Some of your clients are already using AI services. A few of them are paying for it through you.
And if you haven’t thought carefully about how that usage gets measured, rated, and invoiced, you’re probably handling it manually. Which works fine at first. It stops working when the number of clients doing it doubles.
This is not a future problem. For the MSPs already reselling AI services, whether that’s AI phone agents, AI assistants, or similar AI-powered tools, the billing question is here now.
Why usage-based billing is the right model for AI services
The case for usage-based pricing is simple: clients who use more, pay more. Clients who use less, pay less. It’s a fair model, it’s transparent, and it reflects the actual economics of AI services.
The risk, for the industry, is letting that fairness get designed out. Fixed monthly charges and per-licence pricing have done a lot of good for SaaS, but they’ve also taken the margin out of a lot of what telecom used to sell. The MSPs who get ahead of AI billing now, and get the pricing model right, are in a better position than the ones who default to a flat fee because it’s easier to explain.
Usage-based billing for AI is still being figured out across the industry. That’s an opportunity.
How AI billing works the same way as telecom billing
When MSPs first started reselling voice and UCaaS, the billing question was identical.
Usage came in from a carrier as raw data. The MSP had to rate it, bundle it, mark it up, white-label it, and get an accurate invoice to the client. Without the right tools, that meant spreadsheets, manual calculations, and a billing cycle that could eat up days of work every month.
AI services work the same way. The usage data just looks different.
Instead of call detail records, you have token consumption, seconds of processing time, or request counts. Instead of per-minute rates, you have per-token rates. Instead of included minutes in a bundle, you have a token allowance with overage. The underlying logic is the same: something gets used, it gets measured, it gets rated, it gets invoiced.
The MSPs who figure this out early will have a clean, automated billing process for AI services from day one. The ones who don’t will end up where many of them started with voice: billing manually, losing time, and leaving money on the table.
How does AI usage billing work for MSPs?
A client uses an AI service you’re reselling. At the end of the billing period, you get a usage file. It includes how much was consumed and what type of charge applies, because not all AI usage is the same, and different types can carry different rates.
From there, the billing logic works like this:
- Usage is rated against the right rate card for each charge type
- Bundle logic applies if the client has included usage (say, 500,000 tokens per month, with overage billed separately)
- A white-labelled invoice goes to the client under your brand
- The data flows into your PSA and accounting tools, so nothing sits outside your normal workflow
No spreadsheets, no manual calculations, no billing cycle that eats up days because someone had to work through AI usage for 40 clients by hand.
Datagate handles this today. We have customers already billing their clients for AI usage through the platform, covering tokens, seconds, and requests, running through the same workflow they use for their telecom billing.
How do MSPs get set up with AI usage billing?
It’s not self-service. Datagate reviews and scopes each new feed, then quotes the consulting time to configure, test, and hand it over. How long that takes depends on whether the feed type already exists in our system. For platforms we’ve already built for, it can be as little as a couple of hours. For a net-new integration, it takes a bit longer.
Either way, once it’s running, the billing runs on its own.
Frequently asked questions
Is AI usage billing self-service?
No. Getting set up requires a custom feed build. You reach out to Datagate, the team reviews and scopes your requirements, and then provides a quote for the consulting time to configure, test, and hand over the feed. Once it’s live, the billing runs automatically.
How long does setup take?
It depends on whether the feed type already exists in Datagate’s system. For AI platforms we’ve already built for, setup can take as little as a couple of hours of consulting time. For a net-new integration, it takes longer and is scoped and quoted individually.
Can MSPs bundle AI usage in client invoices?
Yes. Bundle logic is supported, so you can offer clients an included usage allowance (for example, a monthly token allowance) with overage billed separately beyond that threshold. This works the same way as bundled minutes in a voice plan.
How is AI usage billing different from standard telecom billing?
The underlying workflow is the same: usage data comes in, gets rated against a rate card, and flows into a white-labelled invoice that goes to your client. The difference is the data type. Instead of call detail records measured in minutes, you’re working with tokens, seconds of processing time, or API request counts, depending on the AI service.
Does AI usage billing connect to PSA and accounting tools?
Yes. AI usage billing runs through the same Datagate workflow as telecom billing, which means invoices and usage data flow into your existing PSA and accounting integrations automatically.
Worth a conversation
AI services are moving from pilot to revenue line faster than most MSPs expected. If you’re already reselling AI or heading that way, the billing piece is not a problem to defer. The manual workaround that feels manageable at 10 clients gets painful at 50.
If you want to see how AI usage billing works alongside your existing telecom billing workflow, reach out at [email protected] or talk to the team. We’re already doing this for customers right now.
