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There is no single “cheapest cloud” for AI. Oracle Cloud Infrastructure (OCI) can meter inference by characters, transactions, tokens, or dedicated AI-unit hours, while AWS Bedrock and Google Vertex AI generally publish model-token rates and Microsoft Foundry combines several Azure services with different meters. A valid comparison must hold the model, region, endpoint, input/output mix, context length, capacity model, and optional features constant.
Why cloud AI prices are difficult to compare
Cloud providers do not sell one interchangeable unit called an “AI request.” The bill may be based on input tokens, output tokens, characters, transactions, GPU or AI-unit hours, grounding queries, or a combination of these. Even when two services publish a price per million tokens, the numbers are not comparable unless they refer to the same model family, endpoint geography, context tier, and service level.
Oracle’s own documentation describes two broad choices: “In OCI Generative AI, you can pay for on-demand inferencing or dedicated AI clusters.” It also says, “You pay as you go.” Those choices sit alongside hosted models with token prices and other OCI AI services with transaction-based meters. AWS, Azure, and Google Cloud have similarly important distinctions between standard, batch, priority, provisioned, regional, and global offerings.
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The figures below are public list prices and service descriptions identified for 2026. They are not negotiated enterprise rates, committed-use discounts, taxes, network charges, or a jointly tested total-cost benchmark. Provider pages change frequently, so every estimate should be rechecked for the model version, region, endpoint, and date you intend to deploy.
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At-a-glance comparison
| Provider | Primary published meter | Important pricing choices | What is established publicly |
|---|---|---|---|
| Oracle Cloud Infrastructure Generative AI | Characters or transactions for some offerings; tokens for hosted models; AI-unit hours for dedicated capacity | On-demand versus dedicated AI clusters; model-specific rates; minimum metric hours for dedicated OpenAI capacity | gpt-oss-120b: $0.1500 per 1M input tokens and $0.6000 per 1M output tokens. gpt-oss-20b: $0.0700 input and $0.3000 output per 1M tokens. Dedicated OpenAI: $1.00 per AI-unit hour with a 744-hour metric minimum. |
| AWS Bedrock | Model-specific input and output tokens | Standard on-demand, batch, flex, priority, and other model or capacity options | Select foundation-model batch inference is 50% below on-demand; priority is 75% above standard; flex and batch are 50% below standard, according to AWS’s published guidance. |
| Microsoft Foundry (Azure) | Service-specific Azure meters | Exact model, deployment mode, region, tier, and included Azure AI services determine the bill | Foundry is Microsoft’s platform for generative-AI solutions and custom copilots, incorporating Azure AI services and Azure OpenAI. A single Foundry-wide token price is not established. |
| Google Vertex AI | Model-specific input and output tokens plus feature meters | Global versus non-global endpoints; Gemini model and context tier; grounding and other add-ons | Google publishes separate global and non-global Gemini rates. Grounding with Google Search/Web Grounding includes 5,000 queries per month, then costs $14 per 1,000 additional queries. |
Oracle Cloud AI pricing
On-demand inference
On-demand OCI Generative AI inference is usage-based. Depending on the model or API, Oracle exposes character-length or transaction-style pricing rather than one universal token meter. Oracle’s public highlights include per-10,000-transaction prices for Cohere and Llama models. Character-based billing can produce a very different result from token billing when prompts contain code, non-Latin scripts, long identifiers, or other text that tokenizes inefficiently.
For a transaction-priced endpoint, multiply the number of billable transactions by the published price per 10,000 and divide by 10,000. For a character-priced endpoint, count the characters that Oracle defines as billable and apply that endpoint’s rate. Do not substitute a token estimate unless the particular model’s price list explicitly uses tokens.
