Top Questions to Ask Microsoft During Your Azure OpenAI Negotiation
Executive Summary:
Negotiating an Azure OpenAI agreement is a high-stakes endeavor for enterprises. The goal is to secure favorable pricing and terms while ensuring the service aligns with your organization’s compliance, security, and business needs.
This advisory outlines the top questions and tactics to bring to your Microsoft Azure OpenAI negotiation – from cost forecasting and discounts to legal and data protections – so you can optimize spend and minimize risk.
Understand Azure OpenAI Pricing to Forecast Costs
Azure OpenAI Service uses a consumption-based pricing model, charging per API usage (tokens) with different rates for each model.
For example, advanced models like GPT-4 cost an order of magnitude more per 1,000 tokens than GPT-3.5. These fees can add up quickly with enterprise-scale usage.
Start by estimating your expected workloads: How many prompts and responses might you generate monthly, and which models will you use?
This forecasting is crucial – it enables you to anticipate costs and determine the optimal pricing approach.
Ask Microsoft:
“Can you provide pricing for our specific use cases? What would our monthly spend be if we send X requests or Y million tokens to GPT-4 versus GPT-3.5?”
Keep in mind that usage patterns drive cost: longer prompts or chats consume more tokens, and broad deployments across business units multiply usage. If you plan a pilot, track token consumption closely to project full-scale needs.
Also consider mixing models to optimize spend – use the expensive model (e.g., GPT-4) only for high-value tasks and rely on cheaper models (like GPT-3.5 or embeddings) for routine queries. Azure doesn’t offer a free tier beyond any trial credits, so implement cost controls from day one. Set up Azure cost alerts and budgets to prevent surprises.
Ask Microsoft:
“What tools or best practices can help us monitor and cap our Azure OpenAI spending? Can we get a detailed breakdown of costs (compute, networking, storage) associated with the service?” This ensures you have transparency into where every dollar goes.
Negotiate Volume Discounts and Commitments
Treat Azure OpenAI like any other strategic cloud service when negotiating price; your usage volume is a lever.
Microsoft’s list prices are a starting point.
If you expect significant consumption, push for enterprise discounts or credits.
For instance, if your forecast is $ 50,000 or more per month in Azure OpenAI charges, you can request a custom rate card (e.g., a percentage off the standard per-token rate) or tiered pricing (lower prices per token after reaching certain usage thresholds).
Microsoft often rewards committed cloud spending: consider integrating Azure OpenAI into your existing Azure Enterprise Agreement (EA) or cloud commitment. Ensure any OpenAI usage will count toward your pre-paid Azure credits or minimum spend—so you’re not paying extra on top of your committed budget.
Ask Microsoft:
“Can our Azure OpenAI consumption be included under our Azure commitment for discounting? What discount level can we get at our projected usage, and are there promotional credits for AI initiatives?”
If you aren’t ready to commit upfront, you can start with pay-as-you-go, with no obligation, and gather usage data.
But even then, let Microsoft know that as usage grows, you’ll negotiate formal terms – this sets the expectation that you won’t simply pay sticker price forever. On the other hand, avoid overcommitting too early.
A common pitfall is committing to a large, multi-year expenditure before fully understanding demand or before new model alternatives emerge. Strike a balance between cost savings and flexibility.
One strategy is to enter into a short initial term or pilot agreement with an option to renegotiate after 6–12 months, based on actual usage.
During negotiation, also inquire about free trial credits or funding for AI projects.
Microsoft often has programs to subsidize initial Azure OpenAI experiments (e.g., offering a few thousand dollars in credits) – leverage these to reduce early costs.
