Negotiating Azure OpenAI Credits in New Enterprise Agreements

Negotiating AI Credits and Special Pricing in Microsoft Enterprise Agreements

Executive Summary: CIOs and CTOs can capitalize on the novelty of generative AI services to negotiate Azure AI credits and special pricing within their Microsoft Enterprise Agreements.

With Microsoft aggressively promoting AI (like Azure OpenAI and Microsoft 365 Copilot), enterprises have a unique window to secure usage credits, custom discounts, and favorable terms for AI services.

By bundling AI into contracts, requesting trial credits, and strategically leveraging the organization’s buying power, IT leaders can significantly reduce costs and risk as they adopt these cutting-edge services.’

Read Microsoft AI Licensing for Copilot and Azure OpenAI.

Understanding “AI Credits” in Enterprise Agreements

“AI credits” refer to Azure consumption credits or funding specifically allocated to AI services (such as Azure OpenAI, Cognitive Services, or Microsoft’s AI-powered products).

In practice, this could mean free Azure usage earmarked for AI workloads or a monetary credit applied to your Azure bill to offset the cost of new AI services.

These credits are typically negotiated incentives, rather than standard entitlements, intended to encourage the adoption of emerging technology.

• In Microsoft Enterprise Agreements (EA), Azure spend is often governed by a committed monetary amount. AI credits can effectively increase your Azure spending power within that commitment (for example, an additional $ 100,000 in Azure credits to use on Azure OpenAI Service).
• Microsoft sometimes funds partners or provides “Customer Success” funds to help deploy new tech. If you’re rolling out an AI pilot or project, you can ask Microsoft to cover initial usage or consulting costs with credits instead of paying full price from day one.
• Special pricing for AI services typically refers to a discounted rate or a custom pricing model based on consumption. Since AI workloads (such as GPT-4) are usage-based, enterprises can negotiate a lower per-unit cost (e.g., a lower cost per 1,000 tokens) once usage reaches certain thresholds.

Real-world example: A large enterprise entering a new EA for Azure might negotiate “AI pilot credits” – say $250,000 of Azure credit – specifically to experiment with Azure OpenAI Service in the first year. This credit directly reduces their AI experimentation cost while Microsoft benefits by driving adoption.

Read Azure OpenAI Data Privacy and Compliance for Enterprise AI Deployments.

New Tech, New Leverage: Why AI Services Are Negotiable

Generative AI is a strategic priority for Microsoft, and it’s still a new frontier for enterprise contracts.

This dynamic creates leverage for buyers:

• Vendor eagerness: Microsoft’s sales teams have aggressive targets for AI adoption. They recognize that AI is the future and want customers to be locked in early on Azure AI. This means they’re more willing to sweeten the deal with credits, discounts, or flexible terms to win your AI business. Early adopters often get the best concessions.
• Unstandardized pricing: Many AI services (especially Azure OpenAI) are consumption-based with usage costs that can spike. Because the pricing models are evolving, there’s no “one-size-fits-all” quote yet. Enterprises can push for a custom deal – Microsoft would prefer to have you using their AI even at a discount, rather than losing you to a competitor or to caution.
• High costs & risk: Running GPT-4 at scale isn’t cheap. For example, using GPT-4 via Azure can cost about $0.06 per 1,000 tokens of output (and $0.03 per 1,000 input tokens). That translates to ~$60 per million output tokens. At an enterprise scale (approximately 500 million tokens per month, equivalent to around $ 30,000 per month), CIOs are concerned about unpredictable costs. Microsoft recognizes this concern. They may offer introductory credits or price caps for AI to prevent sticker shock from slowing adoption. In essence, your concerns are bargaining chips – if you voice that AI costs are a barrier, Microsoft might counter with financial incentives or special pricing to address it.
• Competitive pressure: Microsoft also knows you have choices – you can go directly to OpenAI or opt for other clouds like AWS, Bedrock, or Google Vertex AI. If they sense you’re evaluating alternatives, they often become more flexible on price and credits to keep your AI workloads on Azure. Savvy CIOs let Microsoft know that multi-cloud AI is on the table to encourage a better offer.

