OpenAI vs Microsoft Azure OpenAI: Who Should You Sign With?

OpenAI vs Microsoft Azure OpenAI: Who Should You Sign With

Choosing between OpenAI and Microsoft’s Azure OpenAI Service is a strategic decision for enterprises. Both give access to cutting-edge AI models like GPT-4, but they differ in contract models, SLAs, pricing, and integration.

This brief provides an unbiased comparison to help IT, procurement, finance, and legal stakeholders understand the differences between OpenAI and Microsoft in enterprise agreements.

The OpenAI vs Microsoft Azure OpenAI Difference

OpenAI (Direct) is the creator of GPT-series models and offers API access and enterprise plans for organizations to use these models in their applications.

Azure OpenAI Service, on the other hand, is Microsoft’s managed offering of OpenAI models through the Azure cloud platform.

In practice, both routes let you harness the same AI capabilities, but the experience and partnership differ:

• Vendor Relationship: Signing with OpenAI means contracting directly with a fast-moving AI research company. Signing with Azure means working through Microsoft as your vendor, under Azure’s umbrella of services.
• Enterprise Focus: OpenAI’s platform is evolving fast and is open to any developer or company (with usage limits and approvals for some models). Azure OpenAI is targeted at enterprise customers with Azure accounts, often requiring an application or Microsoft’s sign-off to ensure responsible use.
• Innovation vs. Integration: OpenAI may roll out new model features first, while Azure focuses on integrating AI into a secure, compliant enterprise environment. For example, Azure provides built-in identity management (Azure AD), networking controls, and monitoring tools from day one. OpenAI’s platform is standalone, which gives flexibility but requires more custom integration on the customer’s side.

Key Differences at a Glance:

The table below summarizes major points for OpenAI vs Microsoft Azure.

Table: Key Differences between OpenAI and Azure OpenAI in the Enterprise Context.

Contract Models and Azure OpenAI Licensing

Contracting with OpenAI:

Enterprises that go directly to OpenAI will typically sign a Master Services Agreement (MSA) or similar contract with OpenAI. This is a separate vendor relationship – your legal team will negotiate terms around data usage, confidentiality, liability, and IP directly with OpenAI.

OpenAI’s contracts are relatively straightforward, usage-based agreements. However, for large customers, they offer enterprise licensing options, such as committed-use discounts or private instances.

Notably, OpenAI’s standard terms do not include certain protections by default (for example, no built-in HIPAA clause), so any required terms must be negotiated.

OpenAI is a relatively new vendor in enterprise procurement, meaning you may need to conduct vendor risk assessments and security reviews from scratch.

Azure OpenAI licensing:

If your company is already an Azure or Microsoft customer, Azure OpenAI comes under your existing Microsoft agreement. In effect, Azure OpenAI is just another Azure service (much like Azure SQL or Cognitive Services).

You consume it under your Azure subscription, and any usage charges appear on your Azure bill. Legally, you’re covered by Microsoft’s Azure terms and the Azure OpenAI Service-specific terms.

This means standard cloud contract provisions (like Microsoft’s data protection addendum and service terms) automatically apply.

Many enterprises prefer this because they have pre-negotiated enterprise agreements with Microsoft.

It can simplify legal approval – there’s no new vendor to onboard, and Microsoft’s contract often already addresses key concerns (data privacy, security certifications, etc.).

In short, Azure OpenAI licensing is enterprise-friendly: it lets you leverage Microsoft’s robust contract framework and any volume discounts you have in place with Azure.

However, vendor neutrality might be a consideration. Signing with OpenAI directly keeps your AI vendor separate, which some buyers prefer to avoid due to excessive dependence on a single large provider.

Others see value in consolidating with Microsoft, as it provides a single point of contact in case of issues. It’s wise to weigh your procurement strategy: would adding OpenAI as a new vendor create complexity, or is the value it provides manageable?

Also, consider the longevity and roadmap: OpenAI (as a company) is rapidly evolving – direct alignment may give you earlier access to innovations.

Microsoft, on the other hand, will package and govern the tech in a more controlled way – potentially a plus for risk management.

