The API Gateway, once the clean, single entry point to your burgeoning microservices architecture, has become a source of dread. Latency is creeping up, deployments are a complex ballet of dependencies, and every new feature adds another layer of convoluted routing logic. What was designed to be a streamlined facade has slowly morphed into a distributed monolith in its own right—a central bottleneck that chokes performance and slows down development teams. This is a common failure pattern that even the most competent engineering teams can fall into. The initial benefits of centralizing cross-cutting concerns like authentication and rate-limiting are so compelling that the gradual accumulation of business logic and complex transformations goes unnoticed, until it doesn't.
This isn't a sign of initial architectural failure; rather, it's a natural consequence of a system's evolution under pressure. As more services are added and client requirements diversify, the gateway's responsibilities expand. Soon, it's not just routing traffic but also performing complex data aggregations, protocol translations, and serving multiple front-end experiences. The result is a component that is difficult to test, risky to deploy, and a single point of failure for the entire system. For Tech Leads and Engineering Managers, this translates into a direct operational headache: slow response times trigger alerts, developer productivity grinds to a halt, and the promise of microservice agility feels like a distant memory. This playbook is for you. It's a pragmatic guide to diagnosing the root causes of your API gateway bottleneck and a framework for executing a recovery strategy without resorting to a high-risk, big-bang rewrite.
Key Takeaways
- Diagnose, Don't Guess: An API gateway bottleneck is rarely a single issue. It's often a combination of excessive payload transformations, inefficient caching, network latency from things like VPC links, and chatty interactions with downstream services. Use distributed tracing and metrics to pinpoint the actual source of latency before attempting a fix.
- The Gateway is for Traffic, Not Business Logic: The most common failure pattern is letting business logic and complex data aggregation creep into the gateway layer. The gateway's primary role is to handle cross-cutting concerns like authentication, rate-limiting, and routing. When it starts acting like an application server, it becomes a bottleneck.
- Recovery is Incremental, Not a Rewrite: Fixing a gateway bottleneck doesn't require a complete overhaul. The most successful strategies are incremental. Options range from optimizing in-place (e.g., improving caching, offloading auth) to gradually decomposing the gateway using patterns like Backend for Frontend (BFF) or adopting GraphQL Federation.
- Decision Framework for Recovery: The right recovery path depends on your specific bottlenecks and organizational structure. A decision matrix comparing options based on implementation effort, operational overhead, performance impact, and team autonomy is crucial for making a strategic choice.
Why This Problem Exists: How Good Intentions Create Gateway Bottlenecks
No one sets out to build a slow, monolithic API gateway. These bottlenecks are born from a series of well-intentioned decisions that accumulate over time, creating a technical debt that eventually comes due. Initially, the API gateway is a model of clean architecture. It provides a single, unified entry point for all clients, abstracting away the complexity of the underlying microservices ecosystem. It's the perfect place to handle cross-cutting concerns: authentication, authorization, rate limiting, logging, and metrics collection. Centralizing these functions prevents duplicating this logic in every single microservice, saving significant development effort and ensuring consistency. This initial success is what plants the seeds for future problems.
As the organization grows, so do the demands on the gateway. The marketing team wants a new mobile app that requires a slightly different data format. Instead of building a new service, the quickest solution is to add a payload transformation directly in the gateway. A partner needs access to a subset of data from three different services. Again, the path of least resistance is to create an aggregation endpoint in the gateway that calls the three services and stitches the responses together. Each of these decisions, viewed in isolation, is pragmatic. It solves a business problem quickly. However, repeated over dozens of sprints, this pattern transforms the gateway from a simple traffic cop into a complex application server, saddled with business logic it was never designed to handle.
This phenomenon is exacerbated by team structure and ownership. Often, a single platform team owns the central API gateway. When a product team needs a change, it's faster for them to request a small tweak in the gateway configuration than to spin up and maintain a new service. The platform team, focused on enabling other teams, obliges. Over time, the gateway becomes a shared resource where every team's specific needs are implemented, leading to a tangled web of dependencies. This tight coupling means that a change for one team can inadvertently impact another, and deploying the gateway becomes a high-stakes, coordinated event that slows down the entire engineering organization.