In summary, the novelty and strategic importance of AI services mean that many of the “rules” of past Microsoft licensing negotiations are in flux.

There’s no established street price for, say, millions of GPT-4 queries – so you have an opening to create your deal structure.

Azure AI Service Pricing: Consumption vs. Per-User Models

When negotiating AI in an enterprise contract, understand the two broad licensing models at play:

• Consumption-Based AI Services: Azure OpenAI Service (GPT-4, etc.), Azure Cognitive Services, and Azure AI tools are typically offered on a pay-as-you-go basis. Costs scale with usage (tokens processed, hours of model running, API calls, etc.). This model offers flexibility but can lead to unpredictable costs if usage spikes. It’s similar to other Azure services – you pay for what you consume, measured in units like tokens or transactions. Negotiation implication: You can seek volume discounts (e.g., lower unit price after X million tokens) or commitment discounts (discounts in exchange for committing to spend a certain amount). Also, ensure that any Azure credits or commit dollars apply to these services, so you can draw down pre-paid funds instead of paying extra.
• User-Based AI Services: Some AI offerings are licensed per user or per seat. A prime example is Microsoft 365 Copilot, which is offered at a list price of $30 per user per month. That’s a fixed fee per user (in addition to their existing M365 license) for AI capabilities. This model offers cost predictability, but at a steep price, and ROI depends on adoption (you pay even if a user barely utilizes the AI). Negotiation implication: Microsoft initially set Copilot’s price publicly with “no discounts,” but large enterprises have negotiated deals (e.g., 10–30% off for enterprise-wide rollouts or multi-year commitments). If you’re enabling thousands of users, you have room to request a lower per-user rate or rebates based on the scale of your deployment. Microsoft might resist lowering the Copilot price directly; instead, they could offer bundled discounts (e.g., discounting other Microsoft 365 components when Copilot is added).

Key point: Identify the AI services you plan to use and their respective models. Consumption services are where asking for credits and rate discounts makes sense. Per-user services are where you push for license discounts or bundled savings.

In both cases, make sure the AI services are covered under your enterprise agreement terms (for liability, compliance, etc.) and not treated as one-off add-ons with weaker protections.

Read Azure OpenAI GPT-4 Cost Strategy for CIOs.

Leveraging Enterprise Agreements for AI Deals

Your Enterprise Agreement with Microsoft is a powerful framework to embed these new AI services under favorable terms:

• Consolidate AI into EA: Whenever possible, include Azure-based AI services as part of your Azure EA enrollment or add Copilot to your EA as an add-on SKU. This way, all your pre-negotiated EA terms (such as price protections, discounts, liability caps, and data privacy commitments in the DPA) will be extended to the new AI offerings. It also lets you apply Azure commits or overage discounts to AI usage. For example, if you have a $10M Azure commitment over 3 years, every dollar spent on Azure OpenAI would count towards that commitment (so essentially you’ve pre-paid at a discount).
• EA renewal as an opportunity: The best time for negotiating AI is often during your EA renewal (or a significant expansion point). Microsoft tends to introduce new products during renewals, a strategy known as “land and expand.” If generative AI is on your roadmap, signal that early. You might find Microsoft offering to “sweeten the renewal” with AI incentives. For instance, at renewal, they might throw in extra Azure credits or cut a special deal on Copilot to secure a longer contract. If your renewal is still far off but you want AI now, you can negotiate a coterminous addendum – add the AI services now, but have their end date align with your EA. This gives you a chance to renegotiate those AI terms in the next renewal once you have usage data.
• Ensure flexibility: Since AI technology is evolving rapidly, avoid locking in beyond what you’re comfortable with. In the EA context, that means:
  • If you commit to a certain AI spend or number of AI licenses, keep terms short (align with EA term) and/or include exit ramps. For example, pilot for one year with the option to cancel or reduce if outcomes aren’t met.
  • Preserve the ability to re-negotiate pricing if newer models or price drops occur. You might not get an automatic price reduction clause (vendors dislike that), but you can aim for a “meet or beat” clause: if Microsoft lowers public pricing or a new model is cheaper, you should get that benefit.
  • Clarify that any unused AI credits or funds can carry over or be repurposed. If Microsoft gives you $200K in AI credits, try to attach them to the full term of the EA (e.g. usable anytime in the next 3 years) rather than expiring in year 1.

