u/OneBillSoftwares

One pricing model collapse is getting all the attention. Another one is flying under the radar — and it might matter more.

The one everyone's talking about: per-seat SaaS giving way to outcome-based pricing. If agents do the work, you don't buy seats. You buy results.

The quieter one: Time & Material billing — the backbone of professional services for decades. Human hours were the proxy for value. Log them, bill them, track utilization. The entire operating model built on that single assumption. Agents don't have billable hours. They just execute. And the human-hours model is breaking.

Which brings us to Service-as-Software. SaaS meant software delivered as a service — humans still drove the work. Service-as-Software flips it: the service is delivered by software. In some workflows, agents are Copilots — first line, assisting humans who own the outcome. In others, Autopilots — executing autonomously, humans handling only exceptions. Most real engagements will run all three in parallel: human hours, assisted hours, agent-executed hours. our PSA needs to track all of it. No PSA today was built to.

Open questions — genuinely curious what people think:

• If human hours, copilot hours, and compute hours all contribute to delivery — how do you bill for all three?
• If utilization rate is the operating metric of a human-driven PSA, what replaces it in a Service-as-Software world?
• How do you track project margin when costs are split between salaries and compute?
• Does PSA need a new expansion — Professional Services Agents instead of Professional Services Automation?

One pricing model collapse is getting all the attention. Another one is flying under the radar — and it might matter more.

The one everyone's talking about: per-seat SaaS giving way to outcome-based pricing. If agents do the work, you don't buy seats. You buy results.

The quieter one: Time & Material billing — the backbone of professional services for decades. Human hours were the proxy for value. Log them, bill them, track utilization. The entire operating model built on that single assumption. Agents don't have billable hours. They just execute. And the human-hours model is breaking.

Which brings us to Service-as-Software. SaaS meant software delivered as a service — humans still drove the work. Service-as-Software flips it: the service is delivered by software. In some workflows, agents are Copilots — first line, assisting humans who own the outcome. In others, Autopilots — executing autonomously, humans handling only exceptions. Most real engagements will run all three in parallel: human hours, assisted hours, agent-executed hours. our PSA needs to track all of it. No PSA today was built to.

Open questions — genuinely curious what people think:

• If human hours, copilot hours, and compute hours all contribute to delivery — how do you bill for all three?
• If utilization rate is the operating metric of a human-driven PSA, what replaces it in a Service-as-Software world?
• How do you track project margin when costs are split between salaries and compute?
• Does PSA need a new expansion — Professional Services Agents instead of Professional Services Automation?

Disclosure: I lead GTM at OneBill, a quote-to-cash platform. We work with SaaS companies on price experimentation and product bundling.

The honest answer — stop trying to pick the right model and start building the infrastructure to experiment before you commit. Here's what that actually looks like on the ground:

• A/B testing pricing structures across customer cohorts before committing publicly
• Segmenting customers by actual usage patterns rather than assumed personas — consumption signals that SaaS companies never had to track in a per-seat world
• Measuring customer sentiment toward different pricing options before they become policy
• Bundling products flexibly to smooth the transition for existing customers while opening new monetization lanes
• Running multiple pricing models simultaneously across different segments — per-seat for established enterprise accounts, consumption-based for new PLG-driven cohorts, outcome-based for customers who came in expecting to pay for results
• Supporting PLG and SLG motions concurrently — new AI-native companies use product-led growth as price discovery. Usage data tells them what to charge before they formalize a pricing structure. That requires billing infrastructure that can meter, capture, and report on consumption from day one

The billing platform underneath all of this is no longer a back-office detail. In a period of pricing model transition, it becomes a strategic asset.

Curious what others are seeing on the ground — are your finance and RevOps teams already tracking consumption signals, or is pricing model transition still mostly a boardroom conversation? And for those who've tried running multiple pricing models simultaneously across different customer segments — what broke first?