Finally, technical limitations and misconfigurations play a significant role. For instance, using synchronous, blocking calls to downstream services means the gateway holds connections open, waiting for the slowest service to respond. Inefficient caching strategies, or a complete lack of caching for frequently accessed, non-transactional data, force the gateway to repeatedly fetch the same information, adding unnecessary load on backend services. In cloud environments, network configurations like VPC Links can introduce subtle latency that adds up across thousands of requests. These individual technical issues combine with the accumulated business logic to create a perfect storm, turning the component designed for efficiency into the primary bottleneck of the entire system.
How Most Organizations Approach It (And Why That Fails)
When faced with a slow and complex API gateway, most engineering teams default to one of two knee-jerk reactions: either throwing more hardware at the problem or initiating a high-risk, all-or-nothing rewrite. The first approach, horizontal scaling, is often the initial response. If the gateway is slow, the logical first step seems to be to simply run more instances of it. While this can alleviate issues caused by pure CPU or memory exhaustion, it completely fails to address the root cause of most bottlenecks: latency in downstream dependencies and inefficient processing logic. If your gateway is slow because it's waiting on a 2-second response from a legacy backend service, scaling the gateway from 3 to 30 instances won't make that backend service any faster. It just means you have 30 instances all waiting for 2 seconds.
The second common failure is the 'Big Rewrite' initiative. A team of architects, frustrated with the gateway's complexity, will propose replacing the existing gateway with a new, supposedly superior technology. They might advocate for switching from a self-managed Kong or NGINX setup to a fully managed service like AWS API Gateway, or vice-versa. Another popular pitch is to replace the entire REST-based gateway with a GraphQL Federation layer, promising to solve all data fetching problems in one fell swoop. The problem with this approach is that it fundamentally misunderstands the issue. The problem isn't the technology; it's the accumulated architectural decisions and business logic embedded within it. A rewrite often just ports the same flawed logic to a new platform, burning months of engineering effort only to arrive at a slightly different version of the same bottleneck.
A more subtle but equally ineffective approach is localized, tactical optimization without a holistic view. One team might notice a specific endpoint is slow and add a caching layer. Another team might optimize a database query for a different endpoint. While these individual fixes can provide temporary relief, they don't address the systemic issues. The gateway remains a tightly coupled monolith. Without a coordinated strategy, these point solutions can even add to the complexity, creating a patchwork of different caching rules, timeout configurations, and error-handling logic that makes the gateway even harder to manage and reason about. This reactive, whack-a-mole approach fails because it treats the symptoms, not the underlying architectural disease.
Finally, many organizations fail by not properly diagnosing the problem in the first place. They rely on anecdotal evidence—'the app feels slow'—or basic metrics like overall average latency. They lack the detailed, distributed tracing needed to see the full lifecycle of a request as it flows through the gateway to various downstream services. Without a trace that clearly shows 'we spent 30ms in the gateway, 500ms waiting for Service A, and 800ms waiting for Service B,' it's impossible to know where to focus optimization efforts. Teams end up guessing, often blaming the gateway itself when the real culprit is a slow network hop or an unoptimized database query three services deep. This lack of visibility ensures that any attempt to fix the problem is a shot in the dark.
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Schedule a ConsultationA Recovery Framework: From Diagnosis to Incremental Refactoring
Recovering from a gateway bottleneck requires a systematic, data-driven approach, not a frantic rewrite. This framework breaks the process down into two main phases: Deep Diagnosis and Incremental Refactoring. The goal is to make a series of small, low-risk changes that deliver measurable improvements, gradually transforming the gateway back into a lean and efficient component. This approach minimizes disruption and allows you to demonstrate value to the business at every step. It’s about evolving your way out of the problem, not trying to leap out in a single bound.
Phase 1: Deep Diagnosis and Quick Wins. Before you change a single line of code, you must understand precisely where the latency is coming from. This is non-negotiable. The primary tool for this is distributed tracing (using tools like Jaeger, Zipkin, or cloud-native solutions like AWS X-Ray). A trace visualizes the entire request lifecycle, showing exactly how much time is spent in the gateway, in the network, and in each downstream microservice. This immediately reveals whether the bottleneck is CPU-bound work within the gateway (e.g., complex payload transformations) or I/O-bound waiting for other services. Once you have traces, identify the top 5-10 slowest or most frequently called endpoints. For these, analyze the traces to pinpoint the source of latency. Concurrently, implement some 'quick win' optimizations that often yield significant improvements with low effort. These include: enabling Gzip compression, reviewing and setting appropriate cache-control headers, and normalizing cache keys to ignore irrelevant query parameters (like UTM tags) that cause cache misses.
