Komodor is an autonomous AI SRE platform for Kubernetes. Powered by Klaudia, it’s an agentic AI solution for visualizing, troubleshooting and optimizing cloud-native infrastructure, allowing enterprises to operate Kubernetes at scale.
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Here’s what they’re saying about Komodor in the news.
Your platform team is drowning in TicketOps while your K8s clusters are burning money on idle resources, and the on-call rotation looks like a death march schedule.
You’ve got 15 people who actually understand the infrastructure, 300 engineers who keep breaking it in creative ways, and a backlog of quick questions that would take six months to clear.
This is where AI SRE starts being the difference between scaling your infrastructure and scaling your headcount at the same rate.
AI SRE is a system that understands your infrastructure topology, learns the relationships between your services, and takes autonomous action when things break or drift from optimal states.
The key difference between traditional SRE tooling and an AI SRE platform is decision-making speed.
When a pod starts crash-looping at 2 AM, your current setup probably sends an alert to PagerDuty, wakes someone up, and that person spends 20 minutes digging through logs before they even understand which service is affected.
An AI SRE agent sees the crash loop, correlates it with a deployment that happened 15 minutes earlier, identifies the config change that broke things, and either rolls back automatically or surfaces a one-click remediation, all before your on-call engineer finishes making coffee.
Enterprise organizations using AI-driven observability are already achieving 40% reductions in Mean Time to Repair (MTTR).
This is not about replacing your SRE team.
It’s giving them back the time they waste on repetitive investigation work, so they can focus on the architectural problems that actually require human judgment.
The AI SRE tools handle the pattern matching, the log correlation, the resource right-sizing, and the “did you try turning it off and on again” troubleshooting that consumes 60% of most SRE teams’ time.
Analysts spend, on average, 2.7 hours per day resolving incidents, costing $3.3B in the US alone.
Source: BDM Brochure by Microsoft Security
Your senior people stop being glorified log archaeologists and start being the infrastructure architects you hired them to be.
The foundation of any useful AI SRE platform is system understanding, which means the agent needs to know what your infrastructure actually looks like.
Not just what’s deployed right now, but how services depend on each other, which teams own which components, and what the expected behavior patterns are for each service.
An AI SRE agent builds a live topology map that shows you the actual relationships between your pods, services, ingresses, persistent volumes, and everything else running in your clusters.
When something breaks, the agent doesn’t just tell you “pod X is unhealthy,” it tells you “pod X is unhealthy, which is affecting service Y, which is owned by team Z, and here are the last five changes that touched any component in this dependency chain.”
This is where the 100x AI SRE claim stops sounding like marketing fluff and starts being a real multiplier.
One person with proper system understanding can troubleshoot issues that would normally require three people comparing notes across different monitoring tools.
The AI SRE platform eliminates the “let me check Grafana, then Datadog, then ArgoCD, then Slack to piece together what happened” workflow that eats hours of SRE time every single week.
AI SRE agents learn what normal looks like for your infrastructure by observing deployment patterns, resource utilization trends, and failure modes over time.
When a new issue appears, the agent compares it against historical incidents and surfaces similar patterns from your past troubleshooting sessions.
This is particularly valuable for the long-tail issues that only happen once every few months.
Your team forgets the fix between occurrences, so you end up re-investigating the same problem from scratch.
An AI SRE system remembers, and it remembers the exact remediation steps that worked last time.
Let’s talk about what happens during an actual incident, because this is where AI SRE engineer capabilities get tested against reality.
Your application starts timing out.
Users are complaining.
The on-call person gets paged.
In a traditional setup, that engineer now starts the investigation process: checking recent deployments, looking at resource utilization, examining logs, tracing the request path through your service mesh.
This typically takes 15 to 45 minutes before they even identify the root cause, depending on how complex your infrastructure is.
An AI SRE agent starts investigating the moment the first anomaly appears, often before it escalates to user-impacting failures.
The agent checks recent changes across all affected services, correlates error patterns with known issues, and identifies the blast radius of the problem.
By the time your engineer sees the alert, the AI SRE platform has already narrowed the problem down to two or three likely causes and surfaced the relevant context.
Your engineer confirms the diagnosis and approves the remediation, or they override it if the AI got it wrong.
Either way, your mean time to resolution drops from 30 minutes to under 10 minutes, because the investigation phase is mostly automated.