Pricing and Cost Considerations

When comparing costs, note that the underlying model usage fees are similar in many cases, but the billing models and potential discounts differ:

• OpenAI Direct Pricing: OpenAI publishes transparent per-token prices for each model (for example, GPT-4 at a certain rate per 1,000 tokens). You pay only for what you use, with no upfront commitment required. Many teams start on this pure consumption model. For enterprises, OpenAI can offer volume-based discounts or monthly credit packages if your usage is expected to be significant. OpenAI has also introduced reserved capacity options (dedicated instances of models for your use) – these come at a fixed cost per hour, plus usage, and include higher reliability and support, which is beneficial if you have a very high, steady load. The bottom line: OpenAI’s pay-as-you-go is simple and often cheaper upfront for experimentation, but as usage scales, you’ll want to negotiate an enterprise plan to control costs.
• Azure OpenAI Pricing: Azure’s pay-as-you-go pricing for OpenAI models generally mirrors OpenAI’s pricing per unit (token or request). Microsoft essentially passes through the usage cost of the model. Where Azure differs is in additional cloud infrastructure costs and the availability of discount opportunities. Suppose you deploy a dedicated capacity on Azure OpenAI (called Provisioned Throughput Units or reserved instances). In that case, you’ll incur an hourly charge for that reserved compute, on top of usage – similar in concept to OpenAI’s dedicated instance cost. Additionally, using Azure means you may inadvertently utilize other billable Azure services (such as networking, logging, and storage of prompts), so oversight is necessary to avoid unexpected charges. On the other hand, enterprises can integrate Azure OpenAI usage into their existing cloud spending commitments. Suppose you have an Azure consumption commitment or negotiated discounts (common in Enterprise Agreements). In that case, your AI usage might qualify for those same discounted rates or at least count toward committed dollars. Additionally, Microsoft sometimes offers promotional credits or incentive funds for AI projects as part of larger deals. Savvy buyers will ask their Microsoft rep about AI-specific credits or pricing programs – for instance, some enterprises negotiate a pool of Azure OpenAI credits to support initial pilots.

Cost management tips:

Regardless of vendor, implement strict monitoring on usage. Set budgets or alerts (OpenAI’s dashboard allows setting hard limits; Azure has cost management tools) to prevent runaway costs.

Many companies start with GPT-4 for everything, then realize costs can balloon. A cost-saving tactic is to use cheaper models (like GPT-3.5) for trivial queries and reserve GPT-4 for complex tasks. Also, understand that pricing models may change as both OpenAI and Microsoft adjust to market demand.

Lock in rates or discounts contractually if possible. For example, if negotiating an enterprise contract with OpenAI, see if you can secure rate protection for a year.

With Azure, any enterprise agreement typically includes discounts or provides Azure credits, offering a cushion against potential price hikes.

Service Levels and Support

A critical factor for enterprise decision-makers is the service level commitment and responsiveness to support, especially if AI will be used in production or mission-critical processes.

SLAs:

OpenAI’s standard API comes with no explicit uptime guarantee – it’s a “best effort” service in the public cloud. Outages or slowdowns can occur (as OpenAI’s systems face heavy public load), and you have limited recourse except for waiting or contacting support.

However, OpenAI’s enterprise contracts (particularly if you opt for a dedicated instance or their Scale Tier plan) do offer an SLA, often around 99.9% uptime for the model endpoints you’re using.

These higher tiers essentially give you reserved capacity and support guarantees. Make sure to ask OpenAI about SLA options if uptime is crucial – it won’t be in place unless you explicitly opt for it (and it will likely incur additional costs).

Azure, by contrast, treats OpenAI as a managed service with an Azure SLA. Microsoft commits to a certain uptime (for many Azure services, it’s 99.9% or above). If the Azure OpenAI service has an outage beyond that SLA, customers can typically claim service credits.

This can be reassuring for enterprise users: you have a financially-backed guarantee of service continuity. Additionally, Azure’s infrastructure resilience (multiple regions, failover capabilities) can add reliability.

One nuance: SLA applies once you’re approved and using the service, but keep in mind that Azure OpenAI was initially capacity-constrained (with limited availability of GPT-4 in some regions). Microsoft has been scaling up, but always verify current capacity and any limits that could affect your usage scaling.

Support:

With OpenAI’s direct support, it is improving but still lean. Standard users rely on email support and community forums, which might be slow for urgent issues.

Enterprise customers are typically given faster email contacts or even a dedicated support rep if you’re large enough. OpenAI is a smaller organization than Microsoft, so its support may not be available 24/7 globally, unless you’re on a top-tier plan. In contrast, Microsoft has a huge support organization.