Phase 2: Strategic Recovery and Incremental Refactoring. With data in hand, you can now choose a strategic path for recovery. This isn't about picking one 'best' pattern, but about selecting the right pattern for your specific problems. The options generally fall on a spectrum from simple optimization to full decomposition. You can start by offloading specific functionalities from the gateway. For example, if authentication is taking significant time, you can move it to a dedicated sidecar proxy or even to the service mesh layer. If the gateway is aggregating data from multiple services for various clients, this is a strong signal to introduce the Backend for Frontend (BFF) pattern. Create a new, lightweight BFF service for each client type (e.g., a Mobile BFF, a Web App BFF). These BFFs become responsible for the aggregation logic, freeing the central gateway to focus purely on routing and security. This is a form of gradual decomposition.
For more advanced scenarios, especially where multiple teams are competing for resources on the gateway, you might consider more profound architectural changes. One powerful approach is to use the Strangler Fig pattern. You can stand up a new, leaner gateway (or a BFF) alongside the old one. Using a load balancer or CDN, you can route a small percentage of traffic, or traffic for a specific new feature, to the new component. Over time, you migrate more and more endpoints until the old gateway is 'strangled' and can be safely decommissioned. For organizations struggling with data over-fetching and under-fetching, introducing a federated GraphQL layer can be a powerful long-term solution. A GraphQL gateway allows clients to request exactly the data they need, but it comes with its own complexity and governance overhead. The key is to choose the right strategy based on the evidence gathered in the diagnosis phase, using the decision matrix below to weigh the trade-offs.
Decision Matrix: Choosing Your API Gateway Recovery Strategy
Once you have diagnosed the root causes of your API gateway bottleneck, the next step is to choose a recovery strategy. There is no one-size-fits-all solution; the right choice depends on your specific context, including the nature of the bottleneck, your team's skills, and your long-term architectural goals. This decision matrix provides a framework for comparing the most common recovery patterns across key dimensions: implementation effort, performance impact, operational overhead, and team autonomy.
The matrix below outlines four primary strategies, ranging from low-effort optimizations to significant architectural shifts. Use this as a guide to facilitate a discussion with your engineering team and stakeholders to select the path that best aligns with your needs.
Decision Artifact: API Gateway Recovery Strategy Matrix
| Strategy | Description | Implementation Effort | Performance Impact | Operational Overhead | Best For... |
|---|---|---|---|---|---|
| 1. Optimize In-Place | Tune the existing gateway configuration. Focus on caching, compression, connection pooling, and right-sizing timeouts. Offload heavy computations like complex payload transformations to backend services. | Low | Medium | Low | Systems where the bottleneck is caused by misconfiguration or inefficient use of gateway features, not fundamental architectural flaws. |
| 2. Offload Cross-Cutting Concerns | Move specific, heavy responsibilities out of the gateway. For example, move complex authentication/authorization logic to a dedicated auth service or a service mesh sidecar. | Medium | Medium-High | Medium | Gateways that have become bloated with too many cross-cutting concerns, where a few specific functions are responsible for most of the latency. |
| 3. Decompose with Backend for Frontend (BFF) | Introduce new, smaller gateways (BFFs) for specific client applications (e.g., Mobile BFF, Web BFF). The BFFs handle aggregation and transformation logic, while the central gateway handles routing and security. | High | High | High | Organizations with multiple client teams (web, mobile, partner) that have different data requirements and are slowed down by a single, monolithic gateway. |
| 4. Adopt GraphQL Federation | Replace the REST aggregation layer with a federated GraphQL gateway. Each microservice exposes its capabilities as a GraphQL schema (a subgraph), and the gateway composes them into a single supergraph. | Very High | Very High (for read-heavy APIs) | Very High | Complex ecosystems with many services and clients where data over-fetching and under-fetching are major problems. Requires a significant investment in tooling and governance. |
How to Use This Matrix:
- Start with Diagnosis: Your tracing and monitoring data from the diagnosis phase should point you toward one or two primary problem areas. If latency is due to repeated database calls for the same data, 'Optimize In-Place' by improving caching is a clear starting point. If traces show the gateway is spending 300ms on data aggregation for the mobile app, 'Decompose with BFF' is a strong candidate.