In short, think of your EA as the umbrella that contains and controls AI costs. By slotting AI into it, you not only leverage your existing discounts and commitments but also gain the contractual safeguards that an EA provides.

Figure: Key components of an Azure negotiation strategy include accurate consumption forecasts, base discounts (ACD), and additional concessions like service-specific credits and funding. New AI services often fall under “strategic workloads” where Microsoft can provide extra incentives.

Many variables factor into an effective negotiation.

The figure above illustrates elements such as consumption forecasting and tiered discounts – AI workloads are a prime example where service-specific discounts or credits might apply due to their strategic importance.

Always approach an AI deal with a holistic view of your Microsoft relationship: your total Azure spend, other Microsoft products in use, and upcoming projects can all be leveraged.

Strategies to Secure AI Credits and Discounts

Negotiating AI pricing isn’t just about haggling on a number – it’s about using all the levers available.

Here are key strategies and tactics for CIOs and CTOs:

• Ask for Trial Usage Credits: If you’re just starting with services like Azure OpenAI or an AI pilot, explicitly request evaluation credits. Microsoft often has internal programs to fund trials (for example, a certain amount of Azure OpenAI Service is free for 2-3 months). These pilot credits reduce your risk and signal Microsoft that you’re serious about AI (since you’re planning a pilot). Always get the agreement in writing – e.g., “Microsoft will provide $50,000 in Azure credits for OpenAI usage in Q1 as part of this deal.”
• Volume Commitments = Volume Discounts: Treat AI usage like a commodity you plan to buy in bulk. The more you commit to using it, the greater the discount you will get. Be prepared with projected usage numbers (such as millions of tokens per month, or the number of Copilot users over time). Use that as a bargaining chip: “If we use 10 million GPT-4 tokens a month, we expect a rate 20% lower than pay-as-you-go.” Microsoft can create a custom rate card for large consumption commitments. This might involve tiered pricing (e.g., the first X million tokens at standard rate, the next Y million at 30% off). If your usage is significant, insist on negotiating a rate that is better than the public one. Pro tip: Also negotiate true-up terms – if you exceed your committed volume, ensure additional usage is priced at the same discounted rate, not a higher one.
• Bundle AI with Other Deals: Microsoft sometimes resists simply cutting the price of a hot new product (they’d hate to say “yes, we discounted Copilot from $30 to $20 for you”). Instead, they offer value elsewhere in the bundle. You might hear something like, “Copilot is universally $30, but we can give you an extra 5% off your Office 365 E5 licenses since you’re adopting Copilot.” Be open to cross-product deals that achieve your cost objectives. For example:
  • Agree to add Microsoft 365 Copilot for 5000 users, and in exchange, Microsoft increases your overall Office 365 discount from 15% to 20%. Net effect: you’re saving money, just not directly on the AI line item.
  • Tie in an Azure increase: “We’ll move that new analytics workload to Azure (instead of AWS) if you give us $X in Azure credits toward AI services.” This way, Microsoft gains more of your cloud business, and you fund your AI.
  • Strategic co-terming: Bundling can also simplify contracts. If you add AI licenses now, co-term them with your EA end date. Microsoft values the administrative simplicity and might reward it with a concession (e.g., a longer price lock or a one-time credit).
