Enterprises spent roughly 12.5 billion dollars on foundation model APIs in 2025, according to Menlo Ventures’ research with 495 U.S. enterprise AI decision makers, while the cost of accessing a given level of AI capability kept falling. Buyers are paying more in aggregate to rent intelligence precisely because it keeps getting cheaper and better per call.
Model as a Service (MODaaS) is the arrangement behind that spending: an organization accesses a provider’s trained system via an API and pays for usage, most often per token, while the vendor retains ownership of the engine and the serving infrastructure. It became the default because frontier capability advance faster than any owned asset can be paid off, which shifts the executive question from which engine to own toward how to rent engines well.
What MODaaS means in practice
The engine itself, the training runs, and the servers stay with the supplier, while prompts, retrieval, proprietary data, orchestration, and evaluation sit with the buyer and determine whether a general capability translates into business value.
Three neighboring paths compete with it: running open-source models on company infrastructure, fine tuning a pretrained system on internal data for a specific task, and training a new system from scratch in house, each trading extra control for heavier capital and scarcer talent.
Why metered access became the default, and where Tokenomics enters
Training a leading system costs hundreds of millions of dollars, and its capability starts aging when a stronger successor ships, so an owned engine behaves like a depreciating asset.
Metered access replaces a large upfront investment with a pay-as-you-go operating cost, which helps explain why spending on foundation model APIs continues to grow.
Usage-based billing also gives rise to Tokenomics, the discipline of knowing what each token costs and what business value it returns. Unit prices keep falling, yet agentic workflows chain many calls into a single task, so total consumption can climb faster than prices fall. Ronny Fehling, HTEC’s Chief AI Transformation Officer, examines the procurement implications in a point of view article titled The AI-Enabled Operating Model.
Faster releases, and a new dependency
The best available system now changes every few months, and buyers tend to stay with a chosen vendor and move to its newest release, inheriting gains without rebuilding the stack. That steady advancement also creates a dependency, since a company’s capability becomes tied to a lab’s release cadence, pricing, and deprecation schedule, a risk that grows as more processes run on rented intelligence.
If everyone rents the same engines, where does advantage come from?
David Wingate, Barclay L. Burns, and Jay B. Barney argued in MIT Sloan Management Review in 2025 that access to AI does not by itself create a durable advantage, and when competitors call the same endpoints, the capability becomes table stakes. Differentiation migrates to the layers a supplier does not ship: proprietary data, retrieval quality, orchestration, evaluation, and the workflow integration that converts a call into a measurable result. The data foundation matters most because it is the one input no competitor can rent.
Where MODaaS reaches its limits, and what to govern
Regulated data, sovereignty requirements, latency-critical or edge workloads, and extreme token volumes can justify running a model on infrastructure the company controls, or a hybrid posture that splits traffic between that setup and an external API. Depending on an outside provider also brings governance work that a self-hosted setup largely avoids: deciding where prompts and data travel, managing cost volatility through token budgets and usage monitoring, and preserving portability so a vendor can be swapped without a rebuild. Buyers increasingly weigh cost, latency, reliability, and governance ahead of raw capability, and HTEC’s guide to Build or Buy Enterprise AI Agents covers the choice between building in-house and purchasing.
The decision that outlasts any single release
Every rented engine will give way to a stronger one, so the assets that last are the data that grounds each answer, the evaluation that proves an upgrade is safe, and the portability that that lets a company change vendors without rewriting its applications. The question for a CTO is whether those three exist today.
Working with HTEC
HTEC works alongside enterprises architecting how they access, govern, and build on foundation models across regulated and large-scale environments, with depth in evaluation, orchestration, and portability across vendors.lds up under real conditions.
FAQ
What is Model as a Service in simple terms?
MODaaS lets a company use a provider’s pretrained AI system through an API and pay only for what it uses, usually per token. The provider owns the technology and the infrastructure behind it, while the customer keeps its prompts, data, and orchestration.
Why do most enterprises consume AI models through an API rather than train their own?
Training a frontier system costs hundreds of millions of dollars, and its capability ages as soon as a stronger release appears. Metered access turns that outlay into spend that follows usage and lets buyers adopt each upgrade without reengineering.
What is Tokenomics and why does it matter for MODaaS?
Tokenomics is the practice of managing the cost and value of every token an AI system consumes, from prompt to response. Agentic workflows multiply calls per task, so cost per business outcome tells a truer story than the price of a single token.
Does MODaaS create vendor lock-in, and how do enterprises manage it?
It can, because capability becomes tied to a vendor’s release cadence, pricing, and deprecation schedule. An abstraction layer between applications and any single API, evaluations that can be rerun against alternatives, and company-owned data let an organization change providers by retesting in place of rebuilding.
When does it make sense to self-host a model instead of using MODaaS?
Regulated data, residency and sovereignty requirements, latency-critical or edge workloads, and extreme token volumes can justify running a model inside the company’s own environment. Many organizations settle on a hybrid posture that routes sensitive or high volume traffic there and sends everything else through a metered service.
If competitors use the same foundation models, how do we build a competitive advantage with AI?
Differentiation moves to what a supplier does not ship: proprietary data, retrieval, orchestration, evaluation, and workflow integration. HTEC works with enterprises on that layer through its Data, Analytics & AI practice, starting with getting a company’s own information organized, trustworthy, and ready for AI to draw on.