The word “autonomous” makes most SREs nervous, and for good reason.
You’ve probably seen automation that was supposed to help but ended up making things worse because it didn’t understand the full context of what it was changing.
AI SRE platforms like Komodor handle this by working within defined guardrails and learning from your team’s remediation patterns.
For low-risk actions like restarting a crashed pod or scaling up a resource-constrained deployment, the agent can act automatically.
For higher-risk changes like database rollbacks or traffic shifting, the agent surfaces the recommended action and waits for human approval.
Over time, as the system proves its reliability, you can expand the set of actions it’s allowed to take autonomously.
The goal is progressive automation that reduces toil without introducing new risks.
Your team still has the controls, but they’re spending their time on decisions that matter instead of manually executing the same troubleshooting checklist for the 50th time this month.
Most AI SRE tools are either too narrow (they only handle one specific problem) or too broad (they promise to solve everything but don’t integrate with your existing stack).
The platforms that actually deliver value in production environments share a few common characteristics.
First, they integrate with your existing observability tools instead of trying to replace them.
If you’re already running Prometheus, Grafana, and Datadog, an AI SRE platform should pull data from those sources and add intelligence on top, not force you to rip out your monitoring stack and start over.
Second, they understand Kubernetes natively.
This means they work with your actual K8s primitives (pods, deployments, services, ingresses) and they understand the relationships between them.
Generic APM tools can show you application metrics, but they don’t understand that your service mesh configuration is what’s actually causing your latency spike.
Third, they give you clear ownership mapping.
When the AI SRE agent identifies a problem, it should be able to tell you which team owns the affected component, what the escalation path is, and who made the last change that might have contributed to the issue.
This is critical for organizations with multiple teams working on the same infrastructure.
Without an ownership context, you end up with “someone should probably look at this” alerts that everyone ignores because nobody knows if it’s their problem.
An AI SRE platform becomes significantly more valuable when it’s integrated with your CI/CD pipeline.
This allows the agent to correlate issues with specific deployments, rollbacks, or configuration changes.
If your team uses ArgoCD or Flux for GitOps deployments, the AI SRE tools should be able to see what changed in your Git repository, what got deployed to which cluster, and what the impact was on your running services.
When something breaks, the agent can immediately point to the deployment that caused it and suggest a rollback.
This is just having all the relevant context in one place instead of forcing your engineers to manually connect dots across five different tools.
The time savings compound quickly when you’re running dozens of deployments per day across multiple clusters.
The idea is not that one AI SRE agent literally replaces 100 humans.
AI SRE tools amplify the capabilities of your existing team by handling the repetitive, pattern-matching work that doesn’t require creative problem-solving.
Your platform team currently spends significant time on tickets like “why is my pod stuck in pending state” or “can you check if we’re being throttled by AWS” or “what changed in the last hour that might have broken this.”
These are legitimate questions with legitimate answers, but answering them manually is toil.
An AI SRE platform can answer most of these questions automatically by checking resource quotas, examining recent changes, and correlating symptoms with known issues.
The questions that do require human expertise can now get proper attention because your team isn’t buried under a pile of routine troubleshooting requests.
This is where the 100x multiplier comes from.
Not from replacing engineers, but from giving them their time back and letting them work on problems that actually benefit from their years of experience.
AI SRE platforms also tackle the other major pain point in cloud-native operations, which is Kubernetes cost optimization.
Your clusters are probably running at 30 to 40 percent utilization because everyone is over-provisioning resources out of fear that under-provisioning will cause outages.
This is rational behavior when you don’t have clear visibility into actual resource needs. It’s also how you end up with a cloud bill that makes your CFO ask uncomfortable questions about infrastructure efficiency.
AI SRE tools can analyze actual resource usage patterns over time and recommend right-sizing for your workloads.
The agent knows which pods are consistently over-provisioned, which ones are hitting resource limits, and which ones have usage patterns that would benefit from autoscaling configurations.
More importantly, the agent can make these recommendations without requiring your team to manually review resource metrics for hundreds of services.
This is not about cutting costs at the expense of reliability.
This is about eliminating waste while maintaining the same performance and availability guarantees your users expect.
The typical outcome is a 20 to 40 percent reduction in compute costs with no impact on service quality. This happens simply by aligning resource allocations with actual needs instead of guesses.