• Leverage Early Adopter Status: If your company is a recognizable name or in a key industry, use that to get a better deal. Microsoft highly values reference customers for AI. You could negotiate something like: “We’ll be a featured case study or speak at an event about our Azure OpenAI deployment if you provide an additional 10% discount on our AI usage.” Essentially, trading publicity for price. Only do this if you are comfortable, but it can convert your brand value into real dollars saved.
• Utilize Multi-Cloud Competition: Even if you intend to stay with Azure, let Microsoft know you’re evaluating OpenAI API, AWS, and Google for AI solutions directly. A subtle mention that “AWS is offering us some Bedrock credits for a pilot” or “OpenAI’s direct pricing is an option we’re considering” can make Microsoft more generous. They’d rather keep your AI workload on Azure at a lower margin than see you go elsewhere. This competitive tension is one of your strongest negotiation levers in the AI space.
• Partner Funding for Deployment: Beyond usage credits, request funding for services. New AI solutions often require integration, user training, and change management. Microsoft can provide funding for a partner or FastTrack team to help with implementation (e.g., covering consulting hours to deploy a chatbot or set up Copilot governance). While not a direct discount on the product, it saves you money on the project. It’s reasonable to request, for example, “a $100K services fund to ensure successful rollout of Azure AI.” This is especially effective if you lack in-house expertise for the new tech.
• Reserved Capacity & Long-Term Discounts: Azure OpenAI offers Provisioned Throughput Units (PTUs) – essentially reserving capacity with a commitment (monthly or yearly). Committing to a reserved capacity can yield unit costs 30-50% lower than those of on-demand, as noted by early enterprise users. If you know you’ll need steady, high usage, consider negotiating a reserved capacity deal. Often, vendors might even include some free capacity initially (e.g., a few months free on a 1-year commitment) as an incentive. Ensure that reserved terms align with your needs (e.g., a 1-year commitment, not 3 years, unless you’re sure).
• Service-Level and Support Terms: Don’t Neglect SLA and Support in Your Negotiation. Azure OpenAI comes with a standard 99.9% uptime SLA. If your use of AI is mission-critical, you may request enhanced support (such as a named technical account manager or quicker response times for AI issues) at no additional cost. If Microsoft won’t budge on core pricing, you may be able to get support upgrades included. For example, “include Azure Rapid Response support for our AI project for the first 6 months.” This ensures if something goes wrong with the new tech, you’re not left in the lurch – effectively, it’s an insurance policy Microsoft can provide relatively cheaply.
• Phased Commitments to Reduce Risk: As the technology is new, avoid overcommitting on day one. Negotiate the right to start small and expand at locked-in prices. For instance, “We’ll license 1,000 Copilot users now, with the option to add up to 5,000 more at the same discounted rate within 12 months.” Or for Azure OpenAI: “We commit to $100K usage this year, but if we scale to $500K next year, maintain the same unit pricing.” This way, you’re not forced to purchase more than you need, but you also secure future pricing for growth. Microsoft might prefer you commit all upfront, but it’s reasonable to condition it on actual adoption proving value.
• Document Everything: When negotiating non-standard perks, such as credits, special pricing, or custom terms, ensure they are included in the contract (or an addendum). Verbal promises from sales, such as “we’ll give you some free tokens to test,” must be written down as part of the agreement (e.g., as a “Credit Offer” clause or a separate funding letter). This avoids any disputes later and ensures you can redeem those benefits.

