.png)
Register now
June 17, 2025
June 17, 2025
June 17, 2025
June 17, 2025
We don’t need to convince you that it’s helpful to optimize your software’s performance. The result is lower costs, less latency, and probably a pat on the back from your boss.
But you’re busy. And chances are, you’ve got a lot of applications you’re responsible for. That’s why we created this expert guide to quickly make sure your software runs well, along with recommendations for how to reach the next level of optimization, using AI.
When your app slows down, users bounce. You already know performance isn't just a tech issue; it's a business risk. But, there is a slight change since not all workloads are same. Real-time transactions, batch jobs, and API calls each have a different performance threshold. What works for one may not work for another.
Amazon famously discovered that a 100ms delay in page load times caused a 1% drop in revenue. That’s not a rounding error; it’s millions of dollars. And it’s proof that latency is expensive.
If you’re running e-commerce or customer-facing apps, you’ve felt this. Slow pages mean lost carts. Hesitations mean lower conversions. The customer doesn’t wait; they just leave.
Here’s what that means for you:
Amazon’s example isn’t extreme. It’s the new normal. Even when you're scaling up for a flash sale or throttling background processes to save cost, performance has to align with real-time demand.
AI has raised the bar. Today’s users expect instant answers from LLMs, not spinning loaders. If your system can't keep up, they won't stick around.
Here’s what you’re up against:
Performance now has to be smart, not just fast. That’s where AI-driven optimization tools come in. They don’t just flag slow endpoints; they predict and prevent them. Think real-time monitoring that doesn’t just observe but acts.
You can use AI to:
Sedai takes it a step further. It’s the world’s first self-driving cloud, continuously adjusting and optimizing your resources in real-time. No more waiting for slow endpoints to catch up, Sedai acts before the problem even hits.
Bottom line: if you're not proactively tuning for both performance and cost, you're already falling behind.
Next: Let’s examine how autonomous systems are changing the game for performance optimization across the DevOps lifecycle and why manual tuning is no longer enough.
Performance isn't just about speed; it's about stability under pressure. It’s about keeping your latency SLOs green, your cloud bill under control, and your team out of excess workload mode.
Yet performance bottlenecks continue to appear in the same painful places through bloated code, inefficient database queries, and surprise traffic spikes your infrastructure wasn’t ready for.
Let’s break down the real drivers of performance and where you can take action before your next incident.
The cleaner and more efficient your code is, the faster your app runs. Sounds obvious, but teams still underestimate how much latency comes from things like:
If you’re chasing tight latency SLOs, every millisecond matters. Focus on simplicity and speed, not cleverness.
Next step: Start using profiling tools to spot the slowest lines of your code. Don't guess, measure.
Overprovisioning is expensive. Underprovisioning is a support ticket waiting to happen. CPU, memory, and network throughput need to be tuned in real time. You can’t afford static configs in a dynamic system.
Tools like AWS Auto Scaling or Kubernetes HPA let you adjust based on live metrics, not gut feeling.
Performance tip: Set up dynamic thresholds for scaling, and use AI/ML to fine-tune based on real usage patterns. That’s where autonomous optimization shines.
A slow query will kill your performance faster than a failing pod. The database is often the silent killer. If you're seeing latency spikes, check for:
Quick wins:
Your app is only as fast as your worst query.
No single server should carry the whole weight. Load balancers (like NGINX, HAProxy, or AWS ELB) spread traffic so there isn’t a single point of failure or meltdown.
Smart load distribution avoids throttling and gives you headroom when traffic surges.
What to check:
Old code slows you down. Refactoring isn’t just about readability, it’s about getting rid of code rot. Tech debt accumulates fast. And that legacy logic you’ve been ignoring? It’s dragging your latency down.
Key areas to target:
Small changes = big impact when they happen in high-traffic flows.
Don’t ask your app to do the same work twice. Caching stores the results of expensive operations. Indexing makes data easier to find. Together, they slash latency.
What to cache:
What to index:
If it takes more than 200ms, ask: “Could we cache this?”
7. Test Like It’s Already in Production
Real traffic doesn’t care about your staging environment. Before going live, simulate a heavy load, stress the system, and watch it sweat. That’s where the weak points show up, not in unit tests.
Tools like Apache JMeter, Locust, and LoadRunner let you simulate realistic spikes and long-running sessions.
Bonus tip: Try to integrate performance tests into your CI/CD so nothing ships without proving it can scale.
Users don’t care where the slowdown is; they just feel it. Fast load times start with the frontend:
But don’t forget the backend:
With Sedai, you don’t have to. It optimizes both your frontend and backend performance automatically, keeping everything running smoothly in real-time.
