Market signals we couldn’t ignore: why we made our AI router standalone

When we started building Orq.ai, we didn’t plan to launch a standalone AI router. The goal was always to build a platform that helps teams move from AI ideas to production systems they can trust, operate, and evolve.

The decision to carve out the router and make it available on its own didn’t come from a launch plan or a pricing exercise. It came from a series of market signals that kept repeating across customers, industries, and the broader AI landscape. Over time, those signals became too consistent to ignore.

This post breaks down the signals that led us to unbundle the routing layer, and what they reveal about how AI systems are actually being adopted in practice.

Signal #1: AI sovereignty moved from policy to production reality

In Europe, AI sovereignty is no longer a theoretical discussion. It’s showing up in architecture reviews, procurement processes, and production decisions.

Teams are asking concrete questions:

• Where do our AI requests actually go?

• Which providers sit in the critical path?

• How easily can we change those decisions if regulations, risks, or business priorities shift?

This shift is visible in both regulation and buyer behavior. The EU AI Act, upcoming EU Cloud and AI legislation, and new public-sector cloud sovereignty frameworks all point in the same direction: control over AI infrastructure matters as much as model capability. At the same time, industry research shows this concern is already shaping adoption. IDC found that protection against extra-territorial data access is now one of the top reasons European enterprises seek “sovereign” AI and cloud solutions, while McKinsey reports that more than 30% of European companies have delayed cloud or AI adoption due to data locality and control concerns.

What’s becoming clear is that sovereignty isn’t enforced at the model level. It’s enforced at the routing layer – the point where applications hand control to infrastructure that decides which models are called, where inference happens, and how data flows.

Without a routing layer, these decisions are hard-coded into applications. Changing them later means rewrites, migrations, or uncomfortable trade-offs.

This is why routing is emerging as the practical enforcement point for sovereignty. Not to exclude global providers, but to preserve choice, control, and transparency as conditions change.

Signal #2: AI costs were scaling in the wrong direction

As teams move from experimentation into production, AI costs don’t just increase – they become harder to explain.

We repeatedly saw teams add routing layers to manage complexity, only to discover that infrastructure fees scaled in ways that were difficult to forecast. Platform markups, usage thresholds, and opaque pricing structures made it harder for engineering teams to justify architectural decisions internally.

The pattern was familiar:

• Teams delayed good infrastructure decisions to avoid cost surprises

• Or they bypassed governance entirely to keep projects moving

Neither approach leads to stable, production-grade AI systems.

This signal forced us to confront a core question: if routing is a foundational control layer, should it become a growing tax on AI adoption?

Our answer was no. Routing should make systems easier to scale, not harder to defend.

Signal #3: Customers were already solving this problem elsewhere

One of the strongest signals came directly from our customer behavior.

Across our user base, more than 60% of teams we spoke with already had an AI router in place when they engaged with Orq.ai whether through another tool or a homegrown solution. Others told us they were actively planning to build one.

In several cases, teams said they didn’t realize routing capabilities already existed inside the Orq.ai platform. What they wanted was clear: a lightweight, low-risk entry point that solved an immediate production problem without requiring an all-in platform commitment upfront.

This reflects how enterprises actually adopt infrastructure. Trust comes from solving one real problem well, not from feature completeness on day one.

Making the router standalone wasn’t about fragmenting the platform. It was about meeting teams where they already were.

Signal #4: Routing kept emerging as the first production bottleneck

Across industries, we kept seeing the same maturity curve.

Teams start with a single large language model. Then they add a second for cost, performance, reliability, or compliance. Very quickly, model logic spreads across services, SDKs multiply, and small changes begin to cause regressions.

This pattern shows up clearly in industry data. Recent surveys indicate that more than one-third of enterprises now run five or more LLMs in production, combining proprietary APIs with open-source and region-specific models. At the same time, analyst research consistently points to integration complexity, not model quality, as the primary blocker to scaling GenAI systems.

What breaks first isn’t autonomy or orchestration. It’s control.

Most GenAI applications are still tightly coupled to specific models. When providers change pricing, throttle usage, or experience outages, teams have limited room to adapt without rewriting parts of their systems. European enterprises feel this particularly strongly as they pursue multi-vendor strategies to reduce lock-in and increase resilience.

Routing introduces a control layer that decouples applications from models. It allows teams to define policies once around cost, latency, geography, or reliability and adapt model choices without rewriting their systems. In practice, it turns multi-model adoption from a liability into an advantage.

Our takeaway was clear: before teams can scale AI systems responsibly, they need control. Routing is where that control begins.

Signal #5: Enterprises wanted optionality, not another point solution

The final signal tied everything together.

Enterprises are tired of stitching together narrow tools, but they’re also cautious about committing too early to monolithic platforms. What they consistently ask for is optionality, the ability to start small, stay in control, and expand capabilities as systems mature.

This pattern shows up clearly once teams move beyond simple prompt-based use cases. As AI systems evolve into agents and workflows, routing, evaluation, observability, guardrails, and experimentation quickly become interdependent. Research and customer data consistently show that teams operating more than three disconnected AI tools report higher operational overhead and lower confidence in production readiness. Fragmentation becomes a drag long before autonomy does.

That’s why we believe optionality doesn’t mean more point solutions. It means modular adoption on top of an integrated foundation,  a principle we’ve explored in depth when looking at why agent engineering increasingly requires full-stack platforms rather than stitched-together tools.

For us, this signal set a clear constraint. Making the router standalone couldn’t turn it into an isolated product. It had to remain a front door into a broader system, where teams could start with routing and extend naturally into evaluation, experimentation, governance, and observability without switching platforms or rebuilding their stack.

Optionality isn’t indecision. It’s how serious AI systems actually get adopted.

What these signals told us collectively

No single signal drove this decision. It was the convergence that mattered.

Geopolitical risk, cost pressure, customer behavior, and production realities all pointed in the same direction: teams needed a routing layer they could adopt early, trust fully, and grow with.

Making our AI router standalone wasn’t a departure from our platform vision. It was a way to make that vision accessible sooner, without forcing teams to overcommit before they’re ready.

Standalone doesn’t mean short-term

Routing is not the end state. It’s the starting point.

As AI systems become more complex, the same questions around control naturally extend into evaluation, governance, observability, and experimentation. Because the router sits at the front of the stack, it becomes the foundation those capabilities build on.

Teams can stop at routing or they can go further. The important part is that they don’t have to re-architect to do so.

Start where the signal is loudest

Most teams don’t need a full AI platform on day one. They need control, visibility, and the freedom to adapt as the landscape changes.

For many teams today, the clearest signal is at the routing layer. That’s why we started there.