Hosted models priced by tokens
Oracle’s 2026 global price list lists these OCI Generative AI OpenAI hosted-model rates:
| Model | Input price | Output price | Example cost for 1M input + 250K output tokens |
|---|---|---|---|
| OpenAI gpt-oss-120b | $0.1500 per 1M input tokens | $0.6000 per 1M output tokens | $0.30: $0.15 input + $0.15 output |
| OpenAI gpt-oss-20b | $0.0700 per 1M input tokens | $0.3000 per 1M output tokens | $0.145: $0.07 input + $0.075 output |
The worked costs are arithmetic illustrations using the listed rates, not a performance or quality comparison. They exclude grounding, tool calls, embeddings, storage, networking, taxes, and any commitment or enterprise agreement.
Dedicated AI clusters and AI-unit hours
Dedicated capacity changes the question from “How many tokens did this request use?” to “How many capacity hours did I reserve?” Oracle lists OCI Generative AI OpenAI Dedicated at $1.00 per AI-unit hour and specifies a 744-hour metric minimum. At one AI unit for the full metric minimum, the arithmetic is 744 × $1.00 = $744 before other charges. The minimum is a billing condition, not a promise that every model, region, or deployment needs exactly one unit; verify the capacity quantity and applicable region in the current price list.
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Dedicated clusters can make sense when predictable throughput, isolation, or latency matters more than minimizing the cost of occasional requests. For intermittent workloads, an hourly minimum can outweigh a low per-unit rate. Compare expected utilization against the reserved hours rather than comparing $1.00 directly with a token price.
Do not confuse AI Data Platform Workbench units with inference prices
Oracle also publishes a separate AI Data Platform Workbench measure of 4,110 AIDP Units per NVIDIA GPU-hour. That figure describes a Workbench platform resource meter, not the OCI Generative AI token or AI-unit prices above. It should not be inserted into an API inference calculation.
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AWS Bedrock publishes model-specific input and output prices per 1 million tokens. A Bedrock estimate therefore needs the exact foundation model, AWS Region, prompt and completion volumes, and any cached-input or other model-specific tier shown on the model’s pricing page. Comparing a smaller, faster model on Bedrock with a larger frontier model on OCI would answer a different question from comparing equivalent capabilities.
Discount and priority modes
AWS documents several service-level choices that materially change the rate:
- Batch inference: select foundation models are priced 50% below on-demand.
- Flex and batch: AWS describes these as 50% below standard for the applicable offerings.
- Priority: AWS describes priority inference as 75% above standard.
These percentages apply to the qualifying Bedrock options AWS identifies, not automatically to every model or request. Confirm eligibility, latency behavior, throughput guarantees, and the region before applying a discount to a forecast.
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Microsoft Foundry and Azure AI pricing
Microsoft Foundry is a platform for building generative-AI solutions and custom copilots. It brings together Azure AI services and Azure OpenAI, so “Foundry pricing” is not one universal meter. The bill depends on the exact model, deployment mode, Azure region, tier, and services attached to the application.
What to specify in an Azure comparison
- Model and version: different models can have different input, output, cached-input, or context-window rates.
- Deployment mode: pay-per-token and provisioned or reserved capacity have different economics.
- Region: Azure prices and model availability can vary by geography.
- Supporting services: search, content safety, orchestration, storage, monitoring, and network transfer can add separate meters.
Because the available figures do not establish one current Foundry-wide rate, a responsible table cell is “not stated” until those variables are fixed. Use the Azure pricing page for the selected model and deployment rather than treating Foundry as a direct equivalent of a single-token API.
Google Vertex AI pricing
Vertex AI publishes Gemini prices per 1 million tokens and separates global endpoint rates from non-global endpoint rates. The endpoint choice is therefore part of the price, not merely a routing detail. Record whether requests use a global endpoint or a specific geography before comparing Vertex with a regional OCI, Bedrock, or Azure deployment.
Grounding and Google Search
Grounding with Google Search or Web Grounding carries a separate query meter. Google includes 5,000 grounding queries per month; after that allowance, the published charge is $14 per 1,000 queries. This allowance is not a replacement for model-token charges: a grounded request can consume both Gemini input/output tokens and grounding queries, along with any other enabled feature meters.
Worked comparison: 1 million input and 250,000 output tokens
Use a fixed workload before asking which provider is cheaper. The example below assumes one model family, one region or endpoint, no discounts, no grounding, no tools, no cached-input adjustment, and 1,000,000 input tokens plus 250,000 output tokens.
