If you’re running Apigee while simultaneously managing a separate Kafka infrastructure, congratulations, you’ve accidentally built the architectural equivalent of a Rube Goldberg machine. One team member recently vented on a software architecture forum: “We started using Kafka heavily this year and Apigee doesn’t support it at all, so now we’re managing two completely separate systems for APIs and event streams.” The frustration is real, the bill is climbing, and the XML configurations nobody understands anymore are just the cherry on top.
This isn’t a isolated complaint. It’s the sound of a entire category of tools reaching its expiration date.
The Great API Gateway Reckoning
For years, monolithic API management platforms like Apigee have positioned themselves as the “Rolls Royce” of API infrastructure, comprehensive, enterprise-grade, and expensive enough to make you think you’re getting something special. But the ground has shifted. Modern architectures aren’t just about RESTful request-response patterns anymore. They’re event-driven, multi-cloud by default, and demand declarative configuration that fits into GitOps workflows.
The research is stark: teams are hitting a wall where their API gateway, the tool meant to simplify connectivity, has become another silo to manage. When you’re paying enterprise licensing fees for features you barely use while simultaneously building workarounds to connect your event streams, something fundamental is broken.
The Kafka Problem Nobody Talks About
Here’s the dirty secret traditional API gateway vendors don’t put on their marketing slides: they were never designed for event streaming. Apigee’s architecture predates the mainstream adoption of Kafka, and retrofitting event support into a system built for synchronous HTTP requests is like adding a jet engine to a horse carriage. Sure, you might get some movement, but you’re missing the point.
The technical mismatch runs deep. Apigee’s XML-based configuration system, already a source of team-wide confusion, has no native concept of topics, partitions, consumer groups, or stream processing. When your entire event-driven architecture revolves around these primitives, forcing them through a gateway that treats everything as a stateless HTTP call creates a translation layer that kills performance and operational clarity.
Multi-Cloud: The Lock-In Amplifier
If the Kafka compatibility issue weren’t enough, Apigee’s GCP-centric design creates another layer of pain. One engineering manager put it bluntly: “The GCP lock-in is annoying since we use multiple clouds, and our bill keeps climbing for features we barely use.” This isn’t just about vendor preference, it’s about architectural sovereignty.
Modern enterprises run workloads across AWS, Azure, and GCP, often with on-premises components still in the mix. An API gateway that subtly (or not-so-subtly) nudges you toward a single cloud provider’s ecosystem becomes a strategic liability. You’re not just locked into a tool, you’re locked into a cloud provider’s vision of how APIs should work, how they should be billed, and how they should integrate with other services.
The numbers tell the story. While Apigee touts “multi-cloud” capabilities, the reality is that its deepest integrations, pricing advantages, and newest features all point toward GCP. For organizations genuinely committed to multi-cloud strategies, whether for resilience, cost optimization, or avoiding single-vendor dependency, this creates a constant tension between the architecture you want and the tool you’re stuck with.
The Rise of Unified API-Event Platforms
The market is responding with tools that treat APIs and event streams as first-class citizens in the same platform. Gravitee, for instance, offers a Kafka gateway that exposes event streams as managed APIs. Kong is going even further, announcing their Event Gateway for Q4 2025, which will enable developers to “expose, manage, and secure real-time event streams from Apache Kafka as Konnect-managed APIs and services.”
This isn’t just feature parity, it’s a fundamental rethinking of what an API gateway should be. Instead of treating events as an afterthought, these platforms recognize that modern applications need both synchronous and asynchronous communication patterns, managed through a single control plane with consistent policies, observability, and developer experience.
What Modern Gateways Actually Look Like
Declarative Configuration: While Apigee users wrestle with XML, modern alternatives like Kong support declarative configuration via YAML, managed through decK for GitOps workflows. This isn’t just about preference, it’s about treating API infrastructure as code, enabling versioning, code review, and automated deployment.
Performance That Doesn’t Make You Cry: Kong’s recent LMDB backend for configuration delivers a 50-70% reduction in rebuild time during constant configuration pushes. When you’re running dynamic environments with frequent updates, this isn’t a nice-to-have, it’s the difference between responsive automation and deployment bottlenecks.
AI-Native Capabilities: Kong AI Gateway 3.8 introduces semantic plugins and advanced load-balancing specifically for LLM traffic. As APIs become the supply chain for AI systems (a point The New Stack emphasizes), your gateway needs to understand the unique patterns of model inference, not just HTTP requests.