To illustrate how different pricing arrangements impact cost and flexibility, consider the following approaches:
| Approach | Cost Basis | Negotiation Leverage | Flexibility (Lock-In Risk) |
|---|---|---|---|
| Pay-as-You-Go (no commitment) | Standard rate per token (published prices). Pay only for actual usage each month. | Low leverage by default (pay list price), but easy to start/stop usage at will. | High flexibility, minimal lock-in. You can scale down or leave at any time (contractually), though app integration creates some practical stickiness. |
| Committed Spend (Azure EA or credits) | Pre-committed Azure spend that includes OpenAI usage, often over 1–3 years. Usage draws down against this commitment, usually at a discounted rate. | High leverage if committing significant spend. Can negotiate volume discounts, tiered rates, or bonus credits. | Moderate lock-in. You’re financially obligated to a set spend (use it or lose it). Ensure terms allow adjusting if usage patterns shift or new models change the game. |
| Custom Pricing Agreement (Multi-year deal) | Negotiated custom token pricing or fixed rates for certain usage, often via an EA amendment. May include price holds for new features. | High leverage if Azure OpenAI is strategically important. Can secure price locks or dedicated capacity guarantees. | Lower flexibility in term: you’re tied to Microsoft for the duration for those workloads. Mitigate by aligning with EA renewal and including review or exit clauses if technology or requirements evolve. |
Ask Microsoft: “What discount can we get for a committed annual spend of $X on Azure OpenAI? Can we structure a deal where, after we consume Y million tokens, the rate per token drops? Are there any one-time credits or incentives if we sign a deal this quarter?” By posing these questions, you signal that you know volume should translate to savings.
Clarify Key Contract Terms and Risks
A critical part of any Azure OpenAI negotiation is nailing down the legal terms. Don’t assume the standard online terms cover your specific needs. Ensure that all Azure OpenAI usage falls under your enterprise agreement or an equivalent Microsoft contract, rather than relying solely on the default online terms.
This gives you the benefit of negotiated protections (liability caps, data protection commitments, etc.). Ask Microsoft: “Will our use of Azure OpenAI be governed by our existing Master Agreement and Microsoft Product Terms? Can we get a contract addendum documenting any unique terms for Azure OpenAI?”
Pay special attention to intellectual property and liability clauses. Microsoft’s terms typically state that you own the input and output you generate using the AI. However, they also disclaim responsibility for the content of those outputs.
In practice, this means if the AI produces incorrect information or inadvertently similar content to a copyrighted source, Microsoft isn’t liable your company is on the hook for how you use the output.
Ask Microsoft: “Do we retain full ownership of AI-generated outputs? How does the contract address IP or errors in the AI’s responses?” Expect language that the service is provided “as-is” with no warranty that outputs are error-free or non-infringing.
To protect your enterprise, plan to vet important AI outputs internally (as you would review a junior employee’s work) and consider additional insurance or indemnities on your side if you’ll rely heavily on AI-generated content.
Examine the liability cap and indemnification in your Azure agreement and confirm Azure OpenAI isn’t carved out from those protections. Microsoft usually caps liability at a fixed amount or the fees paid – ensure that cap is acceptable given how you’ll use AI.
If the default cap is low, and you foresee high risk (e.g., using AI for critical financial decisions), attempt to negotiate it upward or get specific remedies (like stronger service credits or support commitments) in case of problems.
Complete indemnification for AI output is unlikely, but make sure Microsoft at least indemnifies you for claims involving their software or services (e.g., if the Azure OpenAI platform itself infringes someone’s IP, Microsoft will defend you).
Ask Microsoft: “What remedies or assurances do we have if Azure OpenAI causes a business disruption or if output misuses lead to a claim? Are Azure OpenAI liabilities covered under our main agreement’s cap and indemnities?”
Another contractual area to clarify is acceptable use and ethical AI obligations. Microsoft requires customers to abide by a Responsible AI Code of Conduct when using Azure OpenAI. You should explicitly understand what is permitted or prohibited.
Ask Microsoft:
“What use cases or content are off-limits under the Azure OpenAI terms? What happens if a user accidentally violates those policies?”
Typically, you must agree not to attempt to generate disallowed content (hate speech, violence, personal data abuse, etc.) and not to use the service to create AI models that compete with Microsoft.
Ensure you have an internal compliance plan: train your developers and users on these rules and establish processes to monitor usage.
One rogue use of the model could risk suspension of your service – something you want to avoid at all costs by staying within the agreed boundaries.
Ensure Data Security and Compliance
For enterprise buyers, data handling is a make-or-break issue in any Azure OpenAI deal. Azure OpenAI’s big selling point is that it keeps your data within the Azure cloud environment.
Unlike the public OpenAI API, Azure will, by default, not use your data to improve the base models – it’s isolated for your use.
Ask Microsoft:
“Can you confirm that none of our prompts or outputs will be used to train OpenAI models, and that data stays within our Azure tenant?”
The answer should be yes: by design, prompts and responses in Azure OpenAI are not fed back into OpenAI’s systems for training, and Microsoft only retains them temporarily (for service monitoring).