- Evaluate Effort vs. Impact: Always start with the lowest-effort, highest-impact solution. Don't jump to a full BFF decomposition if simply enabling Gzip compression and fixing cache headers can cut your p95 latency by 50%.
- Consider Team Structure: The BFF pattern naturally aligns with autonomous, full-stack product teams. GraphQL Federation requires a strong central platform team or guild to manage the supergraph schema and enforce governance. Choose a pattern that fits your organizational design.
- Plan for Incremental Adoption: None of these strategies need to be a big-bang change. You can introduce a BFF for a single new feature. You can federate one or two services into a new GraphQL endpoint while the rest of the system continues to use the existing REST gateway. This incremental approach de-risks the migration and allows you to learn as you go.
Common Failure Patterns: Why Recovery Attempts Stumble
Even with a solid framework, attempts to fix an API gateway bottleneck can fail. These failures are rarely technical; they are almost always rooted in process, governance, and a misunderstanding of the problem's scope. Recognizing these anti-patterns is the first step to avoiding them and ensuring your recovery efforts succeed.
Failure Pattern 1: The 'Magic Bullet' Technology Fallacy. This is the most common pitfall. The team becomes convinced that a specific technology, like GraphQL or a new service mesh, is a 'magic bullet' that will solve all their problems. They spend months migrating to the new platform without ever addressing the underlying complexity. For example, a team might replace a REST API gateway that performs complex data aggregation with a GraphQL gateway. However, they simply move the complex data-joining logic from the gateway's code into complex GraphQL resolvers. The result is the same: a slow, monolithic data access layer, just with a different syntax. The failure here is focusing on the 'how' (the technology) before fully understanding the 'what' (the architectural problem of tightly coupled data and business logic). The technology is a tool, not a strategy. Without fixing the architectural flaws, you're just repainting a sinking ship.
Failure Pattern 2: Lack of Observability and Measurement. A recovery effort that doesn't start with a clear, measurable baseline is doomed to fail. Teams often embark on a refactoring project with a vague goal like 'improve performance'. But what does that mean? Are you trying to reduce p99 latency, lower CPU utilization on the gateway nodes, or decrease the number of calls to downstream services? Without specific, measurable KPIs, you can't prove whether your changes are having a positive, negative, or neutral impact. For example, a team might spend weeks implementing a sophisticated caching layer, but without pre- and post-deployment metrics on cache hit ratio and origin load, they have no way of knowing if it's actually working. Success requires defining clear metrics upfront (e.g., 'reduce p95 latency for the `/products` endpoint by 50%') and using distributed tracing and monitoring tools to track progress against that goal.
Failure Pattern 3: Ignoring the 'Human' Layer and Governance. You can have the best architectural pattern in the world, but if you don't fix the organizational processes that created the bottleneck in the first place, it will inevitably reappear. The original gateway likely became a monolith because it was the path of least resistance for product teams to get features shipped quickly. If you decompose a monolithic gateway into multiple BFFs but don't give the product teams ownership and autonomy to manage their own BFFs, the central platform team will once again become the bottleneck. Effective recovery requires establishing clear governance. This means defining clear ownership boundaries, creating a 'paved road' for teams to create and manage their own facades or BFFs, and implementing automated checks (e.g., linting for query complexity, performance testing in CI/CD) to prevent bad practices from creeping back in. Without this governance, architectural drift is inevitable.
Failure Pattern 4: The Inner-Platform Effect. This is a subtle but dangerous failure mode where, in an attempt to solve the gateway problem, a team builds an overly complex, highly abstract internal platform that becomes a product in itself. They create a custom DSL for routing, a bespoke plugin system for transformations, and a complex deployment pipeline. Soon, other developers have to spend more time learning the internal platform than building their actual services. The platform becomes so complex and brittle that only its original creators understand it, and they become the new bottleneck. The goal of recovery is to simplify and decompose, not to build a new, more complex monolith. The solution is to favor standard, well-documented open-source tools and established patterns over building custom frameworks wherever possible. Leverage existing solutions like NGINX, Envoy, or managed cloud services, and focus your engineering effort on your core business logic, not on reinventing the wheel for API management.
