When AI Meets Tuning: Why Manual Performance Optimisation Is a Dead End

Every system eventually reaches a point where performance becomes an issue.

It starts subtly. Maybe an API request slows under load. Perhaps there’s an unexplained spike in CPU usage or a queue clogs now and then, only to cascade into widespread timeouts across your stack at the worst possible moment.

You investigate, tweak a few JVM settings, fine-tune the database pool, restart the pods. Crisis averted. For now!

But a short time later, the issues resurface.

Why? Because real-world systems are anything but static. What works today might collapse next month. Your traffic might double, a new service might shift the balance, or changing load patterns might expose fresh bottlenecks.

Manual performance tuning just can’t keep up.

The Challenge

It’s time to face the facts. Your stack may be refined, and your methods sophisticated, but manual tuning simply doesn’t scale in the long run.

We’ve encountered it first-hand. What seemed like a mature and robust backend processing system was put to the test. Built on Java, containerised with Kubernetes, and powered by a MySQL database, the system handled steadily and predictably, or so we thought.

But under stress? Latency spiked, CPU usage behaved irrationally, and our test environments served up inconsistent results. Each issue was time-consuming, frustrating, and worst of all, reactive.

The signs were clear:

• We’d reached the limit where intuition and trial-and-error worked.
• Throwing more hardware at the problem wasn’t solving it.
• Weekly tuning rituals took too much time and delivered diminishing returns.
• And worst of all, we were still guessing.

This is a universal pain. Whether you’re a lead engineer in Riyadh, a platform architect in Berlin, or a CTO in San Francisco, the pattern is the same. You can automate deployments and can have world-class observability tools in place but if tuning relies on humans, it’s fragile and unsustainable.

We knew there had to be a better way.

Enter AI-Driven Optimisation

We weren’t chasing AI for the hype. We needed a practical way to break the cycle of endless guesswork.

Performance tuning is an exploration problem, not a checklist. To do it well, you need a process that’s measurable, repeatable, and fast. That’s when we found Akamas.

Akamas isn’t about flashy dashboards or abstract insights. It’s about actionable, data-driven experiments. You define your goals, hook it up to your metrics, give it the space to explore, and it does the tuning.

Here’s how it works:

• Define “success.” – Do you want lower latency, higher throughput, or both? Specify your objectives.
• Set the stage. Give Akamas control over the configuration space (like JVM heap sizes, Kubernetes limits, or DB pool settings).
• Connect observability tools. We paired Akamas with Prometheus and AppDynamics for precise metrics.
• Run experiments. Akamas tweaks configurations, runs automated tests, learns from the results, and repeats the cycle.

This system puts the science behind tuning. It uses reinforcement learning and Bayesian optimisation to explore performance bottlenecks in ways humans simply can’t, at least not on this scale.

What We Discovered

You’ve heard the horror stories about tuning projects that implode due to complexity or abstraction. Akamas avoids this trap with a process that’s integrated, controlled, and focused on results.

We decided to challenge Akamas:

Can we reduce latency and increase throughput for a service running in Kubernetes without altering the code or adding more resources?

Akamas didn’t disappoint. Within the first 10 experiments, it uncovered issues we’d overlooked for months:

• A “safe” connection pool size we’d never questioned turned out to be a bottleneck.
• Subtle interactions between Kubernetes resource limits and JVM settings were crushing performance.
• Configuration tweaks we assumed were risky actually boosted throughput by double digits.

This wasn’t just an improvement. These were revelations. Akamas offered insights we didn’t know we needed and solved problems faster than any manual approach could.

Why AI Makes Tuning Sustainable

The value of AI isn’t just in the tweaking. It’s in creating a process your team can rely on without burning out.

Manual tuning takes time, mental energy, and focus. Even the sharpest engineers are still human. They’re prone to bias, limited by time, and bogged down by endless permutations of JVM flags, Kubernetes settings, kernel tweaks, and database configurations.

Akamas changes the game. It doesn’t replace engineers; it frees them.

• No more guesswork. AI digs into the problem space systematically.
• No jumping between dashboards. Experiments are automated and integrated.
• No babysitting. Define the rules, feed it data, and watch it work.

Instead of spending nights and weekends on tuning loops, your team can focus on what actually drives your business forward.

Why This Matters

If you’re tired of debugging the same performance issues, justifying your cloud bill, or putting out fires that never really go away, this should matter to you.

Today’s stacks are sprawling, dynamic, and full of hidden choke points. The tuning landscape is too vast and complex for humans to manage alone. But AI? This is where it thrives.

This isn’t about replacing expertise. It’s about leveraging it smarter.

It’s time to stop trying to outguess entropy. Instead, automate the pain points and focus on innovation.

Your system’s performance isn’t just about speed. It’s about freeing your engineers and preparing for growth. And the only way to do that sustainably is to bring AI into the tuning loop. 

What’s Next? Implementation and Insights

The revelations aren’t over yet. This post is just part one.

Coming up in part two, we’ll detail the practical side of implementing Akamas, including:

• How we built a configuration plan for optimisation
• A real-world walkthrough of optimising a Kubernetes application
• Structuring test automation and metrics collection
• The performance improvements we achieved, complete with before-and-after data
• Lessons learned, surprises, and the adjustments we’d make next time

We’ll also include helpful visuals like screenshots, configuration examples, performance graphs, and more valuable insights to show you how it all works.

Stay tuned!