Manual tuning is dead. Performance optimization isn’t a one-time thing. It has to be baked into your DevOps pipeline and continuously improved.
Here’s how to embed it into your DevOps lifecycle:
The table organizes and maps the key factors to specific DevOps lifecycle stages, offering a clear roadmap for integrating performance optimization practices throughout the development and operational phases of software.
Up next: Let’s talk about common metrics to measure and positively drive your software performance for modern DevOps and SRE teams.
In reality, most of the time, you’re not tracking metrics for the sake of dashboards. You're doing it because performance issues are painful. They frustrate your team, impact users, and eat into your cloud budget faster than you can say "autoscaling."
If you're a CTO pushing for speed at scale, an SRE defending your SLOs, or a DevOps engineer trying to keep production steady, you need to know exactly what to measure and why it matters.
Here are the metrics that give you the clearest signal on software performance, and how to use them to drive actual outcomes.
This one’s non-negotiable. Response time tells you how fast your app reacts to a user action. Anything over 200ms? That’s already pushing it. Slower response = user frustration = churn.
If you care about conversion, engagement, or just keeping your systems snappy under load, track this religiously, especially during the Verify stage of the DevOps loop.
Throughput measures how many requests your app can process per second or minute. You want high throughput without sacrificing latency. If response time is the speedometer, throughput is your engine capacity.
Evaluate this during the Configure stage to make sure your infrastructure can keep up when demand surges.
If you’re not watching CPU and memory usage, you’re guessing. High resource usage often signals inefficient code, poor architectural choices, or services crying out for optimization.
Use this data across the lifecycle, especially in the Monitor stage, to stay ahead of scaling issues and avoid unnecessary cost spikes.
Errors erode trust. Failed transactions, 5xxs, unhandled exceptions, they’re all signs something's off. Track them in the Verify stage to catch issues before they impact users. And if your error rate’s spiking, don’t wait. Investigate fast and fix faster.
This is where user intent meets system failure. FCIs are when your app technically works, but the user still can’t complete their task. Broken flows, dead buttons, confusing UX, they all count.
Analyze these in the Monitor stage to identify friction and fix it before your NPS tanks.
SLOs define what “good enough” looks like. They turn performance goals into measurable targets and keep your team aligned on what really matters. Set them early in the Plan stage and monitor consistently to see whether you’re meeting expectations or missing the mark.
If your app can’t scale, it’ll fail, especially when user traffic spikes. Scalability is the ability to grow without degrading performance. Design for this upfront in the Design stage to avoid costly rearchitecture later.
These metrics aren’t just numbers; they fuel every stage of your DevOps cycle:
Want to fix performance issues, not just measure them? In the next section, learn how to spot and eliminate performance bottlenecks before your users feel the pain.
If you're chasing uptime, performance, and cost efficiency simultaneously, bottlenecks are your biggest threat.
They sneak in silently, clogging up systems, slowing down user experiences, and burning through resources. When they go unnoticed, you are left in production while explaining to leadership why things are lagging again.
The good news is that you can catch most performance issues long before they explode. You just need the right tactics built into every phase of your DevSecOps pipeline, from development to production.
Let’s break it down.
Technical roadblocks show up in two places: on the client side and the server side. Knowing where to look saves hours of guessing and failed fixes.
1. Client-Side (Browser + Frontend)
These issues kill UX and eat up engineering cycles fast. Here’s how to stay ahead:
2. Server-Side (Backend + Database)
When queries crawl or connections hang, everything else slows down. Don’t let backend bottlenecks catch you off guard:
Next: Now that you know what to look for, here’s how to catch it earlier in the pipeline.
If you're still waiting until staging or prod to diagnose performance issues, you’re already too late. The smart fixes start in the development and testing stages.
1. Simulate Chaos by trying new solutions:
2. Bake Diagnostics into the Pipeline
Coming up: Let’s talk about what changes when your code actually hits production.
Performance metrics aren’t just numbers, they are warning signs. If you’re not tracking these, you’re guessing.
Sedai also keeps an eye on these metrics for you, using AI to automatically adjust resources when something’s off. It doesn’t stop at the basics. Sedai digs into storage latency, usage patterns, and cost anomalies, then quietly rebalances workloads or shifts storage tiers when thresholds are crossed.
Every release should make your system faster and more secure. Benchmarking is how you prove that.
Now that you’ve mastered diagnostics and benchmarks, let’s discuss automation and how Sedai helps you move from fixing bottlenecks to preventing them.
Sedai’s AI doesn’t just monitor your cloud, it acts. It continuously fine-tunes resources to keep performance sharp and costs low, all without you lifting a finger.
