AI Pricing Strategies for SaaS Companies Offering Copilots including Microsoft

When Microsoft announced Microsoft 365 Copilot , many of us pondered how they would price the solution. Would it be per-user pricing, as it does with Office 365 bundles, or would Microsoft end up doing consumption-based pricing or a hybrid of these two?

I mentioned in my blog posting that the challenge for software vendors is estimating the potential consumption of the underlying AI services from cloud platforms that the software vendor is using. I also described in another blog posting how different GenAI solutions are from traditional SaaS solutions due to changes in cost dynamics and how these GenAI solutions are architected and constructed.

According to Ibbaka , Takeshi Numoto , executive vice president and chief marketing officer at Microsoft, is one of the architects behind Microsoft's cloud bundles. The questions that Microsoft had to answer were as follows:

• When should Microsoft charge for AI directly, and when should it be included in other applications?
• Which bundles should Copilot and other AI solutions be included in?
• What pricing metrics should be used?
• What should the pricing level be?

Customers have quite a few bundles from which to choose. Microsoft had to ask whether to include Copilot in all existing bundles or, for example, only in Microsoft 365 E3 and Microsoft 365 E5.

An existing software vendor has multiple different options when implementing new technology to an existing platform:

??It could add the new technology/solution to the existing bundles without calling it out separately.

??It could also add the technology/solution as an add-on to existing bundles at an additional cost.

??The third option would be to create a new foundation or AI platform that is specialized and not part of the existing bundles.

I have been part of the same questions in the envisioning/business design workshops where a software vendor wanted to move away from an on-premises environment to the cloud and a subscription-based business model. Many software vendors offered native cloud extensions as add-ons that could only be purchased through a subscription-based model.

If the add-on is offered as an extra cost to a bundle (like in the case of Microsoft 365 Copilot), Microsoft had to ask whether they could use the same pricing metric (like price/user). That would be the logical selection for most cases and the easiest way for the customer to understand. However, this approach could be challenging as the pricing metric might need to align with the cost of operating AI, and the software vendor might lose money.

Companies using platforms like Open.AI, Anthropic, or similar patterns will pay for token input and?output. This could be a problem for the software vendor as the usage per user can't be predicted.

In an article in Techcrunch , Box unveils a unique AI pricing plan to account for the high cost of running LLMs. The same applies to Box and all software vendors offering software solutions and IP to end customers. What Box has done is to "blend per-user pricing and an enterprise pool. Each user gets 20 credits that they can use for any function that calls Box AI. This could be creating content in Box Notes or asking questions about specific documents. Once an individual user has used all of their individual credits they can dip into a pool of 2,000 AI credits available to the company.

Microsoft ended up with an add-on pricing of Microsoft 365 Copilot that charges the client $30/user/month. By doing pricing research, Microsoft had to ask whether the customer would be willing to pay that price. According to Ibbaka's article , Microsoft had to answer some key questions as follows:

1. Is the purpose of AI to enhance existing revenue streams or to create new revenue streams? (A company as large as Microsoft with a diverse product portfolio can pursue both of these goals in different parts of its portfolio, but most companies do not have the scale or scope to do this)
2. Is the goal to optimize volume, revenue, or profit? (These are different goals, and you can only pick one).
3. How does AI create value, and who does it create value for? (This is the foundational question and where most companies should begin their pricing research; if the answers to the ‘who’ and ‘how’ questions are the same as for conventional products, then use existing pricing metrics and package this as a product enhancer or product extension, if the answer to either of these questions is different than you have a new product)
4. Does the AI enhance current packages and bundles, or should it be used to create new bundles? (This is where Microsoft will spend most of its time).

So, the question is how some other known SaaS companies have implemented their Copilot pricing. Tomaz Tunguz has analyzed pricing strategies for SaaS companies offering copilots, and the following table portrays how the base price is in relation to the AI price with an associated ratio.

The table above lists the company, the product, the base price per seat for the enterprise plan if available (otherwise, the team plan), the price for the AI or co-pilot add-on, and the ratio between the AI price and the base price.

When you compare AI Copilot pricing to the list price, the following chart shows how each company prices its AI to the list price:

This clearly shows that, for example, Google charges more for its AI features than the base seat, while Loom charges about a 32 percent premium.

Microsoft plays different roles in the AI domain, with Azure AI providing all the foundational AI services needed to build AI solutions. Microsoft 365 with Microsoft Graph provides a unified API for modern work and is the foundation for Microsoft 365 Copilot. Microsoft has a special relationship with Open.ai GPT models and several others, and it also allows customers and partners to develop their own models . Microsoft has also embedded Copilot in numerous other applications and will continue to do so.

We are living in an exciting era concerning pricing strategies, as Generative AI is enabling the development of new-generation solutions. I expect lots of trial and error along the way, which we will read about over time.

If you are building Copilots, have you already established your pricing strategy for them? It would be interesting to hear your feedback and thoughts on this topic. I will continue researching pricing and monetization of AI solutions, so stay tuned for more.

Yours,

Dr. Petri I. Salonen

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