MCP: The Protocol That Taught AI to Use Tools
Why Every GenAI Builder Faces the Same Trilemma, and How to Escape It
The age of Generative AI is here. For the builders, innovators, and product leaders on the front lines, the mission is clear: build great products. Generative AI is our newest and most powerful tool, and it has completely changed the rules of the game.
But as the initial excitement wears off, a tough new reality is setting in. The path from a brilliant idea to a real, scalable, and profitable AI application is full of difficult compromises. As product builders, we see this every day. We are all stuck in what we identify as the GenAI Product Trilemma an impossible choice between moving fast, controlling costs, and prioritizing consumer trust whilst keeping our products secure. Our current infrastructure options are forcing us into a corner, holding us back from real success.
This isn’t just a theory; it’s the daily struggle for product and engineering teams everywhere. In a recent talk, Neysa’s Chief Product Officer, Karan Kirpalani, broke down this very problem, arguing that we’re being forced to make a false choice.
In the following read we break down the three sides of this trilemma, using real-world data to show the traps. Most importantly, it will introduce you to a new, smarter way forward – a third option designed to free your teams to do what they do best: build.
The New Reality: The Old Product Playbook Is Broken
For years, we have championed building a good software. The process was predictable. We understood development cycles, managed our costs, and knew our biggest risk was a software bug. But…, building an AI product is completely different.
Suddenly, our plans depend on whether we can get enough GPUs. Our profits are eaten up by unpredictable “inference costs” every time a user interacts with our AI. And our biggest product risk isn’t a bug in the code, but a model “hallucination” that destroys the trust we have with our users.
This new reality means every product leader has three clear goals:
- Ship Fast: Get to market quickly to win over customers before competitors do.
- Build Profitably: Create a product with healthy unit economics that actually makes money as it grows.
- Be Reliable: Build a secure and stable product that earns and keeps user trust.
The problem is, our current infrastructure choices make it nearly impossible to achieve all three at once. We are forced to sacrifice one, and each choice comes with a huge cost.
Breaking Down the Trilemma: The Two Flawed Paths
To understand the trap we’re in, let’s break down the two traditional paths available to us today.
Path 1: The Hyperscaler – The Tempting Promise of Speed
The Promise: Get to market now. Use a big cloud provider’s services to build a prototype and launch in weeks, not years.
This is the most popular path, and it’s easy to see why. Hyperscalers like AWS, Google Cloud, and Azure offer easy-to-use AI tools and models through APIs. Your team can spin up a proof-of-concept in a matter of hours, impressing everyone. You have solved for Time-to-Market.
The Reality: You Sacrifice Unit Economics.
The good times end the moment you try to scale. That rocket ship of rapid growth suddenly explodes under the weight of its own costs.
Your costs for running the AI model (inference costs) spiral out of control. The fees for moving your data around become a painful line item that gets your CFO’s attention. A 2023 report from Sequoia Capital made it clear: while training an AI model is expensive, running it for users can account for up to 90% of the total cost at scale. When you rely on a big provider’s premium model, you pay a heavy and unpredictable tax for every single user interaction.
This leads to major business problems:
- Exploding Costs: Your cloud bill becomes a wild, unpredictable expense. A successful product launch can accidentally cause a financial crisis, forcing you to limit features or slow down user growth just to control the bill.
- Loss of Control: Your product’s features and pricing are now tied to your cloud provider. If they change their prices or discontinue a model you use, your entire plan is at risk. As Andreessen Horowitz has pointed out, this can crush your profit margins over time.
- Data Security Risks: For industries like finance or healthcare, sending your most sensitive data to a third-party model is a huge compliance and security risk.
The conclusion is simple, you cannot build a profitable product if your core costs are volatile and unpredictable. By choosing the hyperscaler path, you’ve given up on profit just to be fast.
Path 2: The ‘Do-It-Yourself’ approach – The Long Road to Control
The Promise: Build everything yourself for total control, predictable costs, and rock-solid security.
After getting burned by high cloud bills, many consider this option. It offers complete control over your hardware, models, and data. You solve for Unit Economics and Trust.
The Reality: You Sacrifice Time-to-Market.
This path is a multi-year, multi-million-dollar project. While you are busy building your perfect system, the market moves on without you.
The challenges are huge:
- Huge Upfront Costs: Building your own AI infrastructure requires a massive investment in specialized GPUs. In today’s market, getting these chips is like an arms race, with long waits and fierce competition.
- Hiring Is Nearly Impossible: The number of engineers who are experts in MLOps, GPUs, and complex systems is extremely small. You’re competing for the same rare talent as Google and Meta.
- A Technical Nightmare: Your team spends all its time fighting with infrastructure managing servers, optimizing GPUs, and patching together different open-source tools; instead of building features your customers want.
The conclusion here is just as clear, you cannot win the market if you show up too late. The long journey to build it yourself means that by the time you’re ready, your competitors have already captured the customers.
The Hard Truth: The Old Tools Are Broken
This is the trilemma we’re all stuck in. We are forced to choose between shipping fast, building affordably, and building reliably. It feels like a no-win scenario.
The clear conclusion is that our existing tools are broken. They simply weren’t designed for this new world of AI product development. We need a new model.
The Third Way: The Sovereign, Full-Stack AI Cloud
What if we could design a platform from the ground up, specifically to solve this problem?
It would need the speed and ease of a public cloud but with the cost-control and security of a private one.
The new blueprint: the Sovereign, Full-Stack AI Cloud.
This is more than just another tool. It’s a complete, integrated system, from the physical hardware all the way up to the application. It’s made of three connected layers:
- Sovereign IaaS: A solid foundation of dedicated GPUs and storage that you control, giving you guaranteed access to the power you need.
- Integrated PaaS: A unified platform that brings MLOps, data management, and other key tools together, so you don’t have to stitch them together yourself.
- Accelerated SaaS: A marketplace of ready-to-use models and applications that lets your team build on top of existing solutions instead of starting from zero.
This new model solves the trilemma by refusing to compromise. At Neysa, we have built this blueprint into our platform, Velocis. It’s designed to be the engine for AI product teams, giving you the tools to ship faster, the setup to control your costs, and the foundation to build with confidence.
The Real Advantage: A Great Developer Experience (DevEx)
As a product leader, we believe the best predictor of a company’s ability to innovate is its developer experience. If your best engineers are spending their time fighting with infrastructure, they aren’t building your next great feature.
The best AI products are built by happy, productive developers.
This third way is obsessed with the developer experience. It hides the complexity of the underlying infrastructure, providing a simple way for developers to work. With just a few lines of code, they can start a project, train a model, or launch a new feature.
This frees your teams from the headache of managing infrastructure and lets them do what they do best: build.
From Theory to Practice: How Teams Are Escaping the Trilemma
This isn’t just an idea. We are already helping some of India’s top companies escape the trilemma.
For example, a leading media company used Velocis to build and scale its recommendation engine. They were able to increase user engagement by 23% by quickly testing and improving their AI models. Most importantly, they did this without hiring a huge infrastructure team, keeping their costs predictable even as their user base grew 10x. They didn’t have to choose they got speed, cost-control, and trust, all at once.
Your New Goal: Demand More
Our message to you today is simple. The trilemma is not a law of nature; it’s a limitation of old tools.
As product leaders, it’s time to demand something better. It’s time to demand a platform that is actually built for the way we build products today.
The opportunity to build game-changing AI products has never been greater. The only thing holding us back is the friction in our development process. Let’s remove that friction.
Let’s build the future, together.
Stop choosing between Speed, Cost, and Control.
Start building on a platform that delivers all three.