Most performance tuning is still reactive. Sedai flips the script. Its autonomous system continuously monitors, analyzes, and optimizes your application in production, 24/7, with zero human intervention.
Bottom line: Sedai takes the guesswork and grunt work out of cloud performance management. Learn more about autonomous cloud management.
You already know pre-prod testing isn’t enough. Real users behave differently in the wild. Sedai closes that gap by shifting some performance optimization to runtime, where it really counts.
The takeaway: Sedai doesn’t just spot problems, it fixes them automatically. Read about Sedai’s finops optimization capabilities.
Every new release carries risk. That shouldn’t mean rolling the dice with performance. Sedai’s Release Intelligence watches every deploy like a hawk and takes action when things go sideways.
Pro tip: Sedai turns your releases into data-driven, low-risk events. Transition to Smart SLO Management below.
KnowBe4 needed to reduce the overhead of cloud management and meet strict performance targets on Amazon ECS. They turned to Sedai and got exactly that, without adding headcount or complexity.
Recognition: Sedai was also named in Gartner’s first-ever Platform Engineering Hype Cycle, solidifying its place as a leader in autonomous cloud optimization.
If you want to scale like KnowBe4, Sedai is your partner.
You didn’t sign up to play cloud cost whack-a-mole or chase performance issues after users feel the lag. You want systems that run fast, scale smart, and stay within budget without burning out your team.
That’s where performance optimization becomes your power play. Tuning software early and continuously helps avoid latency spikes, surprise bills, and downtime dramas. But doing this manually at scale is an extra task you don’t have time for.
Sedai changes the game with AI-driven performance tuning. It analyzes real-time app behavior, manages SLOs, rightsizes infrastructure, and automatically slashes costs. There are no manual configs, just software that gets smarter as it runs.
Join hands with Sedai and discover how effortless it can be to achieve continuous software performance optimization.
With distributed architectures (microservices, serverless) and rising user expectations, even minor latency or inefficiencies impact scalability, costs, and customer experience.
AI analyzes metrics (CPU, memory, latency) in real time to auto-scale resources, optimize queries, and predict bottlenecks, reducing manual toil.
Common issues include:
Yes, cold starts, improper memory allocation, and poorly designed triggers can degrade response times. Tools like Sedai help auto-tune configurations.
Use:
June 17, 2025
June 17, 2025
We don’t need to convince you that it’s helpful to optimize your software’s performance. The result is lower costs, less latency, and probably a pat on the back from your boss.
But you’re busy. And chances are, you’ve got a lot of applications you’re responsible for. That’s why we created this expert guide to quickly make sure your software runs well, along with recommendations for how to reach the next level of optimization, using AI.
When your app slows down, users bounce. You already know performance isn't just a tech issue; it's a business risk. But, there is a slight change since not all workloads are same. Real-time transactions, batch jobs, and API calls each have a different performance threshold. What works for one may not work for another.
Amazon famously discovered that a 100ms delay in page load times caused a 1% drop in revenue. That’s not a rounding error; it’s millions of dollars. And it’s proof that latency is expensive.
If you’re running e-commerce or customer-facing apps, you’ve felt this. Slow pages mean lost carts. Hesitations mean lower conversions. The customer doesn’t wait; they just leave.
Here’s what that means for you:
Amazon’s example isn’t extreme. It’s the new normal. Even when you're scaling up for a flash sale or throttling background processes to save cost, performance has to align with real-time demand.
AI has raised the bar. Today’s users expect instant answers from LLMs, not spinning loaders. If your system can't keep up, they won't stick around.
Here’s what you’re up against:
Performance now has to be smart, not just fast. That’s where AI-driven optimization tools come in. They don’t just flag slow endpoints; they predict and prevent them. Think real-time monitoring that doesn’t just observe but acts.
You can use AI to:
Sedai takes it a step further. It’s the world’s first self-driving cloud, continuously adjusting and optimizing your resources in real-time. No more waiting for slow endpoints to catch up, Sedai acts before the problem even hits.
Bottom line: if you're not proactively tuning for both performance and cost, you're already falling behind.
Next: Let’s examine how autonomous systems are changing the game for performance optimization across the DevOps lifecycle and why manual tuning is no longer enough.
Performance isn't just about speed; it's about stability under pressure. It’s about keeping your latency SLOs green, your cloud bill under control, and your team out of excess workload mode.
Yet performance bottlenecks continue to appear in the same painful places through bloated code, inefficient database queries, and surprise traffic spikes your infrastructure wasn’t ready for.
Let’s break down the real drivers of performance and where you can take action before your next incident.