AI SRE platforms are not a universal solution for every organization at every stage.
If you’re running three services on a single cluster with five engineers who all understand the entire stack, you probably don’t need an AI SRE agent.
You need better communication and maybe some documentation.
AI SRE tools start making sense when you cross certain complexity thresholds.
If you have multiple teams deploying to shared infrastructure, if you have more services than any one person can keep in their head, if you have enough alert noise that people are starting to ignore pages, then you’re in AI SRE territory.
The organizations that get the most value from AI SRE platforms are typically running 50 or more services across multiple clusters, with engineering teams that are growing faster than their platform team can scale.
These are the environments where the manual troubleshooting approach breaks down. There’s simply too much happening for humans to track without assistance.
Another factor is your rate of change.
If you’re deploying updates multiple times per day, the probability of deployments causing issues goes up. The value of automated root cause analysis increases accordingly.
Conversely, if you deploy once a month and your infrastructure is relatively static, you might be better off investing in better testing and staging environments than in AI SRE automation.
AI SRE tools are particularly valuable for organizations in the middle of a Kubernetes migration.
If you’re moving from EC2, or VMware, or some legacy orchestration system to K8s, you’re going to have a period where nobody fully understands the new infrastructure yet and things break in unfamiliar ways.
This is when having an AI SRE agent that understands K8s primitives and can surface relevant context quickly becomes a competitive advantage.
Your team is learning the new platform while simultaneously trying to keep production stable.
An AI SRE platform acts as a knowledge multiplier during this transition, helping your engineers ramp up faster by showing them the patterns and relationships in your K8s environment.
The alternative is a painful six-month learning period where every incident takes twice as long to resolve because people are still figuring out how everything connects.
The only metrics that matter for evaluating an AI SRE platform are the ones that directly measure outcomes.
Mean time to resolution is the obvious one.
If you’re currently averaging 30 minutes from alert to fix, and you drop that to 15 minutes after implementing AI SRE tools, that’s a measurable win.
Track this before and after deployment. Also track it consistently over several months to account for seasonal variations in incident frequency.
TicketOps volume is another concrete metric.
Count how many “help me troubleshoot this” tickets your platform team receives per week. Then measure whether that number decreases after implementing an AI SRE agent.
The goal is not to eliminate all tickets, but to eliminate the repetitive ones that don’t require human judgment.
If your team was handling 50 tickets per week and that drops to 30 after AI SRE implementation, that’s 20 tickets worth of time returned to your engineers for more valuable work.
Cost optimization is the third measurable outcome.
Track your total compute spend before and after right-sizing recommendations from the AI SRE platform.
If you’re running on AWS or GCP, you should see this reflected in your monthly bill within a few weeks of implementing resource optimization suggestions.
The typical range is 20 to 40 percent reduction. This varies significantly based on how over-provisioned your workloads were before optimization.
Toil is harder to measure than MTTR because it’s more subjective, but it’s worth tracking anyway.
Ask your team to estimate what percentage of their time they spend on repetitive troubleshooting, routine ticket responses, and manual investigation work versus architectural improvements and strategic projects.
Track this before AI SRE implementation and six months after.
The target is to shift at least 20% of time from toil to strategic work.
Another proxy for toil reduction is on-call satisfaction.
If your on-call rotation is less painful because the AI SRE agent handles the straightforward incidents and only escalates the complex ones, your team will notice.
Track on-call feedback and incident hand-off frequency as indicators of whether the AI SRE platform is actually reducing cognitive load.
One of the biggest concerns organizations have about adopting AI SRE tools is disruption to existing workflows.
Your team already has muscle memory around certain tools and processes. Introducing a new platform that requires wholesale changes to how people work is a tough sell.
The AI SRE platforms that succeed in enterprise environments are the ones that integrate with your existing stack instead of trying to replace it.
If you’re running Prometheus and Grafana for metrics, the AI SRE agent should pull data from those sources and add intelligence on top.
If you’re using ArgoCD or Flux for deployments, the AI SRE platform should integrate with your GitOps workflow and correlate changes with incidents.
Also, if you’re using Datadog or New Relic for APM, the AI SRE tools should be able to ingest that telemetry data and use it for root cause analysis.
The integration work is not zero, but it should be measured in days, not months.