You should also inquire about data retention and deletion policies.
Out of the box, Azure OpenAI logs user prompts and outputs for abuse detection (typically holding them for around 30 days). If you plan to input sensitive data, you may require stricter controls.
Ask Microsoft: “Is there an option to opt out of prompt logging or have our data deleted immediately after processing? How are stored logs protected, and who can access them?” Microsoft does offer the ability for certain customers to request no data retention (no logging) for Azure OpenAI, but you’ll need to get approval for that scenario.
If data privacy is paramount (for example, in healthcare or finance), negotiate to include any needed data handling stipulations in your contract (such as processing only in certain regions or enhanced logging restrictions).
Compliance certifications and regulations are another area that needs to be covered. Azure OpenAI inherits many Azure platform certifications (GDPR compliance, ISO 27001, SOC 2, even HIPAA eligibility via a BAA when applicable). Nonetheless, verify any specific requirements your company has.
Ask Microsoft: “Does Azure OpenAI comply with [your industry standard] and can Microsoft sign the necessary data protection addendum or BAA for our usage?”
Ensure that Azure OpenAI is listed as covered in Microsoft’s Product Terms or Online Services Terms to meet all relevant compliance requirements.
You’ll also want to know the geographic residency of data: you can choose the Azure region where your OpenAI service is deployed – confirm that region aligns with your data sovereignty needs (e.g., EU data stays in EU data centers).
If multi-region redundancy is important, discuss how failover would work and whether any data would be transferred out of the primary region.
In short, pin down how your data will be handled at every stage.
This not only protects your company’s sensitive information but also ensures you meet legal obligations to customers and regulators.
Microsoft should address these concerns in the contract via its standard Data Protection Addendum and specific Azure OpenAI terms – but don’t hesitate to get written clarifications for any gray areas.
Plan for Scale, Support, and Flexibility
As you finalize your Azure OpenAI agreement, broaden the conversation to cover operational and future considerations. Service performance and support are key for any enterprise-grade solution.
Microsoft’s cloud infrastructure backs Azure OpenAI, so it comes with an uptime SLA (typically around 99.9% availability).
Verify the exact SLA and understand the remedy (usually service credits) if it’s not met.
Ask Microsoft:
“What SLA will apply to our Azure OpenAI deployment, and how do we claim compensation if there’s an outage? Do we need a certain support tier to get 24/7 critical support for the AI service?”
If your usage will be mission-critical, ensure you have an appropriate Azure support plan (e.g., Microsoft Unified/Premier support) and that your account team is aware of the importance of this service to your operations.
Consider scalability and capacity as well. During the preview phase of Azure OpenAI, some customers experienced waitlists or throttling for popular models, such as GPT-4.
In 2025, capacity is much improved, but it’s wise to ask: “Are there any usage limits or throttling we should anticipate? If we need dedicated capacity or higher throughput for peak times, what are our options?”
Microsoft offers a Provisioned Throughput mode, which essentially reserves dedicated compute resources for your OpenAI usage at a fixed cost.
This can guarantee consistent performance if you have very high volume, but you’ll pay for it whether you use it or not. It’s worth discussing whether your scenario requires it and negotiating the terms (e.g., a discounted rate for a 1-year reserved instance).
If not needed, at least have Microsoft confirm that the standard multi-tenant service will scale to your needs and what (if any) quotas might apply.
Finally, build flexibility into your negotiation for the future. The AI landscape is evolving rapidly – new models, features, and even competitors will emerge over the next couple of years.
You don’t want to be stuck in an inflexible contract if the technology or your strategy shifts.
Ask Microsoft:
“How will our agreement accommodate new OpenAI models or capabilities released on Azure? If a more powerful model (e.g., GPT-5) becomes available, can we access it under this contract and at what pricing?”
It may not be possible to lock in future model pricing now, but raising the question signals that you expect fair terms when the time comes.
Also, ensure no exclusivity clause implicitly locks you into Microsoft’s AI.
You should retain the freedom to use alternative AI services or providers in parallel if needed – Microsoft’s standard contracts won’t forbid this, but make sure any committed spend targets are reasonable so you’re not effectively prevented from exploring other options.
Consider including a “benchmark and adjust” review in a year: essentially, an agreement that you and Microsoft will revisit pricing or service terms based on market conditions (for example, if competitors offer better pricing or if your actual usage differs significantly from expected).