The cleaner and more efficient your code is, the faster your app runs. Sounds obvious, but teams still underestimate how much latency comes from things like:
If you’re chasing tight latency SLOs, every millisecond matters. Focus on simplicity and speed, not cleverness.
Next step: Start using profiling tools to spot the slowest lines of your code. Don't guess, measure.
Overprovisioning is expensive. Underprovisioning is a support ticket waiting to happen. CPU, memory, and network throughput need to be tuned in real time. You can’t afford static configs in a dynamic system.
Tools like AWS Auto Scaling or Kubernetes HPA let you adjust based on live metrics, not gut feeling.
Performance tip: Set up dynamic thresholds for scaling, and use AI/ML to fine-tune based on real usage patterns. That’s where autonomous optimization shines.
A slow query will kill your performance faster than a failing pod. The database is often the silent killer. If you're seeing latency spikes, check for:
Quick wins:
Your app is only as fast as your worst query.
No single server should carry the whole weight. Load balancers (like NGINX, HAProxy, or AWS ELB) spread traffic so there isn’t a single point of failure or meltdown.
Smart load distribution avoids throttling and gives you headroom when traffic surges.
What to check:
Old code slows you down. Refactoring isn’t just about readability, it’s about getting rid of code rot. Tech debt accumulates fast. And that legacy logic you’ve been ignoring? It’s dragging your latency down.
Key areas to target:
Small changes = big impact when they happen in high-traffic flows.
Don’t ask your app to do the same work twice. Caching stores the results of expensive operations. Indexing makes data easier to find. Together, they slash latency.
What to cache:
What to index:
If it takes more than 200ms, ask: “Could we cache this?”
7. Test Like It’s Already in Production
Real traffic doesn’t care about your staging environment. Before going live, simulate a heavy load, stress the system, and watch it sweat. That’s where the weak points show up, not in unit tests.
Tools like Apache JMeter, Locust, and LoadRunner let you simulate realistic spikes and long-running sessions.
Bonus tip: Try to integrate performance tests into your CI/CD so nothing ships without proving it can scale.
Users don’t care where the slowdown is; they just feel it. Fast load times start with the frontend:
But don’t forget the backend:
With Sedai, you don’t have to. It optimizes both your frontend and backend performance automatically, keeping everything running smoothly in real-time.
Manual tuning is dead. Performance optimization isn’t a one-time thing. It has to be baked into your DevOps pipeline and continuously improved.
Here’s how to embed it into your DevOps lifecycle:
The table organizes and maps the key factors to specific DevOps lifecycle stages, offering a clear roadmap for integrating performance optimization practices throughout the development and operational phases of software.
Up next: Let’s talk about common metrics to measure and positively drive your software performance for modern DevOps and SRE teams.
In reality, most of the time, you’re not tracking metrics for the sake of dashboards. You're doing it because performance issues are painful. They frustrate your team, impact users, and eat into your cloud budget faster than you can say "autoscaling."
If you're a CTO pushing for speed at scale, an SRE defending your SLOs, or a DevOps engineer trying to keep production steady, you need to know exactly what to measure and why it matters.
Here are the metrics that give you the clearest signal on software performance, and how to use them to drive actual outcomes.
This one’s non-negotiable. Response time tells you how fast your app reacts to a user action. Anything over 200ms? That’s already pushing it. Slower response = user frustration = churn.
If you care about conversion, engagement, or just keeping your systems snappy under load, track this religiously, especially during the Verify stage of the DevOps loop.
Throughput measures how many requests your app can process per second or minute. You want high throughput without sacrificing latency. If response time is the speedometer, throughput is your engine capacity.
Evaluate this during the Configure stage to make sure your infrastructure can keep up when demand surges.
If you’re not watching CPU and memory usage, you’re guessing. High resource usage often signals inefficient code, poor architectural choices, or services crying out for optimization.
Use this data across the lifecycle, especially in the Monitor stage, to stay ahead of scaling issues and avoid unnecessary cost spikes.
Errors erode trust. Failed transactions, 5xxs, unhandled exceptions, they’re all signs something's off. Track them in the Verify stage to catch issues before they impact users. And if your error rate’s spiking, don’t wait. Investigate fast and fix faster.
This is where user intent meets system failure. FCIs are when your app technically works, but the user still can’t complete their task. Broken flows, dead buttons, confusing UX, they all count.
Analyze these in the Monitor stage to identify friction and fix it before your NPS tanks.
SLOs define what “good enough” looks like. They turn performance goals into measurable targets and keep your team aligned on what really matters. Set them early in the Plan stage and monitor consistently to see whether you’re meeting expectations or missing the mark.
