Developers are moving fast, but the landscape is getting noisy. Curated toolchains, AI-native architecture, and more intelligent gateways are emerging as the keys to managing scale without sacrificing control.
It’s not new that the one theme echoed across developer discussions and partner briefings recently has been rising application complexity, and the introduction of AI into pipelines is only compounding it. Teams are accelerating delivery, embracing open models, and building for multi-cloud and edge environments, often without a clear framework for scale, security, or governance.
Traefik Labs CEO Sudeep Goswami spoke with us recently to discuss what modern application teams need to keep pace and how developers can architect for the future.
From API Gateway to AI Gateway
The need for API infrastructure is increasing as teams race to integrate AI into microservices and workflows. What used to be a simple interface between services is now a critical junction for LLMs, inferencing endpoints, and model coordination across environments.
“AI plugs right into the existing cloud-native stack. It amplifies the explosion of APIs we’ve already managed for years.”
— Sudeep Goswami, CEO, Traefik Labs
But this isn’t just about scale; it’s about control. The modern AI gateway doesn’t just route requests. It authenticates usage, enforces governance, and caches results to reduce compute overhead in real-time inferencing.
Scaling Inferencing with Caching and Computing at the Edge
Training large models requires GPUs, but running them at scale, especially across the edge, is different. Developers are optimizing performance using semantic caching, where the system stores responses to similar prompts and avoids repeat computation.
This is especially important for inferencing at the edge, where compute may be limited and latency must be minimized.
“You don’t need 100 GPUs in a rack to make AI work at the edge. You need a smart cache, a lightweight runtime, and APIs that talk to each other.”
— Sudeep Goswami
Open source runtimes like Traefik Proxy and new WebAssembly-native approaches are unlocking new possibilities for edge-native applications. With small-model deployment strategies, developers can run intelligent applications closer to the user, without rewriting heritage systems.
Prescriptive Paths, Modular Tools
One of the top concerns among teams today is tool sprawl. With dozens of emerging frameworks, LLM wrappers, inference servers, and gateway plugins, it’s hard to know where to begin. That’s why more vendors are leaning into reference architectures and curated developer paths.
Start in a sandbox with mocked APIs. Scale when you’re ready. Use modular components and open source where possible. Then, layer in premium services and management tools as needed. This pattern is becoming a best practice across industries.
“Start with OSS, scale with partners. That’s how we build a pathway from prototype to production.”
— Sudeep Goswami
The Takeaway
If you’re a developer in the cloud-native ecosystem, your architecture needs to evolve alongside your velocity. AI is no longer experimental, it’s integral. But as it scales, your APIs, gateways, and governance layers need to scale with it.
By leveraging reference frameworks, caching strategies, and smarter gateways, developers can focus on building what matters while keeping the chaos in check.
