Maximize your ROI for Azure OpenAI
In this article, we will explore the different pricing structures, deployment options, and tools that facilitate scalable, budget-friendly AI implementations.
When diving into AI development, every choice counts—particularly in terms of costs. Whether you’re just beginning your journey or expanding large-scale applications, the last thing you want is erratic pricing or inflexible systems hindering your progress. Azure OpenAI has been crafted keeping this in mind: it’s adaptable enough for initial trials, robust enough for international rollouts, and financially aligned with your usage.
From fledgling startups to Fortune 500 companies, over 60,000 customers have opted for Azure AI Foundry—not only for its access to foundational and reasoning models, but also because it resonates with their unique needs, offering deployment choices and pricing models that cater to genuine business requirements. This journey is about more than simply harnessing AI; it’s about fostering innovation that is sustainable, scalable, and accessible.
Flexible Pricing Models to Suit Your Requirements
Azure OpenAI features three distinct pricing models tailored to cater to different workload needs and business scenarios:
- Standard—Ideal for irregular or variable workloads where you only pay for what you consume.
- Provisioned—Best for high-volume, performance-critical applications that demand a consistent output.
- Batch—Designed for large-scale tasks that can be processed asynchronously at reduced costs.
All these options are designed for scalability, allowing you to start with a proof of concept and progress to business-wide deployment with ease.
Standard
The Standard model is perfect for teams seeking flexibility. You’ll be charged per API call based on the number of tokens you use, which helps keep costs down during slower usage periods.
Best suited for: Development, prototyping, or production tasks with fluctuating demands.
You can select from the following:
- Global Deployments: To ensure minimal latency across various regions.
- OpenAI Data Zones: For enhanced control over data privacy and residency.
Regardless of your deployment choice, your data is securely stored within the chosen Azure region.
Batch
- The Batch model focuses on efficient, large-scale inference. Jobs are submitted and processed asynchronously, with responses delivered within 24 hours at a price point that can be up to 50% lower than Global Standard pricing. Batch also provides support for large workloads to process bulk requests cost-effectively, allowing you to handle extensive datasets quickly.
Best suited for: High-volume tasks with flexible timing requirements.
Common applications include:
- Extensive data processing and content generation.
- Data transformation workflows.
- Assessment of models across large datasets.
Customer Spotlight: Ontada
Ontada, a subsidiary of McKesson, harnessed the Batch API to convert over 150 million oncology documents into structured insights. By employing LLMs across 39 types of cancer, they accessed 70% of previously unavailable data and reduced their document processing time by 75%. Discover more in the Ontada case study.
Provisioned
The Provisioned model grants dedicated throughput through Provisioned Throughput Units (PTUs). This option provides dependable latency and high performance—ideal for production needs necessitating real-time capabilities or extensive processing. You can commit hourly, monthly, or yearly, with applicable discounts.
Best suited for: Enterprise tasks with predictable demand requiring consistent performance.
Examples of use cases include:
- High-volume data retrieval and document processing tasks.
- Call centre operations during peak traffic hours.
- Retail assistance requiring steady throughput.
Customer Highlights: Visier and UBS
- Visier developed “Vee,” a generative AI tool that serves up to 150,000 users each hour. Using PTUs, Visier enhanced response times by three-fold when compared to traditional pay-as-you-go models while reducing costs at scale. Read the case study.
- UBS crafted ‘UBS Red’, a secure AI platform catering to 30,000 employees in multiple regions. The use of PTUs enabled the bank to maintain reliable performance with deployments tailored to Switzerland, Hong Kong, and Singapore. Read the case study.
Deployment Options for Standard and Provisioned Models
To address evolving needs around control, compliance, and cost-efficiency, Azure OpenAI supports several deployment types:
- Global: Offers the most economical option, routing requests through the global Azure framework while ensuring data residency.
- Regional: Processes data within a specific Azure region (currently 28 available), with both processing and data residency maintained in the selected locality.
- Data Zones: Acts as a middle ground—processing occurs within geographic zones (E.U. or U.S.) for enhanced compliance without the full regional cost burden.
Global and Data Zone deployments are accessible across Standard, Provisioned, and Batch models.
Dynamic Features for Cost Reduction and Performance Enhancement
Multiple innovative features have now been introduced to help you maximise results while minimising expenditure.
- Model Router for Azure AI Foundry: An AI chat model that automatically chooses the most suitable underlying chat model to respond to a prompt. Optimised for various applications, the model router ensures high performance while reducing computing costs whenever feasible, consolidated within a single model deployment.
- Batch Large Scale Workload Support: Enables bulk request handling at lower costs. Effortlessly manage extensive workloads to expedite processing time, aiming for a 24-hour turnaround at up to 50% less than global standard rates.
- Provisioned Throughput Dynamic Spillover: Offers seamless overflow management for high-performance applications on provisioned setups. Handle traffic surges without affecting service quality.
- Prompt Caching: This built-in enhancement is designed for frequently used prompt patterns, speeding up response timings, boosting throughput, and significantly cutting token costs.
- Azure OpenAI Monitoring Dashboard: Constantly evaluates performance, usage, and reliability throughout your deployments.
To explore these features further and learn how to leverage the latest advancements in Azure AI Foundry models, catch this session from Build 2025 on optimising Generative AI applications at scale.
Alongside flexible pricing and deployment options, Azure OpenAI integrates seamlessly with Microsoft Cost Management tools, offering teams insight and control over their AI expenses.
Key features include:
- Real-time cost insights.
- Budget creation and notifications.
- Support for multi-cloud ecosystems.
- Cost tracking and chargeback systems by team, project, or department.
These tools foster collaboration between finance and engineering teams, enabling better understanding of usage trends, tracking of optimisations, and preventing unwelcome surprises.
Seamless Integration with the Azure Ecosystem
Azure OpenAI is part of an expansive ecosystem that enhances the creation of, adjustments to, and management of AI solutions.
This integration streamlines the entire lifecycle of AI solution building, thereby avoiding the need for disparate platforms, leading to quicker time-to-market and fewer operational challenges.
A Reliable Foundation for Enterprise AI
Microsoft is dedicated to fostering secure, private, and safe AI solutions. This commitment is evident not just in policy but also in product offerings:
- Secure Future Initiative: A thorough security-by-design methodology.
- Responsible AI Principles: Implemented across tools, resources, and deployment workflows.
- Enterprise-Grade Compliance: Encompassing data residency, access controls, and auditing mechanisms.
