With price adjustments three times a year, even Salesforce can't handle it. What exactly makes AI pricing so difficult?

In the traditional SaaS world, changing the pricing structure is a "major event" that requires a five - year demonstration cycle.

Now, even giants like Salesforce have changed their pricing models three times within 12 months.

This seemingly casual remark made by the co - founder of Metronome in an interview reveals a rapidly changing business reality: The rise of AI and usage - based billing is reshaping the business models and organizational methods of SaaS enterprises.

Metronome is at the center of this reconstruction.

Founded in 2019, this company is committed to providing high - performance billing platforms for software enterprises in the AI era. It can convert raw events (such as API calls, data writes, and model inferences) into billable metrics in real - time and automatically generate complex invoices. During peak billing periods, it can process hundreds of thousands of invoices per minute; and the number of usage events processed throughout the month can reach tens of billions.

As a result, Metronome's ARR increased sixfold in 2023, making it one of the fastest - growing "invisible gears" in AI infrastructure.

Not long ago, Metronome co - founder Scott Woody and a16z partner Martin Casado had a discussion about AI pricing. In Scott's view, AI pricing is not just a change in the payment logic, but also an organizational structure reconstruction.

When enterprises no longer charge by "per seat" but capture value around "per call". This means that every department and every role must operate around the questions of "whether the customer really used it", "how much they used", and "whether it's worth using". Thus, all aspects, from pricing, sales, contracts, billing to financial analysis, are being completely rewritten.

01 Pricing is no longer a financial action but a part of the product

The founder of Metronome recalled his experience at Dropbox:

Back then, even a simple price experiment (such as raising the price of some users from $9.99 to $11.99) required changes to the code of the billing system. And this system was extremely fragile. It might take months to launch a single change, extending the entire experiment cycle to one or even two quarters.

The most crucial problem is that the traditional billing system operates on a "run a script once a month" background logic, while users use the product, check usage, and calculate prices every day. The result is: Customers often realize the price adjustment after the price has changed and the fees have been deducted, which is undoubtedly a disastrous user experience incident.

From then on, he began to realize that the billing system is no longer just a "tool" for finance. It must become a part of the product and is an important interface for users to interact with every day.

02 Why is usage - based billing so difficult?

In the AI era, the pricing logic has undergone a fundamental transformation.

The pricing models in the entire software industry have actually gone through three stages:

1. On - Premises (On - Prem) era: Charge a one - time fee for "permanent license" to buy out the right to use.

2. Cloud era: Charge by "seat/subscription", and the billing logic is linked to the number of users. Representative companies include Salesforce and Zoom.

3. AI era (Usage/Value Era): Charge by "the value generated", and the pricing unit shifts from "people" to "workload" or "results".

The change behind this is not simply "pay - as - you - go", but the migration of the software's value anchor point: In traditional SaaS, the value of software lies in "how many people can use it", which is linked to the organizational scale. In AI - driven products, the real value of software becomes "how many things it can help me do" - write code, solve customer service tickets, automatically process data...

Software is no longer a tool but a "digital employee". This also means that the number of users no longer equals the upper limit of value. The growth of value depends on the intensity, accuracy, and persistence of the work performed by the agent.

03 Why is usage - based billing so difficult?

Although "pay as you use" sounds reasonable, it is very complex in practice. Sam summarized three core challenges:

1. Extremely high real - time requirements

Traditional SaaS settles accounts monthly, but usage - based billing requires the system to monitor call situations in real - time, detect abnormal consumption, and avoid large - scale bill explosions. For example, if the wrong API is called or the wrong task is run, it can cost tens of thousands of dollars in an hour.

2. Highly complex and dynamically changing pricing logic

Each contract of large customers is different, and the discounts and pricing rules for each service may vary, often requiring manual processing. This makes it difficult for a standardized system to handle all dynamic situations, and a small bug can turn into a customer crisis.

3. "Financial - level" data accuracy is required

The billing data does not allow 99% accuracy. Only 100% correctness is acceptable. Otherwise, it is financial fraud. Therefore, the billing system not only needs to be real - time but also support auditing, tracing, and legal challenges.

This is why the "billing system" is being upgraded from an auxiliary module to the core infrastructure of the enterprise.

04 It's not just about pricing but also an organizational structure reconstruction

If in the Seat model era, pricing was a decision more related to operations, in the Usage model, pricing becomes a pre - design of the organizational operation mode.

Because once it comes to usage - based billing, every department and every role must operate around the questions of "whether the customer really used it", "how much they used", and "whether it's worth using".