Azure OpenAI issues can be escalated through Azure support channels. If you already have Premier Support or Unified Support with Microsoft, that coverage also extends to Azure OpenAI. You’ll have access to ticketing systems, on-call engineers, and an account manager who can advocate on your behalf.

Microsoft also provides a richer knowledge base for troubleshooting. For highly regulated or mission-critical uses, that mature support apparatus could reduce risk (e.g., you can get on the phone with Microsoft if something is going wrong; with OpenAI, it’s direct email and waiting).

In negotiations, enterprise buyers should thoroughly test the support provisions. If going with OpenAI, clarify support SLAs in the contract (e.g., “P1 issues = response within X hours”). With Microsoft, check if your support tier is sufficient for your AI plans; you may need to upgrade your support for a faster response.

Data Security, Compliance, and Risk

Data Privacy:

Both OpenAI and Azure have had to address concerns about sensitive data. By default, OpenAI’s API does not use your prompts or outputs to train the models (as of 2023).

This is crucial for enterprise confidentiality – you don’t want your data feeding the model for others to use.

Azure OpenAI goes a step further by operating entirely within Microsoft’s cloud: none of your prompt content is sent back to OpenAI’s servers or used in their research; it stays in your Azure instance’s memory and storage.

Microsoft and OpenAI have a strict arrangement for this service where OpenAI provides the model, but Microsoft handles your data. For most enterprises, Azure’s approach feels safer, especially if dealing with personal data or proprietary info.

Additionally, Microsoft allows companies to apply for data retention exemptions – meaning you can request that even the 30-day log storage for abuse detection is turned off, so Azure won’t store any of your prompts or outputs at rest.

OpenAI as a direct service retains data for 30 days (for abuse monitoring) and then purges it; you cannot generally ask them to skip that for basic usage (it’s built-in for security).

Compliance and Certifications:

If you have strict regulatory requirements, Azure has the clear edge. Under Azure OpenAI, you automatically get the benefit of Azure’s compliance portfolio – everything from ISO27001, SOC 2 Type II, PCI, to HIPAA.

Microsoft will sign a Business Associate Agreement (BAA) for HIPAA compliance if you’re in healthcare. They also support customer-managed encryption keys, private networking (enabling access to the AI via a private endpoint rather than over the public internet), and other enterprise-grade compliance features.

OpenAI, while improving, does not offer all of these. OpenAI has achieved SOC 2 compliance and can support GDPR compliance (especially if you sign their Data Processing Addendum and possibly use data controls to avoid sending personal data).

But OpenAI is not yet able to sign BAAs and generally doesn’t offer industry-specific compliance attestations beyond the basics.

If your industry or geography demands specific certifications or contract clauses, Microsoft will likely meet them as part of Azure, whereas with OpenAI, you might reach a dead end.

For instance, a bank that requires a certain level of audit logging or an on-premises option might prefer Azure’s route or even decide that neither cloud option meets its requirements and delay adoption.

Liability and IP risk:

This is a new area for AI contracts. Both OpenAI and Microsoft include terms that limit their liability for the output of the AI. Neither vendor will outright guarantee that the AI’s answers are correct, safe, or non-infringing – you as the user must assume some risk in using generative AI.

That said, large enterprise customers often negotiate liability clauses. Microsoft might be slightly more flexible here, since their standard enterprise agreement language around liability and indemnification could apply (for example, Microsoft generally provides some indemnification for intellectual property infringement by their software – but does that cover AI-generated content? They likely do not have carve-outs for user-provided content out of the box.

OpenAI’s contracts will be more stringent in disallowing consequential damages and other similar provisions. As a negotiating tactic, if you are a big customer, you might push OpenAI for at least assurances around data breach liability or IP rights to outputs. Both vendors, by default, say that you own the content you create with the models – the outputs are your property.

That’s important for businesses building proprietary assets with AI. Ensure this ownership is explicit in whichever contract: OpenAI’s terms state users have rights to their outputs; Microsoft’s Azure terms similarly claim no ownership of customer data or outputs.

In summary, from a risk perspective, Azure offers a more comprehensive compliance alignment and a familiar risk profile (as a big, stable vendor). In contrast, OpenAI offers cutting-edge technology with a leaner compliance wrapper.

Evaluate your internal risk appetite and requirements: some enterprises may have no choice but to adopt Azure OpenAI due to compliance mandates, while others might opt for OpenAI directly to gain a competitive edge, understanding that they’ll need to implement their risk mitigations.