By combining these tactics, enterprises have successfully cut their effective costs for AI.

For example, one Fortune 100 company negotiated a 15% custom discount on Azure OpenAI usage rates by committing to a multi-million-dollar volume, and on top of that, Microsoft provided $ 200,000 in Azure credits to jump-start their AI pilot.

Another company agreed to deploy Microsoft 365 Copilot to all divisions and, in exchange, received a broader 3% discount on their Microsoft 365 E5 contract, offsetting the Copilot expense. These outcomes are achievable with a firm ask and smart bundling.

To illustrate potential savings, consider a few scenarios:

Table: Examples of how negotiating can improve AI economics. Even a modest 10-15% discount or some free credits translate to substantial dollar savings at enterprise scale.

Recommendations

In summary, enterprise IT leaders should treat AI services as negotiable elements of their Microsoft contracts.

To maximize value and minimize risk, consider the following recommendations:

• Start early and inform Microsoft of your AI plans – use upcoming EA renewals or major purchases as an opportunity to discuss your AI needs and signal that you expect incentives to adopt them.
• Request trial credits and funding for initial AI projects. Reduce your experimentation costs by having Microsoft fund pilots or partner engagements before committing to a long-term solution.
• Model your expected AI usage (e.g., tokens, users) and present it in negotiations. Use a data-driven approach to ask for volume-based discounts or a custom rate card for Azure AI services.
• Bundle AI with other product negotiations. If Microsoft resists directly discounting AI, consider leveraging bundle deals (such as licenses or Azure commitments) to achieve overall savings. Every concession counts, even if it’s not labeled “AI discount.”
• Leverage multi-cloud and vendor competition. Engage with or cite AWS, Google, or OpenAI alternatives – even if just for leverage. Microsoft is more likely to give credits or better pricing if they know you have other options.
• Insist on flexibility and review. Negotiate the ability to expand usage at locked prices or to revisit terms in a year. Avoid multi-year commitments on AI without checkpoints. Given the rapid evolution of AI, keep your options open.
• Include AI in your EA governance. Ensure that any negotiated credits, discounts, or support promises for AI are captured in the contract. Monitor usage versus commitment to avoid overspending, and utilize Azure’s cost management tools to set caps and alerts on AI services.
• Secure support and SLAs for these new services. In negotiations, request enhanced support (if needed) at no additional charge, considering the complexity of AI. Ensure the SLA covers the AI functionality (e.g., if Copilot’s AI is down, it counts as an outage).
• Be willing to walk (or delay) if the deal isn’t right. Sometimes holding off on adoption can lead to better offers. Microsoft’s AI offerings and pricing will only get more competitive as the market matures – use that to your advantage if they won’t meet your requirements today.

By following these steps, CIOs and CTOs can leverage Microsoft’s enthusiasm for AI to secure tangible contract value, ensuring their organization obtains the best possible terms for this next-generation technology.

FAQ

Q1: What are “AI credits” in a Microsoft enterprise agreement?
A1: AI credits refer to Azure cloud credits earmarked for AI services or workloads. In an EA context, this might be a monetary credit that reduces the bill for Azure OpenAI Service, Cognitive Services, or other AI products. They’re typically negotiated incentives – for example, Microsoft might agree to provide $100K of Azure consumption credit to support your use of GPT-4. These credits function like free Azure dollars you can spend on specified AI services, helping offset costs, especially during initial adoption.

Q2: How can we get Azure credits or discounts for AI as part of our contract?
A2: You usually obtain AI credits or discounts by asking for them during negotiation. Come prepared with a justification: you’re exploring costly new tech with unknown ROI, so you need Microsoft to share the risk. Tactics include:

• Including Azure OpenAI in your enterprise deal and requesting a custom discounted rate if usage is high.
• At EA renewal, inform Microsoft that you’ll adopt their AI solutions if they provide credits to cover a pilot or a percentage of your consumption.
• If Microsoft won’t cut the product’s price, ask for equivalent value in Azure credits or other products (for instance, “we pay list price for Copilot, but Microsoft gives us extra Azure credits worth 3 months of our expected AI usage”).
  Vendors won’t volunteer credits upfront – you must explicitly make it part of your negotiation agenda.

Q3: Is Microsoft willing to negotiate the $30/user/month price of Microsoft 365 Copilot?
A3: Officially, Microsoft set Copilot at $30/user with no volume tiers. However, in practice, large enterprises have negotiated better deals. Microsoft might not outright say “Copilot now costs $25 for you,” but they can achieve the same effect via enterprise discounts. For example, suppose you are deploying tens of thousands of Copilot licenses. In that case, you may be eligible for a rebate or discount that effectively reduces the per-user cost (some organizations have reported a 10–20% discount for large rollouts). Microsoft may instead offer a discount on your broader Microsoft 365 bundle or provide some complimentary months for certain users. The key is that a significant scale or a multi-year commitment gives you leverage – small customers won’t have much luck negotiating with Copilot, but large ones should push hard for concessions.

