What is our primary use case?
I have been using Kong Konnect for the last year, and it has been a great experience. Mostly, I use it as part of our API testing and integration workflows. I was not directly involved in setting it up, but I have been consistently using it while testing microservices that were exposed through Kong Konnect gateway. Over time, I became quite comfortable with how it impacts authentication and overall API behavior, especially while debugging failures in staging and CI pipelines.
The main use case for me in Kong Konnect is API testing. From my perspective as an SDET, I use it as a centralized API gateway for all our microservices. Instead of hitting services directly, all our requests are routed through Kong Konnect, which handles authentication, request routing, and rate limiting perfectly. For us in testing, it became important because we had to validate not just the API responses, but also gateway-level behaviors, such as whether the correct authentication policies were enforced and if requests were being throttled properly under load. In my company, the load is really high, so Kong Konnect provides a very reliable mechanism to maintain good uptime and quality for the APIs for our clients.
One specific way I used it was while writing and debugging API test cases for services that are behind Kong Konnect. I was validating authentication flows and rate limiting, and I used ChatGPT to quickly draft different request variations, such as how to structure headers and tokens. Then I used Kong Konnect to test that.
Another use case I can think of is that we started focusing more on gateway-level validations after using Kong Konnect, such as verifying authorization failures, handling of invalid tokens, and observing how rate limiting behaves under repeated requests. This actually improved our negative and edge case coverage quite a bit. Overall, interacting with Kong Konnect made our testing approach more aligned with real production scenarios rather than just isolated API validations.
What is most valuable?
As an SDET, I found that the best features of Kong Konnect are the centralized API management, where all the services are exposed through a single gateway layer. It made it much easier for us to validate APIs consistently instead of dealing with multiple service endpoints. Another important feature was analytics. Having visibility into API traffic and failures helped us to debug issues faster, especially in CI pipelines where it is sometimes hard to trace failures.
Analytics in Kong Konnect helped us mainly in debugging and identifying patterns in failures. Using these patterns, we have generated outcomes showing where our APIs exactly failed in edge case scenarios. For example, during regression runs in CI, if a set of API tests started failing intermittently, instead of directly assuming it was a backend issue, I used the gateway-level analytics to check request counts, error rates, and response statuses. In a couple of cases, I noticed spikes in 429 errors, which indicated rate limiting was kicking in. The issue was not the service logic but the gateway policy. That saved us a lot of time in root cause analysis. Overall, analytics gave us better visibility of the failures in our current system.
More specifically, another feature I could think of is authentication, logging, and rate limiting. These things could be handled at the gateway level instead of being implemented separately in each service. This is what I liked the most. From a testing standpoint, it actually reduced duplication and made our validations more consistent across APIs. One more thing I noticed was that these configurations could be updated without major code changes, so testing becomes really easy. It helped us to quickly test different scenarios in staging without waiting for full deployments because the code changes are not very major; they are minor code changes.
What needs improvement?
Kong Konnect requires some improvement, but overall the experience is quite positive. One thing I can think of currently is around debugging visibility. While I know that the analytics are helpful, sometimes getting very detailed request-level insights during failures, especially in CI runs, required additional digging. A bit more granular and easily accessible logs would make troubleshooting faster. Also, in some cases, configuration changes were not immediately intuitive to validate from a testing standpoint. Having better tooling or previews to simulate how a policy change would impact API behavior could be useful. Nothing major, but these kinds of improvements would make it even more efficient for teams, especially our teams, which heavily rely on automation and CI pipelines.
Better integration visibility with CI/CD tools would make it easier to quickly correlate test failures and gateway behavior without switching between multiple dashboards. This should be improved.
For how long have I used the solution?
In my current field, I have been working for more than 3.5 years, and I am going to complete close to four years in my current domain of testing.
What do I think about the stability of the solution?
Kong Konnect is stable. In my company, the product that I am involved in is very API-heavy. In day-to-day testing and CI runs, we did not face frequent outages and major disruptions. Most of the time, the gateway handled routing and policies reliably, even during heavy regression cycles and heavy customer throughput. One thing I noticed is that even if there are occasional issues at the control plane level, the data plane continues to function using cached configurations, so API traffic is not impacted immediately. Overall, I would say it is a stable platform, especially for production-scale API traffic and for companies like HighLevel where the products are very much API-heavy.
What do I think about the scalability of the solution?
From what I have seen recently, Kong Konnect is quite scalable, especially in a microservices-based setup. In our case, even during heavy regression runs or higher traffic scenarios, we did not notice major performance bottlenecks. The gateway was able to handle concurrent requests quite efficiently. Also, from what I understand, it supports auto-scaling of gateway nodes, especially in cloud setups, which helps it adjust dynamically based on traffic spikes without manual intervention. Overall, it scales well both horizontally and across environments, which is important when you are dealing with multiple services and high API traffic.
How are customer service and support?
I am not personally involved in customer support. Whenever issues were raised, especially through support tickets, they were escalated and our internal system takes care of those issues. What I have seen is that the response time was generally quick, and most of our queries were resolved within a reasonable time frame. In some cases, critical issues were addressed within a few hours, which really helped during our testing cycles.
Which solution did I use previously and why did I switch?
Before Kong Konnect, our setup was more direct. We did not use any centralized API management layer. Each service handled authentication and routing on its own, which made testing a bit inconsistent because different services had slightly different implementations. In some cases, we also had basic reverse proxy setups, but they did not provide the kind of flexibility or features that Kong Konnect offers, such as plugin-based policies or detailed analytics. Moving to Kong Konnect helped standardize a lot of those aspects and made our testing approach more structured.
How was the initial setup?
I do not have experience with pricing, but I do have experience with setup and licensing. I did not have direct involvement in the pricing or licensing decision for Kong Konnect, as those were handled more at the management or DevOps level. From what I understood through internal discussions, the pricing was considered reasonable for the value it provides, especially given the centralized API management and flexibility it offers. In terms of setup, since Kong Konnect was already integrated into our AWS environment when I started, the onboarding from a tester's perspective was fairly smooth. I could start interacting with the APIs through the gateway without much back-and-forth.
What was our ROI?
From that standpoint, the core of it is that our debugging time during regression cycles reduced roughly by 25% to 30%, mainly because the gateway layer helped us quickly identify whether issues were coming from authentication policies, rate limiting, throttling, or the backend services. Because the debugging time has been reduced, the number of people that are required for doing that debugging and regression has also been improved. I sensed a 30% reduction in the effort. I have seen it myself in the employees needed to do a particular project.
Which other solutions did I evaluate?
These particular decisions are mostly taken by upper management level people, so I was not directly involved in evaluating which solution would work the best. I believe tools such as Apigee and AWS API Gateway were among the ones explored, mainly because they are quite common in the API management space. Eventually, Kong Konnect was chosen because of its flexibility, plugin-based architecture, and how well it fits in our microservice setup.
What other advice do I have?
One piece of advice I can give is that instead of using traditional methods of API management, Kong Konnect provides a very reliable solution for centralized API management. Because APIs and backend services are the backbone of any product, if these are scaled in a better way, then that is the main thing. One piece of advice I would give is to clearly understand your API ecosystem first before adopting Kong Konnect because it works really well in a microservice architecture. To get the most value, you need to have a clear idea of how your services interact and what kind of gateway policies you want to enforce. From a testing standpoint, it is important to design test cases not just for API functionality but also for gateway-level scenarios such as authentication failures, throttling, and routing behavior. That is something teams sometimes overlook initially. If these things are taken care of, then Kong Konnect will be a very useful product for companies which are heavily reliant on APIs. I have provided a rating of 9 out of 10 for this review.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)