Resolution as a Service (RaaS) is the pricing and architectural model in which enterprise software is priced on problems solved rather than the number of users who log in. When the marginal cost of AI logic approaches zero, that distinction becomes the difference between a software business that survives the next renewal cycle and one that does not.
The era of charging for the “how” is over. For a decade, B2B SaaS founders built moats around proprietary workflows and the UI that housed them. We sold seats. We sold efficiency. We sold the promise that our software made humans better at their jobs. But as the marginal cost of logic approaches zero, those moats are evaporating in real time.
At Cameyo, we saw the early signals of this shift before the Google acquisition. It was never about the virtualization of an app. It was about the resolution of a need. Today, if your startup is still anchored to seat-based pricing while an AI agent performs the work of ten people, you are participating in a race to the bottom.
The Shift to RaaS Economics
The transition to Resolution as a Service (RaaS) is not just a pricing change. It is a fundamental decoupling of revenue from headcount. In the old model, the logic was the value. In the new model, logic is a commodity. The real value has shifted to the integrity of the output and the repository that validates it.
AI-native SaaS companies in 2026 are reporting gross margin compression to approximately 52% under seat-based pricing, because every resolution delivered by an AI agent carries real variable costs: GPU inference, model hosting, and orchestration layers. The vendors who maintain the 75% gross margins that institutional investors require are the ones who have structured their pricing to recover those costs at the resolution level rather than spreading them invisibly across a flat seat fee.
Founders who fail to bridge this transition will find themselves with a burn multiple that no Series A investor will touch. You cannot scale a business on the back of “human efficiency” when the market expects autonomous outcomes. You must own the resolution. You must charge for the “what,” not the “how.”
The Technical Reality Behind the Shift
The reason your software moat is disappearing is not just market pressure. It is a structural liquidation of logic itself. When AI can generate the “how” of a task for near zero cost, the proprietary nature of your code becomes a liability rather than an asset. To survive, your infrastructure must pivot from performing the logic to auditing the result.
The competitive moat in the RaaS era is the High-Fidelity Repository: a graph-structured institutional knowledge architecture that gives AI agents the domain context they need to execute resolutions with accuracy and auditability. Logic can be replicated. A repository built from years of customer-specific resolution history cannot.
To understand the underlying technical shift from workflow-based SaaS to high-fidelity repositories, read our deep dive on The Logic Liquidation at Middle Way in AI.
Prescription
Audit your current pricing model against the 1-to-4 Rule. The rule is precise: for every $1 of AI infrastructure and compute spend, a RaaS vendor must capture at least $4 of Resolution Value. Revenue per resolution must be at least 4 times the AI cost to serve that resolution. A tier-1 support resolution costing $0.25 to serve requires a minimum resolution price of $1.00. A complex workflow costing $2.50 to serve requires $10.00. That ratio is what returns gross margins to the 75% baseline institutional investors require.
The 1-to-4 Rule runs in one direction only: you must price resolutions at a minimum of 4 times your cost to serve. Repricing below that threshold, no matter how much value you deliver to the customer, produces margin compression that compounds as AI usage intensity grows.
Is your current roadmap focused on making the agent smarter, or on making the repository the ultimate arbiter of truth for your customer? The first is a feature. The second is a moat.