Integration and Ecosystem Considerations

Another practical angle is how the AI services fit into your existing technology stack and strategic vendor ecosystem:

• Platform integration: If your organization is heavily invested in Microsoft’s ecosystem (Azure cloud services, Office 365, Power Platform, Dynamics, etc.), Azure OpenAI will slot in seamlessly. Developers can use Azure SDKs, deploy AI alongside existing cloud resources, and implement single sign-on and role-based access via Azure AD. Microsoft also integrates OpenAI models into products such as Power BI, Azure Data Factory, and Logic Apps, making it easier to incorporate AI into workflows with minimal custom code. On the other hand, going directly with OpenAI means you have a neutral API endpoint. You can certainly use OpenAI from any environment (AWS, on-premises, etc.), which gives flexibility outside the Microsoft world. However, you’ll need to build additional integration plumbing yourself (for example, wiring API calls into your applications, handling security tokens, and logging usage separately, etc.).
• Multi-cloud strategy: Many enterprises pursue a multi-cloud approach or avoid locking into a single vendor for all critical services. Using OpenAI directly can be seen as a way to stay vendor-agnostic for AI capabilities. You could host your solution on AWS or Google Cloud and still call OpenAI’s API without involving Microsoft. If your relationship with Microsoft is sour or you want to negotiate better cloud pricing, your AI usage isn’t tied to them. Conversely, if you are deeply partnered with Microsoft, leveraging Azure OpenAI might strengthen that partnership (and possibly give you more leverage in Microsoft contract negotiations overall, as AI consumption becomes part of your spend). It’s worth considering the bigger picture: does integrating AI with our primary cloud provider simplify operations, or do we value a separate approach?
• Adjacent tools and services: Microsoft offers a growing suite of AI-related tools (Azure Cognitive Services, AI model customization with Azure Machine Learning, data orchestration, etc.). If you foresee using these around your GPT-4 implementations, Azure OpenAI will integrate more smoothly with them. OpenAI’s direct service is more focused – it gives you the raw model APIs and leaves the rest to you. Some enterprises are willing to mix and match best-of-breed solutions, while others prefer an integrated approach. Additionally, consider user-facing products: Microsoft is embedding OpenAI models into Office 365 via Copilot, as well as into GitHub with Copilot for code, among other applications. These are separate from Azure OpenAI, but illustrate Microsoft’s end-to-end vision. Signing with Azure might align you with that roadmap (e.g., early previews, unified licensing discussions), whereas signing with OpenAI keeps you strictly at the API level usage for now.

Real-world example:

A global retailer evaluating AI may test both options – building a prototype chatbot using OpenAI’s API and another using Azure OpenAI. They might find the OpenAI build quick and straightforward, with lower cost during testing.

However, when it comes time to integrate with their customer data and deploy at scale, the Azure version could shine due to its easier connection to their databases and existing monitoring capabilities.

The final choice might hinge on priorities: speed and independence (OpenAI direct) vs. enterprise integration and support (Azure).

Many enterprises actually choose to maintain both: using OpenAI for R&D and innovation projects, and Azure OpenAI for production deployments that require rigorous controls. There is no one-size-fits-all answer – it truly depends on your company’s needs.

Recommendations

For organizations negotiating or managing OpenAI-related contracts, here are practical tips to protect your interests and maximize value:

1. Map Your Requirements: Document your must-haves (e.g., data residency, SLA uptime, HIPAA compliance) early. Use this to determine if one provider is a non-starter or if both remain options. This clarity will also strengthen your negotiating position on contract terms.
2. Leverage Vendor Relationships: If you have a strong Microsoft partnership, let them know you’re considering OpenAI directly – this can pressure Microsoft to offer incentives (like Azure credits or discounting) to keep your AI workload on Azure. Conversely, when engaging with OpenAI, mention your large enterprise status and existing cloud usage; OpenAI might extend volume discounts or enhanced support to secure your business.
3. Negotiate Data and IP Terms: Whichever contract you pursue, explicitly address data usage and intellectual property in writing. Ensure it states that your company retains ownership of both inputs and outputs. For OpenAI, get confirmation that your data won’t be used to train models. For Azure, Microsoft has a document that they’ll honor any stricter internal policies you have (e.g., no human access to data).
4. Secure an SLA (if needed): If AI is mission-critical, don’t settle for best-effort service. With OpenAI, request an enterprise SLA or a dedicated instance arrangement. With Azure, verify the standard SLA and consider provisioning throughput units for guaranteed capacity. Make sure support expectations (response times) are part of the deal too.
5. Plan for Cost Control: Treat AI usage like any cloud service in terms of cost governance. Set up budgets, alerts, and regular cost reviews to ensure accurate financial management. Negotiate a discount tier or a committed spend deal if your usage is expected to be high. Both OpenAI and Microsoft have programs for enterprise volume commitments – take advantage of those rather than paying the sticker price indefinitely.
6. Run a Pilot with Both: If feasible, pilot your use case on OpenAI’s platform and Azure OpenAI before you sign a long-term agreement. This hands-on trial can reveal practical differences in integration effort, latency, result quality, and unforeseen limitations. It also gives you data to negotiate — for instance, “we saw slower response times on Azure, how will that improve with enterprise support?”
7. Consider Hybrid Approach: You don’t necessarily have to choose exclusively. Some enterprises utilize Azure OpenAI for specific sensitive workloads, while OpenAI is used directly for less sensitive or more experimental projects. This can optimize both risk and innovation. If you choose this route, establish clear internal guidelines on which use cases are assigned to which platform to avoid confusion.
8. Stay Updated: The AI landscape is evolving monthly. New offerings (like OpenAI’s ChatGPT Enterprise, or other cloud providers partnering with OpenAI) could emerge. Keep an eye on announcements. For example, if OpenAI launches new compliance features or if Microsoft changes Azure OpenAI pricing, you may want to revisit your strategy or renegotiate if possible. Include contract clauses that give you flexibility as things change (avoid long lock-in without escape clauses).
9. Involve Legal and Security Teams Early: Both options raise novel issues (liability for AI output, data handling, ethical use). Get your legal, compliance, and infosec experts reviewing terms early on. They might find that Microsoft’s contract is easier to approve, or they might flag a concern that requires a special addendum with OpenAI. Address these before the final sign-off stage.

Checklist: 5 Actions to Take

1. Define Use Cases and Risk Profile – Assemble a cross-functional team (IT, legal, compliance, procurement) to list what you plan to do with OpenAI’s technology and what risks are involved. Classify data sensitivity, uptime needs, and regulatory requirements for each use case. This will help determine whether Azure’s compliance or OpenAI’s flexibility is a better fit (or which use cases are best suited to each).

2. Engage Both Vendors for Initial Quotes/Info – Reach out to OpenAI’s enterprise sales and your Microsoft account representative. Obtain information on enterprise packages, pricing estimates for your projected usage, and any special requirements (for Azure, you’ll need to apply for access if not already approved). Use this to create a side-by-side comparison of offerings specific to your organization (including any custom terms they propose).

3. Pilot in a Controlled Environment – Set up a small-scale trial with each service. For OpenAI, use the API on a limited dataset or internal app to gauge performance and integration effort. For Azure OpenAI, deploy a test instance in your Azure tenant and integrate with a sample workflow. Monitor response times, outputs, and any integration hiccups. Also, test worst-case scenarios (e.g., heavy load, complex queries) to see how each handles them.

4. Evaluate Contracts and Negotiate – Have your legal team review OpenAI’s MSA/terms and Microsoft’s Azure terms side by side. Note differences in liability, data clauses, termination rights, etc. Based on this, draft a list of points to negotiate. For OpenAI, you might ask for an amendment to data use or a higher liability cap. For Microsoft, consider requesting an assurance of model availability or extra training for your staff. Enter negotiations with a clear list of asks. Remember to also negotiate pricing – for example, seek a commit discount or Azure consumption credit. Don’t hesitate to let each vendor know you have alternatives; use that as leverage to improve terms.

5. Make an Informed Decision (and Plan Management) – Decide which route (or combination) aligns best with your company’s needs now and 2-3 years ahead. Once signed, implement governance. If OpenAI is direct, set up vendor management processes (regular business reviews with OpenAI and monitoring their product changes). If Azure, incorporate the AI service into your existing cloud governance (cost tracking tags, Azure policy for data, etc.). Additionally, establish internal usage policies by training your teams on how to use the AI API responsibly and setting guidelines on what data can or cannot be sent to the model, depending on the platform’s guarantees.

Following this checklist will help ensure that whichever partnership you choose – OpenAI or Microsoft – you maintain control and clarity over the deployment and avoid common pitfalls.

