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With over a billion viewers tuning in across the globe, the Indian Premier League is one of the busiest digital seasons of the year, as payment requests increase every second as fans stream matches.
At Cashfree Payments, that surge is felt immediately in our systems. While the cricketers are chasing runs on field, our systems are handling a chase of their own: traffic spikes, high transaction volumes, and unpredictable surges.
The kind of scale this year’s IPL brought left no space for guesswork. But thanks to our incredible tech teams who spent weeks preparing behind the scenes, making sure that we stayed match-ready from the opening ball to the very last over of the series.
We spoke with three of our engineers, Subham Kumar Goyal (Lead DevOps), Prashant Jha (SDE-4), and Abhishek Ranjan (SDE-2), who’ve been in the war IPL room when payments come in full swing.
From predicting traffic bursts to scaling Kafka seamlessly, here’s how they build the invisible backbone of real-time payments under pressure.
Strengthening Systems Before the Stress Hits
In a high-stakes season like IPL, every second matters. For our merchants, this means every transaction, every webhook, and every dashboard view has to work instantly. That kind of reliability doesn’t come from reacting fast; rather, it comes from planning way ahead and building for scale before it arrives. We don’t wait for traffic to test our systems. We prepare for what’s coming so that our systems can handle even 10x load without flinching.
This is where Subham steps in by treating the platform as three independent stress zones: Traffic, Compute, and Storage, and scaling each on its own terms. His team tackled infrastructure constraints, streamlined rate limits, and ensured that key systems like Kafka were already upscaled well before opening day. He also upgraded core infrastructure components like subnet allocations and rate limits so we could scale faster and more efficiently than before.
During IPL 2025, one Kafka cluster hit 75% CPU usage mid-match. If we hadn’t scaled it in advance, that would’ve caused cascading failures,
Shubham recalls
Meanwhile, Abhishek focused on strengthening our webhook pipeline to enable higher concurrency and smarter recovery, which is the key to maintaining speed and reliability. As traffic patterns became more complex, he engineered the system to adapt gracefully. The result: intelligent queue behavior, better isolation, and faster recovery even under stress.
With merchant-specific queue isolation, idempotency keys, and backoff strategies in place, our webhook systems are built to handle even 10x traffic spikes, without compromising delivery speed or stability,
Abhishek added.
At the database layer, Prashant optimised backend data flow by streamlining how reads and writes are managed. He redesigned traffic flows, split reads and writes, and added targeted caching to help dashboards and transaction views stay fast and responsive, no matter how heavy the load got.
Our prep starts well before the traffic hits. We gather merchant traffic projections, update our Pre-Scaling Tool to translate expected TPS into internal QPS, and validate configurations to make sure we’re ready when the load comes,
Prashant explained.
This approach, based on proactive preparation, smart isolation, and reliable performance, ensured our systems kept pace with IPL’s growing demands. And for merchants, it meant smooth, uninterrupted service, faster settlements, and no hidden surprises behind the scenes.
Handling Spikes in Real Time
When traffic rises unexpectedly, the team switches into live response mode. For Prashant, fast decision-making backed by clean alerts is non-negotiable. He contributed to building detailed runbooks with step-by-step actions for unanticipated scenarios like retry storms and latency dips.
On some days during the season, a payment retry storm pushed traffic up sharply. Thanks to our alerting system, runbooks, and pre-scaling setup, we handled it without a glitch,
Prashant explained.
On the other hand, Subham keeps his eye on golden signals like latency, error rate, and saturation, moving quickly to Kubernetes health and logs to isolate problems. His focus relies on early indicators, not lagging ones.
Abhishek checks queue depth, cache hits, and async delays. One of his key wins was visualising query rates and errors using Datadog dashboards. The graphs, captured during IPL load tests, showed that even as MySQL query rates dropped during scale-downs, error rates stayed flat. That stability confirmed that the system was reacting to traffic shifts, not breaking under pressure.
We’ve fixed retry storms by isolating queues and reducing cache misses. Tail latencies dropped even at 10x load,
Abhishek said.
Query load dropped, but error rates stayed flat, proving the system scaled down cleanly without breaking under pressure.
Conclusion
IPL 2025 was a test of how we prepare, respond, and collaborate together under intense pressure. But what helped the team hold the line was a shared philosophy: when the stakes are high, act with clarity, plan for the worst, act with clarity, and keep things simple.
Treat your system like a patient. Diagnose without bias. Precision matters more than panic
said Prashant, summarising the team’s focus on post-incident learning.
Looking back, Subham spoke about what holds up in the long run.
Empathy and collaboration matter as much as scale and automation. Chaos testing is your reality check.
Abhishek added a reminder that planning small doesn’t cut it anymore.
Prepare for 10x, not 2x. Spikes can be unpredictable. Observability and fewer dependencies are what keep systems stable.
This season of IPL may be over, but the playbook and the mindset for managing high-traffic events will continue to push us.
If scaling for impact sounds exciting, come build with us. Check out our careers page.
