amazon sells Nova through Bedrock alone, so for those a cloud is the only shop.

The price does not move

Every night the hub reads the rate each host publishes for a model. A host is a company that runs the model and sells it by the token: the model maker, a cloud platform like Amazon Bedrock, Azure or Vertex, or an independent host. For 40 models both the model maker and at least one of those clouds publish a rate, which gives 89 comparisons. Of those, 79 are identical, input and output, to the cent.

Take Claude Fable 5, sold through 5 channels and priced by 5 hosts on 2026-10-22:

Moving that workload from the model maker to a cloud saves nothing on the rate. The clouds charge the model maker's own rate. Every cloud does the same, to the cent, for 29 of the 39 proprietary models that the model maker and at least one cloud both price.

The 10 comparisons that do differ deserve a read before anyone concludes that a cloud marks the model up. Of them, 10 sit at exactly 10% above the model maker's rate. AWS documents a 10% premium on regional endpoints for recent Claude models, against its global ones. So the likeliest reading is an endpoint choice, not a markup. The hub cannot see which endpoint each row prices, so treat that as a lead to check.

Where the host is the whole price decision

That flat price is a feature of proprietary models. Split the 70 by licence and the two halves behave differently. The 54 proprietary models have a median of 2 hosts and a median gap of 1.00x between the cheapest and the dearest. The 13 open-weights and open-source models have a median of 5 hosts and a median gap of 2.35x. One model maker setting a price is a rate card. More than one host serving the same downloadable weights is a market, and a market has a spread.

DeepSeek V3.2 is the widest case: 12 hosts, from $0.2794 per 1M tokens at GMICloud to $4.05 at SambaNova. That is 14.5x for the same weights, and here the host decides the bill. The cheap end is not free of catches, because hosts differ on quantization, context limit and throughput, so test the cheapest one on your own prompts before you budget on it. Compare the two examples side by side.

Command A is the opposite case: sold through 4 channels, and exactly one of them publishes a price the hub can read. A channel can carry a model without telling you what it costs there. Four logos on a slide are not four quotes.

What does move: where the model runs

A model bought through a cloud runs only where that cloud serves it, and the map differs by model. On Bedrock, Nova Lite 1.0 is served in 13 regions and Nova Premier 1.0 in 3. The checked list places 39 models on Bedrock; 5 of them (Mistral Medium 3.5, Command A, Command R+ (08-2024) and others) are not in AWS's regional model list, so they have no regions to show and are left out below. Of the 34 models with regions, 5 have no European region at all: DeepSeek V3.2 and Kimi K2.5 and Llama 4 Maverick. Newer models start narrow: the 19 released in the last 6 months sit at a median of 7 regions, against 11 for the rest.

A model can win the benchmark and fit the budget and still be unusable for a workload that must keep its data in Europe. A team that finds this out at deployment, after the shortlist has closed, has nothing left to swap in. Check the map while the shortlist is still open.

Where the channels part ways: commitment terms

Same price, different regions. The third difference appears when the workload is large enough to reserve capacity. The three clouds sell reserved inference on terms that do not line up, and this is where a channel choice becomes hard to undo:

Read the top row against a model roadmap that moves every few months. A commitment locked to one model is stranded the day its successor ships, and the two prices you compared at the start were identical anyway. This is the FinOps Foundation's GenAI capacity framing, as distilled in OptimNow's practice library: commitment terms, not token rates, are where a channel decision gets expensive. Size any reservation to average load, with a spillover path for the peaks. That sizing needs a load curve, which a team at Walk or Run maturity holds and a team at Crawl does not: without one, stay pay-as-you-go until the first 90 days of usage are in.

Running the check on your own shortlist

• Compare prices within one tier. A batch or flex row against a standard row is a different product, not a channel saving.
• For a proprietary model, compare terms, not prices. Commitment scope, spillover, cost attribution and regions carry the whole difference.
• For an open-weights model, pick the host. Then test the cheap end on your own prompts, because quantization and context limits vary at the same price.
• Check regions before the shortlist closes. Residency rules eliminate models late and silently.
• Keep an exit. Three channels means you can move; one host plus a term commitment means you cannot.

Figures on this page come from the 2026-10-22 catalogue snapshot and the 2026-10-22 host prices, and rebuild nightly. The pricing table filters by Available on, and every model page lists its hosts with the rate each one publishes.

The FinOps reasoning behind this guide comes from OptimNow's open-source practice library: github.com/OptimNow/cloud-finops-skills. If you want a channel and commitment review run against your own inference usage, OptimNow does that work.

Data snapshot: 2026-10-22 · Compare all models · Cloud compute pricing · All guides