A Smarter, Lower-Risk Approach: The Path to a Scalable Edge
The smartest path forward is not a revolutionary rewrite but a disciplined, evolutionary journey. It's about making deliberate, data-informed decisions that unwind complexity over time while continuously delivering value. This approach prioritizes stability, measurement, and team autonomy, ensuring that the recovery process itself doesn't become a bigger problem than the one it's trying to solve. It starts with a fundamental mindset shift: the API gateway is not an application; it is infrastructure. Its job is to route, secure, and observe traffic, not to run business logic. Every decision should be weighed against this principle.
The first step in a lower-risk approach is to establish a 'truth baseline' with comprehensive observability. Before any refactoring begins, ensure you have distributed tracing, metrics, and logs that give you a clear picture of every request's journey. This data is your compass. Use it to create a prioritized backlog of bottlenecks. Instead of a vague goal to 'make things faster,' you will have concrete, actionable tasks like 'The `GET /user/profile` endpoint spends 80% of its time waiting for the `legacy-auth-service`. We need to decouple this.' This data-driven approach removes emotion and opinion from the equation and allows you to focus on the changes that will have the most significant impact with the least effort, like optimizing caching or removing redundant data transformations.
Next, adopt the Strangler Fig pattern as your primary methodology for change. Never modify the live, monolithic gateway directly if you can avoid it. Instead, stand up a new, lightweight gateway or BFF next to the old one. Use your load balancer or CDN to route a single, low-risk endpoint or a small percentage of users to the new infrastructure. This creates a safety net. If something goes wrong, you can instantly route traffic back to the old system. This incremental approach allows you to test your new architecture in production with minimal blast radius. You can gradually migrate endpoints one by one, strangling the old gateway over weeks or months until it has no traffic left and can be safely retired. This is the opposite of the high-risk 'big bang' rewrite.
Finally, empower your development teams with ownership and a 'paved road' for managing their own API facades. The central gateway became a bottleneck because it was the only option. The solution is to make creating a dedicated Backend for Frontend (BFF) the easiest and most supported path for product teams. This means providing teams with pre-configured templates, CI/CD pipelines, and clear documentation for spinning up their own lightweight gateways. The central platform team's role shifts from being gatekeepers of a monolithic component to being enablers of a distributed, well-governed ecosystem. By giving teams the autonomy to manage the API layer that serves their specific client, you decentralize the bottleneck and align architectural ownership with product ownership, ensuring that the gateway monolith never grows back.
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Get a Quote2026 Update: The Rise of AI and Edge-Native Gateways
As of 2026, the pressures on API gateways have intensified, driven largely by the explosion of AI-powered applications and the increasing sophistication of edge computing. The patterns described in this playbook remain fundamental, but their application is evolving. AI agents and Large Language Model (LLM) integrations have introduced a new class of 'chatty' API interactions that can easily overwhelm traditional gateways. An application orchestrating multiple AI agents might make dozens of small, rapid-fire API calls to gather context, which puts immense pressure on gateway latency and connection handling. This has made gateway-level caching and connection pooling not just optimizations, but critical requirements for survival.
Furthermore, the need to reduce latency for these real-time AI interactions is pushing more gateway logic to the edge, closer to the user. Modern CDNs are no longer just for static content; they provide programmable environments (like Cloudflare Workers or AWS Lambda@Edge) that can run significant gateway logic. This allows for tasks like authentication, header manipulation, and even simple request routing to be handled at an edge location just milliseconds from the user, before the request even begins its journey to your core infrastructure. A smarter approach today often involves a hybrid model: a lightweight gateway at the edge for ultra-low-latency tasks and a more robust regional gateway to handle complex routing and integration with backend services. Choosing the right balance between edge and regional responsibilities is now a key architectural decision.
Security at the gateway has also become more complex. With AI agents acting as autonomous clients, traditional user-based rate limiting is insufficient. Gateways must now implement more sophisticated throttling strategies based on factors like the complexity of the incoming query (especially for GraphQL), the cost of the downstream operation, and the identity of the machine-based agent. This requires gateways to have deeper visibility into the payloads they are processing. The key takeaway for 2026 and beyond is that the API gateway is not a static component. It is a dynamic control plane that must adapt to new traffic patterns and security threats. The principles of diagnosis, incremental refactoring, and clear ownership are more important than ever in managing this evolving complexity.