This type of clause can be challenging to obtain, but even an informal email commitment from your account team to reevaluate can be valuable.
At a minimum, keep your Azure OpenAI term aligned with your broader EA renewal cycles so you can renegotiate when your main Microsoft contract is up for renewal.
In summary, approach the Azure OpenAI negotiation with a clear vision of your expected usage, firm questions regarding pricing and terms, and a readiness to negotiate both savings and safeguards.
Microsoft wants marquee customers for its AI services, so use that leverage to ensure the deal meets your enterprise’s needs not just on day one, but throughout the life of the agreement.
Recommendations
- Forecast First, Then Negotiate: Do your homework on expected usage before signing. Know roughly how many tokens or API calls you’ll need monthly. This forecast strengthens your case when requesting discounts and prevents overcommitting.
- Integrate Azure OpenAI into Your EA: Wherever possible, have Azure OpenAI usage governed by your Enterprise Agreement or Cloud Commit. This brings pre-negotiated protections (like liability limits and data handling terms) and lets you apply volume discounts or credits to AI usage. Don’t settle for click-through online terms if you have an EA.
- Push for Volume Discounts and Credits: If your projected spend is substantial, request rates that exceed the default rates. Negotiate for tiered pricing (e.g., lower cost after X million tokens), free Azure credits for initial projects, or a flat discount. Microsoft has flexibility, especially if Azure OpenAI is strategically important to your organization’s Azure spend.
- Insist on Data Privacy Assurances: Get clear, written confirmation on data use and retention. For sensitive use cases, negotiate to opt out of data logging and ensure that all AI processing remains within specified regions. If required for your industry, have Microsoft sign any necessary privacy or security addenda (e.g., HIPAA Business Associate Agreement, or BAA).
- Stay Flexible on Term Length: Avoid rigid multi-year commitments whenever possible. Given the pace of AI advancement, seek shorter terms with options to extend or adjust. Align the contract end with your broader IT strategy timeline (12-24 months) so you can reassess technology and pricing.
- Establish Internal AI Governance: Before rollout, create an internal policy governing the use of Azure OpenAI. Define what data can be input, require human review of critical outputs, and train users on Microsoft’s acceptable use rules. Proactive governance will help you comply with contract terms and avoid misuse that could trigger penalties or suspension.
Checklist: 5 Actions to Take
- Gather Requirements and Usage Estimates: Convene IT, procurement, and business stakeholders to define your intended Azure OpenAI use cases. Estimate the volume of requests and identify any special requirements (e.g., data residency, specific models, uptime needs). This baseline will guide all negotiation points.
- Engage Microsoft Early for Information: Contact your Microsoft account team to discuss Azure OpenAI. Request pricing details for your projected usage and inquire about any prerequisites (for example, ensure your Azure subscription is approved for OpenAI Service access). Use this information to prepare informed questions and set expectations with Microsoft regarding the terms you intend to negotiate.
- Review Contractual Terms in Detail: Have your legal team review Microsoft’s standard Azure OpenAI terms (in the Online Services Terms/Product Terms). Flag anything concerning – such as data use language, liability caps, or usage restrictions. Come up with a list of contract questions or changes you will ask for during negotiations (e.g., clarity on who owns outputs, confirmation of data handling commitments in writing).
- Negotiate Pricing and Protections: Armed with your usage forecast and contract review, enter negotiations with Microsoft. Ask for a pricing proposal that includes volume discounts or credits based on your estimates. At the same time, negotiate key terms to ensure Azure OpenAI is included in your enterprise agreement, address any compliance gaps, and confirm support and SLA expectations. Document all concessions in the contract or as addenda.
- Plan Deployment and Cost Management: Once the agreement is nearing finalization, establish internal processes to maximize value. Implement cost-monitoring tools or Azure budgets aligned to the negotiated rates. Finalize your internal usage policy and training so employees use Azure OpenAI responsibly. Additionally, schedule periodic business reviews with Microsoft to assess service usage, spending versus value, and any necessary adjustments over time.
FAQ
Q1: How is Azure OpenAI priced, and can we obtain a better rate for high-volume usage?