Your platform team should be able to connect the AI SRE platform to your observability stack without rewriting your monitoring configurations or changing how your engineers interact with existing tools.
The goal is augmentation, not replacement.
Most large organizations are not running everything in a single cluster on a single cloud provider.
You have development clusters, staging clusters, and production clusters across multiple regions. Maybe even multiple cloud providers if you’re hedging against vendor lock-in.
AI SRE platforms need to handle this complexity natively.
The agent should be able to see across all your clusters. It should understand how they relate to each other and track changes that affect multiple environments.
When a configuration change in your staging cluster reveals a problem, the AI SRE platform should be able to flag that before the same change gets promoted to production.
This kind of multi-environment awareness is particularly valuable for preventing incidents rather than just responding to them faster.
If your platform team is drowning in TicketOps, your MTTR is measured in hours instead of minutes, and your cloud bill keeps growing faster than your revenue, you’re looking at a scaling problem that headcount alone won’t solve.
AI SRE platforms give you a path to operational efficiency that doesn’t require hiring 20 more engineers or accepting that your infrastructure will always be a source of stress.
At Komodor, we’ve built an AI SRE platform that understands your Kubernetes environment. It reduces mean time to resolution by automating the investigation phase and optimizing resource allocation without sacrificing reliability.
Our platform integrates with your existing observability stack, working across multiple clusters and cloud providers. It scales with your infrastructure without requiring you to scale your platform team at the same rate.
Are you ready to reduce operational toil and get your team focused on architecture instead of repetitive troubleshooting? Let’s discuss how autonomous AI SRE can transform your cloud-native operations.
Komodor is an Autonomous AI SRE Platform for cloud-native infrastructure. Powered by Klaudia™ Agentic AI, Komodor helps teams visualize, troubleshoot, and optimize Kubernetes environments at scale.
Traditional monitoring tools collect metrics and logs and let you build dashboards and alerts.
AI SRE platforms add decision-making and remediation capabilities on top of that data.
The monitoring tools tell you something is wrong, while AI SRE tools tell you why it’s wrong and what to do about it.
The key difference is the automation of the investigation phase.
Your monitoring setup can detect that a pod is crash-looping, but it can’t automatically trace that back to a specific configuration change or suggest a rollback without human intervention.
AI SRE agents handle that correlation and recommendation step automatically.
AI SRE agents learn your infrastructure patterns by observing deployments, incidents, and remediation actions.
The more incidents the platform sees, the better it becomes at recognizing similar patterns and suggesting appropriate responses.
This is not generic machine learning that needs millions of data points.
This is pattern recognition across your specific infrastructure, which means the agent can start providing useful recommendations within weeks of deployment as it builds up context about your environment.
This depends on how you configure the platform and what level of automation you’re comfortable with.
Most organizations start with the AI SRE agent surfacing recommendations that require human approval for any changes.
As the team builds confidence in the agent’s judgment, they can expand the set of actions that can happen automatically.
Low-risk actions like restarting crashed pods or scaling up resource-constrained deployments are typically good candidates for full automation.
Higher-risk actions like database rollbacks or traffic shifting usually remain human-approved even in mature implementations.
AI SRE platforms should be designed with high availability and graceful degradation.
If the agent becomes unavailable, your existing monitoring and alerting systems continue to function normally.
You lose the automated investigation and remediation capabilities, but you don’t lose visibility into your infrastructure.
The AI SRE platform adds value on top of your monitoring stack, but it shouldn’t become a single point of failure for your operational capabilities.
AI SRE platforms work by understanding Kubernetes primitives and observability data, which means they can handle any application running on K8s regardless of whether it’s a standard open source component or a custom internal service.
The agent learns your specific deployment patterns and service dependencies by observing your infrastructure.
It doesn’t need pre-trained knowledge about your specific applications to provide value.
As long as your services are emitting metrics and logs that your observability stack can collect, the AI SRE platform can incorporate that data into its analysis.
Your existing platform and SRE teams already have the skills they need.
If your engineers understand Kubernetes, know how to read metrics and logs, and can troubleshoot production issues, they can work with an AI SRE platform.
The learning curve is primarily about understanding what the agent can and cannot do, and learning to trust its recommendations when they prove accurate.
This is less about acquiring new technical skills and more about adjusting workflows to incorporate AI-assisted troubleshooting into your incident response process.
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