FAQ

Q1: Is there a big difference in model quality or features between OpenAI and Azure OpenAI?
No, the core models (GPT-3.5, GPT-4, etc.) are the same underlying technology in both offerings. The difference lies in the surrounding service. OpenAI’s direct API might sometimes roll out new model versions or features slightly sooner in certain cases. Still, Microsoft’s Azure OpenAI Service offers the same major models, often with a short delay if any. For most enterprises, feature parity is effectively equal – your GPT-4 prompt will yield comparable results from either platform. The choice is not about the AI’s capability but about the service wrapper (security, integration, support) around it.

Q2: Which option is more cost-effective for a large enterprise?
It depends on your usage pattern and existing agreements. OpenAI’s direct pricing is straightforward and can be cheaper for pure usage (since you’re not paying for extra cloud services around it). Enterprises can negotiate volume discounts with OpenAI if usage is high. Azure’s pricing per call is similar, but you may incur additional costs (such as reserved capacity or related Azure resources). However, if you already have an Azure spending commitment, using Azure OpenAI might effectively result in lower costs (e.g., it can utilize your committed credits or earn you a higher discount tier). Also, consider the value of Azure’s included services – for example, if Azure’s integration saves development effort, that indirectly saves cost. In short, small-scale or unpredictable usage often favors OpenAI’s direct billing, while large, steady usage can be negotiated favorably on either platform (Azure via an enterprise agreement, OpenAI via a custom contract). Always run the numbers for your scenario.

Q3: How do support and SLAs compare in practice?
Microsoft, in partnership with Azure OpenAI, offers a traditional enterprise support structure. You can open Azure support tickets 24/7 and expect a response according to your support plan. They have an SLA for uptime (typically around 99.9%). In practice, Azure’s service has been stable, but any issues you encounter can be addressed through Microsoft’s support. OpenAI’s direct support is improving, but learners; critical production issues might not get immediate attention unless you have a high-tier contract. Without an SLA in the base offering, outages must be tolerated or worked around. If uptime and rapid support are crucial, enterprises often feel more at ease with Microsoft’s backing. However, OpenAI is also keen to please enterprise clients – some organizations report that after signing an enterprise deal, OpenAI assigned solution engineers to assist and was responsive. It comes down to the level of support you negotiate for and the importance of a formal SLA guarantee to your business.

Q4: What about data security – is one option safer than the other for our sensitive data?
Azure OpenAI is generally seen as “safer” for sensitive data out of the box because it keeps everything within Azure’s controlled environment and offers compliance assurances (including HIPAA BAA for health data and customer-managed encryption). If your data security team trusts Azure’s cloud, they will likely be comfortable with Azure OpenAI. OpenAI’s direct service can also be used securely, as it does not utilize your API data for training and provides encryption in transit and at rest. The main differences are contractual and operational: with OpenAI, you’ll rely on their promises and security measures, which are strong but not audited by your vendor risk standards in the same way Azure might be. Also, OpenAI won’t localize data in a specific region for you – data is processed in the U.S. (primarily), which could be a compliance issue for some EU companies. Microsoft allows you to select an Azure region (e.g., EU data center) for inference, which can aid with GDPR compliance. In summary, both can be used safely, but Azure offers more knobs and certifications to align with corporate security requirements.

Q5: Can we switch from one to the other later if we choose wrong?
Switching is possible but not instant. On a technical level, both services utilize similar APIs (Azure’s API is very close to OpenAI’s standard API, with minor differences), allowing code and applications to be adapted to the other platform with some effort. The bigger challenge is data and operational integration. If you build a lot of Azure-specific integration (using Azure’s identity, monitoring, etc.) and then decide to move to OpenAI directly, you’ll need to replace those with new solutions. Likewise, if you start with OpenAI and later want to bring it into Azure for compliance, you’ll have to migrate your usage and perhaps retrain models or move data into Azure storage. Contracts will also need to be handled: you’d have to terminate or wind down one agreement and sign another, respecting any notice periods or minimum commitments. Thus, while not a permanent lock-in, there’s friction in switching. Many enterprises mitigate this by designing applications in a cloud-agnostic manner (containerized or with abstraction layers for AI calls), so that if needed, they can redirect the API endpoint from one provider to another. But it’s best to choose with a 1-2 year horizon in mind. If you have any doubts, consider running pilot projects on both in parallel until you’re confident in a direction.

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