Q4: Our Azure OpenAI usage is growing unpredictably. How can we control costs and get predictability?
A4: There are a few approaches:

• Negotiation side: Lock in a volume discount or rate card for your expected usage. For instance, if you foresee $50K/month spent on Azure OpenAI, negotiate a committed rate that is lower than pay-as-you-go. Also consider reserved capacity (PTU) offers, which commit to a certain throughput at a lower unit cost. This makes your costs more predictable (like a fixed fee for capacity).
• Contract side: Implement a “not to exceed” clause or burst cap if possible – e.g., if usage doubles unexpectedly, have a provision to renegotiate or get a discount on the overage. Additionally, utilize Azure’s built-in budget and alert tools to monitor and cap usage in real-time. While Microsoft won’t guarantee your bill won’t go over X, they can help with cost management tools and perhaps agree to evaluate pricing if you far exceed plans.
• Phasing: Begin with a smaller rollout or usage limit and include an expansion plan in the contract. That way, you evaluate actual costs before scaling up. Overall, make sure the EA’s price protections (like caps on rate increases) cover the AI services – Azure EA usually has a price cap metric, so your rate per unit won’t arbitrarily spike during the term.

Q5: Should we include AI services in our Enterprise Agreement or handle them as separate contracts?
A5: In almost all cases, include them in your EA. Adding AI services (Azure OpenAI, etc.) under your EA means:

• You benefit from any existing Azure discounts or commitments – AI usage will draw down your pre-committed Azure funds at your discounted rate, rather than being billed separately at retail.
• You receive enterprise-level contractual terms – e.g., Microsoft’s Data Protection Addendum, liability clauses, etc. – that will cover the AI service. This is important for compliance and legal assurance, especially with sensitive data and AI.
• It simplifies management – one consolidated bill and contract to manage rather than juggling a new agreement for the AI tool.
  The only time separate might make sense is if Microsoft requires a different agreement (like some very new preview service under special terms). Even then, try to negotiate it as an amendment to the EA. A unified agreement strengthens your negotiating position (total spend leverage) and reduces the chance of any unfavorable “gotchas” hidden in a separate contract.

Q6: What leverage do we have if we’re not a huge Azure spender yet? Can smaller enterprises still get AI concessions?
A6: Smaller organizations have less natural leverage, but you can still employ tactics:

• First-time adopter leverage: If this is your first significant Azure or AI purchase from Microsoft, use the “new customer” angle. Microsoft aims to attract new AI customers by showcasing success stories. Even if your spend isn’t massive, you can ask for a small amount of credits or a slight discount as an incentive to choose Azure for AI. They know that if you succeed and grow, your spend will increase, so position it as a long-term partnership.
• Multi-product leverage: You might not be huge in Azure, but perhaps you’re a big Office 365 or Dynamics customer. Use that relationship. For example, “We’ve been an E5 customer for years; we’ll trust Microsoft for AI too, but we need a gesture like a 5% discount to get our stakeholders on board.” Loyalty and referenceability can sometimes serve as substitutes for pure volume in negotiations.
• Competitive quotes: Get pricing or trial credits from OpenAI or another cloud and share those details. Even a small business can say, “OpenAI direct is offering us enterprise API access with a 5% discount if we commit early – can Azure match that?” Microsoft may counter with something to win you over.
• In short, while you might not get a giant discount, don’t assume you have to pay sticker price. There are always negotiation angles, just scale your requests to something reasonable for your size.

Q7: We’re concerned about committing to a big AI contract and then a year later, a new model or competitor is better. How do we mitigate that?
A7: This is a valid concern given how fast AI is evolving. To protect yourself:

• Shorter commitment or pilot terms: Avoid locking in a large 3-year AI commitment right away. Start with a shorter term or a smaller commitment, and include an option to re-evaluate at renewal. For example, sign a 1-year addendum for the AI service, separate from your main 3-year EA, so you can adjust in a year without affecting everything.
• Future-proof clause: Try to include language that if a new model/version is released by Microsoft that is more cost-effective or better suited, you can switch to it under the same commercial terms. Additionally, if Microsoft lowers its prices broadly (due to competition), you should benefit. While you may not get an automatic price drop, even a soft commitment like “Microsoft and Customer will meet annually to discuss the impact of product updates on pricing” puts them on notice that you expect to share in any efficiency gains.
• Keep options open: Don’t commit 100% of your AI budget to Microsoft. Perhaps consider a multi-cloud approach (e.g., try one application on Azure OpenAI and another on a rival) to maintain leverage. Microsoft will see that you haven’t bet everything on them, which ironically keeps them more responsive to your needs through the contract.
• Essentially, build in flexibility to allow for pivoting. The best negotiation is one that allows you to renegotiate when the landscape changes.