A: Azure OpenAI pricing is consumption-based – you pay per model usage (measured in tokens for text models, or images for vision models). The rates are set per 1,000 tokens and vary by model (for example, GPT-4 costs much more per 1,000 tokens than GPT-3.5). For enterprise agreements, Microsoft can offer improved pricing if your usage is high. You should negotiate a volume-based discount or commit to a certain spend in exchange for lower rates. Additionally, if you have an Azure spending commitment, any Azure OpenAI charges can be deducted from that at your discounted cloud rate, effectively reducing the overall cost. In short, yes – large, predictable usage opens the door to better pricing than pay-as-you-go, but you have to ask for and negotiate it.
Q2: Will Microsoft or OpenAI use our data (prompts and outputs) for training or other purposes?
A: By default, no one big advantage of Azure OpenAI is that your data stays within Microsoft Azure and is not fed into OpenAI’s public models. Microsoft operates the service in a way that ensures your prompts and the AI’s responses are not used to improve the model. They are temporarily stored for abuse monitoring and troubleshooting, but those logs are typically deleted after a period (around 30 days). You can even request to disable this data retention if your compliance needs demand it. So, your proprietary data and any AI-generated content are confined to your environment. It’s still advisable to avoid sending highly sensitive personal or confidential data to any AI, if possible. However, Azure’s setup is designed with strong data isolation for enterprise peace of mind.
Q3: Who owns the content generated by Azure OpenAI, and are we liable if it produces bad or infringing output?
A: In general, you own the inputs and outputs of the Azure OpenAI Service. Microsoft’s terms don’t claim ownership of what the AI creates for you. However, ownership doesn’t mean no risk. The contract will require you to acknowledge that AI-generated content may be incorrect or could inadvertently resemble existing copyrighted material. Microsoft disclaims liability for the results, meaning it is the user’s responsibility to vet and use the outputs responsibly. If the AI produces something defamatory or a piece of faulty code that causes damage, your organization bears that risk, not Microsoft. That’s why it’s critical to have internal review processes for important AI outputs and not treat the AI as infallible. Microsoft will typically not indemnify you for any IP issues in AI outputs. They will, however, indemnify you if the Azure service itself infringes someone’s IP (a standard vendor indemnity). Always confirm these nuances in your contract, so you know where you stand.
Q4: What service levels and support do we get with Azure OpenAI?
A: Azure OpenAI comes with an uptime Service Level Agreement (SLA) similar to other Azure cloud services (often around 99.9% uptime). If the service falls below that in a given month, you can claim service credits. For support, Azure OpenAI issues can be submitted through Azure Support, just like any other service. If you have a Premier or Unified Support contract with Microsoft, that coverage includes Azure OpenAI – meaning you can get 24/7 support and faster response times as per your support tier. It’s advisable to let your support representatives know that Azure OpenAI is a critical service for you, so they’re prepared. By contrast, if you opt for OpenAI’s direct service, their standard support is more limited (email-based, with no guaranteed response time unless you have a special contract). With Microsoft, you’re leveraging their enterprise support infrastructure, which is a strong point in mission-critical deployments. Be sure to verify if any specific support plan level is required for your expected usage. Generally, existing Azure support entitlements apply.
Q5: How can we avoid getting locked in if the AI landscape changes in the next year or two?
A: The best way to stay flexible is through the contract structure and your architecture. Contractually, keep the term of your Azure OpenAI agreement relatively short – align it with your main Azure agreement renewal or do an initial 12-month commitment with the option to renew. Avoid any clauses that would penalize you heavily for reducing usage or exiting early (aside from forfeiting unused prepaid credits if you committed). You should also confirm that there’s no exclusivity – you remain free to use other AI platforms alongside Azure. If you’re committing to spend on Azure OpenAI, make sure it’s a level you’re confident you’ll use; otherwise, stick to pay-as-you-go so you’re not paying for capacity you don’t need. Technically, design your applications in a way that they could swap out Azure OpenAI for another AI backend if necessary (for example, through modular architecture or abstraction layers for the AI calls). While moving off a platform isn’t trivial, avoiding proprietary lock-in features will give you more agility. In negotiations, you can also request a review clause – for example, at the 12-month mark – to renegotiate pricing if new competitive models or pricing emerge. Microsoft likely won’t guarantee future price drops now, but showing that you’re mindful of market evolution will set the tone that you expect to keep the dialogue open. In fast-moving domains like generative AI, maintaining this flexibility is key to protecting your interests over time.
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