If your app can’t scale, it’ll fail, especially when user traffic spikes. Scalability is the ability to grow without degrading performance. Design for this upfront in the Design stage to avoid costly rearchitecture later.
These metrics aren’t just numbers; they fuel every stage of your DevOps cycle:
Want to fix performance issues, not just measure them? In the next section, learn how to spot and eliminate performance bottlenecks before your users feel the pain.
If you're chasing uptime, performance, and cost efficiency simultaneously, bottlenecks are your biggest threat.
They sneak in silently, clogging up systems, slowing down user experiences, and burning through resources. When they go unnoticed, you are left in production while explaining to leadership why things are lagging again.
The good news is that you can catch most performance issues long before they explode. You just need the right tactics built into every phase of your DevSecOps pipeline, from development to production.
Let’s break it down.
Technical roadblocks show up in two places: on the client side and the server side. Knowing where to look saves hours of guessing and failed fixes.
1. Client-Side (Browser + Frontend)
These issues kill UX and eat up engineering cycles fast. Here’s how to stay ahead:
2. Server-Side (Backend + Database)
When queries crawl or connections hang, everything else slows down. Don’t let backend bottlenecks catch you off guard:
Next: Now that you know what to look for, here’s how to catch it earlier in the pipeline.
If you're still waiting until staging or prod to diagnose performance issues, you’re already too late. The smart fixes start in the development and testing stages.
1. Simulate Chaos by trying new solutions:
2. Bake Diagnostics into the Pipeline
Coming up: Let’s talk about what changes when your code actually hits production.
Performance metrics aren’t just numbers, they are warning signs. If you’re not tracking these, you’re guessing.
Sedai also keeps an eye on these metrics for you, using AI to automatically adjust resources when something’s off. It doesn’t stop at the basics. Sedai digs into storage latency, usage patterns, and cost anomalies, then quietly rebalances workloads or shifts storage tiers when thresholds are crossed.
Every release should make your system faster and more secure. Benchmarking is how you prove that.
Now that you’ve mastered diagnostics and benchmarks, let’s discuss automation and how Sedai helps you move from fixing bottlenecks to preventing them.
Sedai’s AI doesn’t just monitor your cloud, it acts. It continuously fine-tunes resources to keep performance sharp and costs low, all without you lifting a finger.
Most performance tuning is still reactive. Sedai flips the script. Its autonomous system continuously monitors, analyzes, and optimizes your application in production, 24/7, with zero human intervention.
Bottom line: Sedai takes the guesswork and grunt work out of cloud performance management. Learn more about autonomous cloud management.
You already know pre-prod testing isn’t enough. Real users behave differently in the wild. Sedai closes that gap by shifting some performance optimization to runtime, where it really counts.
The takeaway: Sedai doesn’t just spot problems, it fixes them automatically. Read about Sedai’s finops optimization capabilities.
Every new release carries risk. That shouldn’t mean rolling the dice with performance. Sedai’s Release Intelligence watches every deploy like a hawk and takes action when things go sideways.
Pro tip: Sedai turns your releases into data-driven, low-risk events. Transition to Smart SLO Management below.
KnowBe4 needed to reduce the overhead of cloud management and meet strict performance targets on Amazon ECS. They turned to Sedai and got exactly that, without adding headcount or complexity.
Recognition: Sedai was also named in Gartner’s first-ever Platform Engineering Hype Cycle, solidifying its place as a leader in autonomous cloud optimization.
If you want to scale like KnowBe4, Sedai is your partner.
You didn’t sign up to play cloud cost whack-a-mole or chase performance issues after users feel the lag. You want systems that run fast, scale smart, and stay within budget without burning out your team.
That’s where performance optimization becomes your power play. Tuning software early and continuously helps avoid latency spikes, surprise bills, and downtime dramas. But doing this manually at scale is an extra task you don’t have time for.
Sedai changes the game with AI-driven performance tuning. It analyzes real-time app behavior, manages SLOs, rightsizes infrastructure, and automatically slashes costs. There are no manual configs, just software that gets smarter as it runs.
Join hands with Sedai and discover how effortless it can be to achieve continuous software performance optimization.
With distributed architectures (microservices, serverless) and rising user expectations, even minor latency or inefficiencies impact scalability, costs, and customer experience.
AI analyzes metrics (CPU, memory, latency) in real time to auto-scale resources, optimize queries, and predict bottlenecks, reducing manual toil.
Common issues include:
Yes, cold starts, improper memory allocation, and poorly designed triggers can degrade response times. Tools like Sedai help auto-tune configurations.
Use:
.svg)
.svg)
%201.svg)
%202.svg)