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| Provider or option | Calculation | Result or required data |
|---|---|---|
| OCI gpt-oss-120b | (1.0 × $0.1500) + (0.25 × $0.6000) | $0.30 |
| OCI gpt-oss-20b | (1.0 × $0.0700) + (0.25 × $0.3000) | $0.145 |
| AWS Bedrock | (input millions × model input rate) + (output millions × model output rate) | Not stated without the selected Bedrock model, region, and applicable tier. |
| Microsoft Foundry | Apply the selected Azure model/deployment meters to the same token volumes | Not stated without model, deployment mode, region, and tier. |
| Google Vertex AI | (input millions × Gemini input rate) + (output millions × Gemini output rate) | Not stated without Gemini model and global or non-global endpoint. |
For any token-priced service, keep input and output separate: output is often more expensive, and a change in answer length can dominate the bill. If a provider offers cached-input pricing, calculate cached and uncached tokens as separate line items. If the OCI endpoint uses characters or transactions instead, this token workload cannot be translated reliably without the endpoint’s conversion and billing definition.
How to make an apples-to-apples cost model
- Choose equivalent capability. Match model family, reasoning or multimodal features, context window, and quality target. A small model’s lower price is not evidence that the platform is cheaper for a frontier-model requirement.
- Fix geography and endpoint. Write down the cloud region and, for Vertex AI, global versus non-global endpoint. Check regional availability and data-residency requirements.
- Measure the traffic. Record requests, input tokens, output tokens, cached-input tokens, characters, or transactions as the selected service defines them. Include peak and average rates.
- Separate capacity choices. Price on-demand, batch or flex, priority, and dedicated or provisioned options independently. Apply minimum-hour rules and commitments to the period in which they actually apply.
- Add feature meters. Include grounding, search, tool calls, embeddings, reranking, safety filters, storage, logging, and network transfer when your architecture uses them.
- Calculate scenarios. Build low, expected, and peak cases. A low-utilization dedicated cluster may cost more than on-demand, while a consistently busy application may benefit from reserved capacity or batch processing.
- Record the price date. Save the model version, endpoint, region, list-price page date, discount tier, and any contract terms. Recheck after provider price or model changes.
Common pricing mistakes and fixes
Using one token price for all OCI models
OCI mixes character, transaction, token, and AI-unit meters. Fix the mistake by identifying the endpoint’s billing unit first, then use that unit’s official rate.
Comparing model names instead of capability
“AI API” does not describe quality, context length, latency, or tool support. Match the model and workload before comparing dollars.
Ignoring output volume
A prompt-heavy application and a response-heavy application can have very different bills. Forecast input and output separately and test response-length controls.
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Applying a discount universally
AWS’s 50% batch or flex reduction and 75% priority increase apply to qualifying offerings. Confirm the model, region, and scheduling or latency conditions before using them in a budget.
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Leaving add-ons out of the total
Grounding, for example, can add Google Search/Web Grounding queries on top of Vertex token charges. The same principle applies to retrieval, tool execution, storage, and networking elsewhere.
Treating list prices as a contract quote
Public list prices exclude negotiated enterprise terms, credits, taxes, and potentially separate infrastructure charges. Keep a list-price model and a contract-price model separate.
Which cloud is cheapest?
There is no defensible universal winner from the published information. OCI’s listed gpt-oss rates produce a transparent example of $0.30 for 1 million input plus 250,000 output tokens on gpt-oss-120b, and $0.145 on gpt-oss-20b, but those figures do not establish equal quality or equal capability against a Bedrock, Foundry, or Vertex model. AWS may be attractive when an eligible batch or flex workload can use the documented 50% reduction; priority service costs more for latency-sensitive work. Vertex’s total depends on Gemini endpoint geography and any grounding queries. Foundry requires an Azure model and deployment choice before a number exists. OCI dedicated capacity can be economical at sustained utilization but includes a 744-hour minimum for the cited dedicated OpenAI metric.
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Quick Recap
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