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This weblog breaks down the out there pricing and deployment choices, and instruments that assist scalable, cost-conscious AI deployments.
When you’re constructing with AI, each choice counts—particularly in the case of value. Whether you’re simply getting began or scaling enterprise-grade purposes, the very last thing you need is unpredictable pricing or inflexible infrastructure slowing you down. Azure OpenAI is designed with that in thoughts: versatile sufficient for early experiments, highly effective sufficient for world deployments, and priced to match the way you really use it.
From startups to the Fortune 500, greater than 60,000 clients are selecting Azure AI Foundry, not only for entry to foundational and reasoning fashions—however as a result of it meets them the place they’re, with deployment choices and pricing fashions that align to actual enterprise wants. This is about extra than simply AI—it’s about making innovation sustainable, scalable, and accessible.
This weblog breaks down the out there pricing and deployment choices, and instruments that assist scalable, cost-conscious AI deployments.
Flexible pricing fashions that match your wants
Azure OpenAI helps three distinct pricing fashions designed to satisfy totally different workload profiles and enterprise necessities:
- Standard—For bursty or variable workloads the place you need to pay just for what you employ.
- Provisioned—For high-throughput, performance-sensitive purposes that require constant throughput.
- Batch—For large-scale jobs that may be processed asynchronously at a reduced price.
Each strategy is designed to scale with you—whether or not you’re validating a use case or deploying throughout enterprise models.
Standard
The Standard deployment mannequin is good for groups that need flexibility. You’re charged per API name based mostly on tokens consumed, which helps optimize budgets in periods of decrease utilization.
Best for: Development, prototyping, or manufacturing workloads with variable demand.
You can select between:
- Global deployments: To guarantee optimum latency throughout geographies.
- OpenAI Data Zones: For extra flexibility and management over knowledge privateness and residency.
With all deployment picks, knowledge is saved at relaxation throughout the Azure chosen area of your useful resource.
Batch
- The Batch mannequin is designed for high-efficiency, large-scale inference. Jobs are submitted and processed asynchronously, with responses returned inside 24 hours—at as much as 50% lower than Global Standard pricing. Batch additionally options giant scale workload assist to course of bulk requests with decrease prices. Scale your large batch queries with minimal friction and effectively deal with large-scale workloads to cut back processing time, with 24-hour goal turnaround, at as much as 50% much less value than world commonplace.
Best for: Large-volume duties with versatile latency wants.
Typical use circumstances embrace:
- Large-scale knowledge processing and content material era.
- Data transformation pipelines.
- Model analysis throughout in depth datasets.
Customer in motion: Ontada
Ontada, a McKesson firm, used the Batch API to rework over 150 million oncology paperwork into structured insights. Applying LLMs throughout 39 most cancers varieties, they unlocked 70% of beforehand inaccessible knowledge and minimize doc processing time by 75%. Learn extra within the Ontada case examine.
Provisioned
The Provisioned mannequin supplies devoted throughput through Provisioned Throughput Units (PTUs). This permits steady latency and excessive throughput—very best for manufacturing use circumstances requiring real-time efficiency or processing at scale. Commitments might be hourly, month-to-month, or yearly with corresponding reductions.
Best for: Enterprise workloads with predictable demand and the necessity for constant efficiency.
Common use circumstances:
- High-volume retrieval and doc processing situations.
- Call heart operations with predictable site visitors hours.
- Retail assistant with constantly excessive throughput.
Customers in motion: Visier and UBS
- Visier constructed “Vee,” a generative AI assistant that serves as much as 150,000 customers per hour. By utilizing PTUs, Visier improved response instances by 3 times in comparison with pay-as-you-go fashions and diminished compute prices at scale. Read the case examine.
- UBS created ‘UBS Red’, a safe AI platform supporting 30,000 staff throughout areas. PTUs allowed the financial institution to ship dependable efficiency with region-specific deployments throughout Switzerland, Hong Kong, and Singapore. Read the case examine.
Deployment varieties for normal and provisioned
To meet rising necessities for management, compliance, and price optimization, Azure OpenAI helps a number of deployment varieties:
- Global: Most cost-effective, routes requests by way of the worldwide Azure infrastructure, with knowledge residency at relaxation.
- Regional: Keeps knowledge processing in a selected Azure area (28 out there as we speak), with knowledge residency each at relaxation and processing within the chosen area.
- Data Zones: Offers a center floor—processing stays inside geographic zones (E.U. or U.S.) for added compliance with out full regional value overhead.
Global and Data Zone deployments can be found throughout Standard, Provisioned, and Batch fashions.
Dynamic options provide help to minimize prices whereas optimizing efficiency
Several dynamic new options designed that can assist you get the perfect outcomes for decrease prices at the moment are out there.
- Model router for Azure AI Foundry: A deployable AI chat mannequin that robotically selects the perfect underlying chat mannequin to answer a given immediate. Perfect for various use circumstances, mannequin router delivers excessive efficiency whereas saving on compute prices the place doable, all packaged as a single mannequin deployment.
- Batch giant scale workload assist: Processes bulk requests with decrease prices. Efficiently deal with large-scale workloads to cut back processing time, with 24-hour goal turnaround, at 50% much less value than world commonplace.
- Provisioned throughput dynamic spillover: Provides seamless overflowing to your high-performing purposes on provisioned deployments. Manage site visitors bursts with out service disruption.
- Prompt caching: Built-in optimization for repeatable immediate patterns. It accelerates response instances, scales throughput, and helps minimize token prices considerably.
- Azure OpenAI monitoring dashboard: Continuously observe efficiency, utilization, and reliability throughout your deployments.
To study extra about these options and find out how to leverage the most recent improvements in Azure AI Foundry fashions, watch this session from Build 2025 on optimizing Gen AI purposes at scale.
Beyond pricing and deployment flexibility, Azure OpenAI integrates with Microsoft Cost Management instruments to present groups visibility and management over their AI spend.
Capabilities embrace:
- Real-time value evaluation.
- Budget creation and alerts.
- Support for multi-cloud environments.
- Cost allocation and chargeback by group, mission, or division.
These instruments assist finance and engineering groups keep aligned—making it simpler to grasp utilization traits, observe optimizations, and keep away from surprises.
Built-in integration with the Azure ecosystem
Azure OpenAI is an element of a bigger ecosystem that features:
This integration simplifies the end-to-end lifecycle of constructing, customizing, and managing AI options. You don’t need to sew collectively separate platforms—and meaning sooner time-to-value and fewer operational complications.
A trusted basis for enterprise AI
Microsoft is dedicated to enabling AI that’s safe, non-public, and secure. That dedication reveals up not simply in coverage, however in product:
- Secure future initiative: A complete security-by-design strategy.
- Responsible AI rules: Applied throughout instruments, documentation, and deployment workflows.
- Enterprise-grade compliance: Covering knowledge residency, entry controls, and auditing.