Here’s how you configure distributed rate limiting with Redis in Kong, something that becomes critical when you’re handling both API calls and event stream consumption:
curl -i -X POST http://localhost:8001/plugins \
--data name=rate-limiting \
--data config.policy=redis \
--data config.hour=1000 \
--data config.limit_by=consumer \
--data config.sync_rate=1 \
--data config.redis_host=my-redis-host.cache.amazonaws.com \
--data config.redis_port=6379 \
--data config.redis_password=mysecurepasswordThis level of granularity, limiting by consumer, with synchronous updates for accuracy, backed by a distributed cache, is what it takes to manage modern workloads.
The AI Supply Chain Connection
Here’s where it gets really interesting. The New Stack analysis frames APIs as the “new AI supply chain”, and they’re absolutely right. In a RAG architecture, your AI model isn’t just calling one API, it’s orchestrating calls to product specifications, customer history, policy rules, and pricing logic simultaneously.
If your gateway can’t manage event streams, you’re not just creating operational overhead, you’re cutting off a critical data source for AI systems. Real-time event streams carry customer behavior, system state changes, and business events that static APIs simply can’t provide. When your AI system needs to reason about what’s happening right now, events are non-negotiable.
The governance requirements become more complex too. As the article notes: “Can autonomous agents call this API? Under what limits? Does the API expose data that a model is allowed to consume under regulation?” These aren’t theoretical concerns, EU AI Act and similar regulations are making AI governance a legal requirement, not just a best practice.
The Cost Structure Reality Check
Let’s talk money, because this is where the rubber meets the road. Apigee’s enterprise pricing model, combined with GCP’s data egress fees, creates a compounding cost problem. You’re paying premium rates for a platform that requires you to also build and maintain a separate Kafka management layer.
The comparison table from recent research puts this in perspective. While Apigee scores high on feature depth (4.8/5), it’s also “one of the most expensive products in the market” with a “steep learning curve and very complex setup process.” Kong, by contrast, offers a massive plugin ecosystem and high performance with more flexible deployment options.
When you’re budget-conscious (and who isn’t?), tools like KrakenD and Kong OSS deliver state-of-the-art performance at zero licensing cost. For budget-conscious teams, this isn’t just attractive, it’s existential.
The Event Gateway Revolution
The most telling sign that the old guard is falling behind? The feature roadmap. Kong’s Event Gateway announcement for Q4 2025 represents a fundamental shift in how we think about API management. It’s not about bolting on event support, it’s about rearchitecting the platform to treat events and APIs as equal citizens.
This means:
– Unified policy enforcement: Apply the same authentication, rate limiting, and transformation rules to both REST endpoints and Kafka topics
– Consistent observability: Trace requests across synchronous and asynchronous boundaries
– Developer portal integration: Discover and subscribe to event streams through the same interface as REST APIs
– Declarative management: Configure everything through YAML, versioned in Git
Compare this to the Apigee experience: manual XML configuration, separate systems for events vs APIs, and a developer portal that doesn’t understand topics or consumer groups. The gap isn’t just technical, it’s philosophical.
The Path Forward: Intelligence at the Edge
The future isn’t just about supporting events, it’s about embedding intelligence at the API edge. As IBM’s approach demonstrates, modern gateways need to deploy “inference-driven policies, such as anomaly detection, contextual routing and semantic filtering, directly in gateways.”
This is the final nail in the monolithic coffin. You can’t retrofit AI-native capabilities onto a platform built for a pre-AI era. The architecture assumptions are wrong, the data models are wrong, and the operational model is wrong.
For teams still on Apigee, the question isn’t whether to migrate, it’s when, and how fast. The longer you wait, the more technical debt accumulates: custom workarounds for event streaming, ballooning costs for unused features, and a team that’s spending more time fighting XML than building products.
The Reckoning Is Already Here
The data doesn’t lie. Teams are actively seeking alternatives not because they want to, but because their architecture demands it. The rise of event-driven systems, multi-cloud strategies, and AI-native applications has created a perfect storm that legacy API management platforms simply can’t weather.
The good news? The alternatives are mature, well-supported, and often cheaper. Kong’s plugin ecosystem, Gravitee’s native Kafka support, and the emerging class of event-aware gateways offer a clear path forward. The bad news? Migration is never easy, and the longer you wait, the harder it gets.
Your API gateway should be simplifying your architecture, not complicating it. If you’re managing separate systems for APIs and events, wrestling with cloud lock-in, and paying enterprise prices for features you don’t use, you’re not just using the wrong tool, you’re building on a foundation that’s already crumbling.
The event-driven future isn’t coming. It’s here. And it’s quietly killing the tools that weren’t built for it.