Conclusion: From Bottleneck to Enabler
The journey of an API gateway from a clean architectural pattern to a performance-stifling bottleneck is a cautionary tale of accumulated technical debt and good intentions gone awry. However, recovery is not only possible but can be achieved without the high-risk drama of a complete rewrite. The key is to treat the problem as an architectural challenge, not a technological one, and to approach the solution with discipline, data, and a focus on incremental change. By systematically diagnosing the true sources of latency and bloat, you can move beyond guesswork and apply targeted, high-impact optimizations.
The path to a scalable, resilient edge architecture hinges on a few core principles. First, re-establish the gateway's primary role as a traffic manager, not an application server, by systematically offloading business logic and complex transformations. Second, leverage patterns like the Backend for Frontend (BFF) and the Strangler Fig to decompose the monolith gradually, de-risking the process and demonstrating value along the way. Finally, and most critically, solve the organizational problem by establishing clear governance and empowering teams with the autonomy and tools to manage their own API facades. This prevents the bottleneck from simply reappearing in a new form.
Ultimately, a well-architected API gateway is a powerful enabler of developer velocity and system scalability. By navigating the recovery process thoughtfully, you can transform this critical component from a source of frustration back into the strategic asset it was meant to be, ensuring your architecture can support the growth and innovation your business demands for years to come.
This article has been reviewed by the Developers.dev Expert Team, comprised of certified cloud solutions architects and enterprise software engineering leaders.
Frequently Asked Questions
What are the first signs that my API gateway is becoming a bottleneck?
The earliest signs are often subtle. You'll notice a gradual increase in p95 and p99 latency for key endpoints, even when downstream services report normal response times. Development teams may start complaining that deploying changes to the gateway is becoming slow and risky, requiring coordination across multiple teams. Another key indicator is when discussions about new features frequently involve adding logic (e.g., data transformation, aggregation) to the gateway layer instead of creating new microservices. If your distributed traces show a significant amount of time spent 'inside' the gateway itself rather than waiting for I/O, you have a confirmed bottleneck.
Can't I just scale my API gateway horizontally to fix performance issues?
Horizontal scaling (adding more instances) can help if your bottleneck is purely CPU or memory-bound within the gateway itself. However, it's often a temporary fix that masks deeper issues. If your gateway is slow because it's making synchronous calls to slow downstream services, adding more gateway instances won't make those services any faster. Each instance will still be stuck waiting. True scalability comes from addressing the root cause of the latency, whether it's unoptimized backend queries, network hops, or inefficient data processing, not just from adding more compute power at the gateway layer.
What is the difference between an API Gateway and a Backend for Frontend (BFF)?
An API Gateway is typically a single, centralized entry point for all clients, handling broad, cross-cutting concerns like security, rate-limiting, and routing to various microservices. A Backend for Frontend (BFF) is a more specialized pattern where you create a dedicated backend service (which can itself be a lightweight gateway) for each specific frontend or client application (e.g., a web app BFF, an iOS BFF). The BFF is responsible for handling the specific data aggregation and formatting needs of its client, while the central API Gateway might still handle ingress and global security policies. Decomposing a monolithic gateway into multiple BFFs is a common and effective recovery strategy.
When should I consider GraphQL Federation instead of a traditional REST API Gateway?
Consider GraphQL Federation when your primary problem is data fetching inefficiency—specifically, over-fetching (getting more data than you need) and under-fetching (having to make multiple API calls to get all the data for a single view). If your clients are frequently making many sequential REST calls to stitch together a complete picture, GraphQL can be a powerful solution. However, it's not a simple replacement. It introduces a new set of complexities around schema governance, query cost analysis, and caching. It's a significant architectural shift best suited for complex ecosystems where the benefits of a flexible, client-driven data query model outweigh the operational overhead.
How do I use the Strangler Fig pattern to refactor my API gateway?
The Strangler Fig pattern involves gradually replacing the old system with a new one. To apply it to an API gateway, you would: 1. Place a routing layer (like a load balancer or CDN) in front of your existing monolithic gateway. Initially, it routes 100% of traffic to the old gateway. 2. Create a new, lightweight gateway (or a BFF) for a single new feature or to replace a single endpoint from the old gateway. 3. Configure the routing layer to send traffic for that specific endpoint (e.g., `/api/v2/new-feature`) to the new gateway, while all other traffic continues to go to the old one. 4. Over time, you incrementally migrate more endpoints to the new gateway. 5. Once all traffic is routed to the new system(s), the old monolithic gateway is 'strangled' and can be safely decommissioned.
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