Q8: If Microsoft gives us Azure credits for AI, what are the typical conditions attached?
A8: Azure credits negotiated in an EA usually come with conditions like:

• Expiration date: The credit might need to be used by a certain time (e.g., within the first year of the EA). Try to negotiate for a longer validity if possible.
• Applicable services: It could be restricted to specific services (e.g., “only for Azure OpenAI and Cognitive Services usage”). Ensure it’s broad enough to accommodate your plans.
• Non-cashable: Credits typically just offset invoices – you can’t “cash them out” if unused. If you don’t consume them, they lapse. So, plan to utilize them fully.
• Administrative process: Sometimes you need to redeem or apply the credits via your Microsoft account team or a promo code. Ensure you know the process and who to contact so the credits are applied to your bill.
  It’s important to track these credits. In your contract, they may be documented as an “Azure Consumption Offer” or listed as a line item with a dollar value. Assign someone to monitor that the credits appear on your Azure bill and are deducted correctly. Transparency is key – you don’t want a promised $ 50,000 credit to go missing due to a clerical error.

Q9: Can we negotiate Azure OpenAI costs down the same way we do for Azure VMs or other services?
A9: Yes, many of the same principles apply, with some nuances:

• Azure OpenAI is part of Azure, so if you have an enterprise discount (Azure Consumption Discount) that covers all Azure services, it will also automatically apply to OpenAI usage. Check your EA pricing sheet: if you have, say, a 15% off rate on Azure services, OpenAI should inherit that. If not, ask why.
• For additional discounts beyond your standard Azure discount, treat OpenAI like a key workload. Microsoft often has service-specific incentives for strategic workloads (AI is strategic). You can negotiate a supplemental discount on OpenAI consumption in addition to your baseline Azure discount. For instance, “Our EA gives us 10% off Azure generally, but we want an extra 10% off OpenAI usage because we plan to spend $2M on it over 3 years.”
• Use techniques like commitment tiers (commit to X amount of OpenAI spend for Y% off) or cost escalation protections (cap how much the rate can increase year over year).
• So, just as you might get special pricing for a big SQL Server in Azure or for storage, you can aim for special pricing for AI services. It’s all negotiable with enough volume and importance.

Q10: What pitfalls should we watch out for when negotiating AI in our contract?
A10: A few pitfalls to avoid:

• Overcommitting to usage: Don’t let Microsoft push you into committing to more AI consumption or licenses than you realistically need. It’s better to start a bit lower and have room to grow. Otherwise, you may incur costs for unused capacity or licenses.
• Forgetting to align terms: Ensure any AI addendum doesn’t extend beyond your main agreement (unless there’s a strategic reason). Microsoft might offer a great price, but consider the long-term implications of being locked in for 5 years at this early stage of AI technology. Balance savings with flexibility.
• Ignoring data and compliance clauses: New AI services bring new risks (data privacy, output usage rights, etc.). As you negotiate pricing, also review the contract’s language on data handling for AI. For example, ensure that the contract reflects Microsoft’s commitment that your prompts and outputs in Azure OpenAI aren’t used to train their models (which is Microsoft’s policy for enterprise, but good to have in writing). Also, verify if there are any additional licenses (such as for AI output) or restrictions that could impact you later.
• Not securing support: As mentioned, if you’re relying on AI in a critical system, make sure you have adequate support SLAs. Negotiating a great price is no good if an outage occurs and you have no recourse.
• Payment model surprises: Clarify how you’ll be billed. Azure OpenAI consumption will just appear on your Azure bill – no surprise there – but something like Copilot may be billed annually upfront in an EA. Know the cash flow implications (e.g., will Microsoft charge for 12 months of Copilot licenses upfront? Often, yes in an EA – try to negotiate prorated or flexible billing if needed).
  By being vigilant about these aspects, you’ll not only get a good price but also a well-structured deal with no unpleasant surprises. The goal is to safely and cost-effectively harness AI, and a solid negotiation sets the foundation for that success.

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