Sam gave a real - life example:

A listed company processes more than $1 billion in usage - related revenue annually, but its billing and contract management still rely on manual operations. The reason is simple - each customer contract is different and cannot be coded, so it can only be maintained manually.

These aspects that should have been systematic have now become a bottleneck for the enterprise's growth.

Shifting to usage - based billing cannot just change the pricing page. What is really needed is a redesign of the entire business engine. Sam prepared a five - item change list for the CEO:

1. Reshape the sales incentive mechanism

In the traditional sales model, salespeople get commissions once they sign a deal. In the usage model, salespeople must be responsible for the customers' subsequent real - world usage behavior. This means that commissions may be paid in stages according to the number of calls or consumption amounts; the responsibility boundaries between AEs (sales) and CSMs (customer success) need to be re - defined; some companies even let sales, product, and CS teams jointly bear the revenue target.

2. Redefine the role of the customer success (CS) team

In the past, CS teams were more like renewal promoters. Now they need to become value coaches: help customers get started quickly and access the core functions of the product; teach customers to use the product well, precisely, and economically; actively recommend best practices instead of just responding to problems.

Excellent CS teams often have the ability of technical consultants and can help customers "spend smartly" rather than "spend a lot", just like an "on - site CTO".

3. Make the product team responsible for "revenue indicators"

The product team can no longer only focus on user activity. Instead, they need to be responsible for indicators directly related to revenue, such as usage intensity, value indicators, and call frequency; cooperate with the growth team to build a cycle of "product is growth, usage is revenue". When designing features, they should consider how this feature can increase user usage and reduce marginal costs?

4. Transform finance from a "quarterly reporting department" to a "real - time data department"

Traditional finance reconciles accounts quarterly. In the usage model, billing data is "real - time operational data": Track consumption changes daily to judge trend fluctuations; help sales predict the inflection point of customer consumption; cooperate with the product team to evaluate the impact of new features on revenue. In short, finance must evolve into a "strategic data department".

5. The CEO must set a "non - negotiable implementation deadline"

Without a strong leader, the change is likely to be dragged down by organizational inertia. The CEO must: Clearly identify the person in charge. It is best to set up a "pricing leader" or a "usage strategy leader"; Set a launch deadline for the new model and promote it in stages; Require all departments to adjust their goals, processes, and tools in a coordinated manner. Sam even said bluntly that if the CEO does not personally lead the change and set a clear rhythm and implementation deadline, it will end up with "the pricing committee discussing for a year without changing a single line of code".

05 Reconstruct the product as the bill, and the price as the brand

In the past, pricing was a supporting action after the product was launched.

Now, pricing itself is a part of the product experience and also a part of the brand expression.

Some new - wave companies directly use "pricing strategies" as market weapons: Some companies directly adopt a "cost + fixed gross profit" strategy to capture the market at extremely low prices; Some companies use free or ultra - low - price strategies to obtain massive data and user habits; Some companies directly launch challenging pricing. For example, Intercom promises that if the resolution rate of its AI customer service is lower than 65%, it will compensate you with $1 million. This "price is the brand" approach is not only a dimensionality - reduction blow to competitors but also strengthens its own value - alignment stance: What we sell is not functions but effects; not the right to use but the results.

06 The AI - driven "value re - evaluation" super - cycle has begun

Usage - based billing is not new, but in the AI era, it has become a necessity. There are several structural factors behind it:

1) AI has transformed "software as a service" into "software as an employee". Traditional SaaS is a tool, while AI SaaS is an agent. You are not buying a "function that can be clicked and used" but hiring a "digital employee that can do things for you".

2) AI calls incur "real costs" and cannot be hidden. Different from traditional code running logic, each AI model call has a unit cost. OpenAI calls, inference tasks, and image generation are all "priced APIs". This requires enterprises to be able to design, control, track, and dynamically optimize price models.

3) The market has entered an era of "winner - takes - all + strong brand lock - in". The AI market is accelerating its concentration. The first - batch of companies that emerge use efficient usage models to compress marginal costs and lock in users' minds with their brands. Whoever takes the initiative in usage - based pricing will have the initiative in the market.

07 Conclusion

You can regard usage - based billing as a pricing method.

You can also see it as the "operating system" of the organizational structure of future AI enterprises.

All aspects behind it, including product design, sales incentives, financial logic, and technical systems, must be updated. In this super - cycle, the only things that will be rewarded are the speed of action and the agility of the organization.

If companies in the previous era started with subscriptions, the winners in the next era will rely on the real control of "value".

This article is from the WeChat official account "Crow Intelligence Talk". Author: Smart Crow. Published by 36Kr with authorization.