Sold by: Komodor
Deployed on AWS
Autonomous AI SRE
4.4
Overview
Komodor is an Autonomous AI SRE Platform for investigating, troubleshooting, and optimizing cloud-native applications. By automating detection, root cause analysis and remediation, Komodor dramatically reduces MTTR, empowers developers, and ensures optimal cost and performance.
Highlights
- Automated Root Cause Analysis
- Remediation
- Cost Optimization
Details
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Delivery method
Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Additional vCPUs | Price per additional vCPU | $132.00 |
Komodor Enterprise - 1,000 vCPUs included | Enterprise platform pricing - including the first 1,000 vCPUs | $150,000.00 |
Dimensions summary
This contract is priced around your compute footprint, measured in vCPUs on your hosts. You start with the Komodor Enterprise dimension, which includes the first 1,000 vCPUs in the base platform price. If your environment grows past that count, you add capacity through the Additional vCPUs dimension, charged per extra vCPU. The two dimensions work together: one sets your platform baseline, the other scales usage as your clusters expand. Komodor calculates pricing based on the average number of nodes in your clusters over the year, so your cost tracks your actual capacity.
Top-of-mind questions for buyers
A vCPU is a virtual processor unit on the hosts, or nodes, running your Kubernetes clusters. Komodor counts the vCPUs across the hosts it monitors. Pricing is calculated on the average number of nodes in your clusters over the year, so your count reflects real capacity.
The Enterprise dimension covers the first 1,000 vCPUs in the base platform price. Once you pass that count, each extra vCPU is charged through the Additional vCPUs dimension. Only the vCPUs above the included 1,000 draw the per-vCPU charge; the base does not change.
Komodor calculates pricing from the average number of nodes across your clusters over the year, not the peak. Short spikes in capacity do not immediately drive up your count. Your cost tracks sustained average usage rather than momentary highs or lows.
komodor.com
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
docs.komodor.com support@komodor.com
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Komodor
By Observe, Inc.
Top
100
In Monitoring
Top
10
In Issue & Bug Tracking, Monitoring and Observability
Top
10
In Application Performance and UX Monitoring
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
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Negative reviews
AI generated from product descriptions
Automated Root Cause Analysis
Automated detection and identification of root causes in cloud-native applications to reduce mean time to resolution
Automated Remediation
Autonomous remediation capabilities that automatically address identified issues without manual intervention
Cost Optimization
Automated analysis and optimization of cloud resource utilization and spending for cloud-native environments
Cloud-Native Application Monitoring
Monitoring and investigation capabilities specifically designed for cloud-native application architectures
AI-Driven Troubleshooting
Artificial intelligence-powered troubleshooting system for investigating and diagnosing application issues
Distributed Tracing
Autonomous deployment of tracing across microservices and serverless environments with automatic capture and contextualization of transaction data from beginning to end.
Log Management and Correlation
Automatic correlation of log data with distributed tracing to enable seamless search and analysis of logs across the system.
Real-time Alerting
Preconfigured smart alerting system that provides real-time notifications for critical issues in applications with direct links to troubleshooting information.
System Architecture Visualization
Auto-generated, continuously updated visual system map based on real-time execution data showing transactions, functions, and managed services in a single view.
Cost Analysis and Optimization
Granular cost breakdown analysis of every application component to identify inefficiencies and optimize resource allocation in serverless environments.
Data Ingestion and Query Performance
Ingests petabytes of telemetry per day with capability to process hundreds of terabytes and execute tens of millions of queries daily without performance degradation
Knowledge Graph Architecture
Utilizes O11y Knowledge Graph to structure and correlate data across logs, metrics, and traces for fast search and correlation capabilities
Natural Language Processing for Incident Analysis
Implements O11y AI to enable troubleshooting of complex incidents using natural language queries for accelerated root cause analysis
Open Data Lake Foundation
Built on Snowflake data lake architecture providing open data storage without vendor lock-in and enabling cost-efficient telemetry retention
Multi-Signal Correlation
Correlates and contextualizes data across logs, metrics, and traces to provide unified observability across multiple teams and use cases
Standard contract
No
Customer reviews
Hospital & Health Care
Komodor Makes Kubernetes Troubleshooting Fast, with Great AI and Onboarding
Reviewed on Nov 06, 2026
Review provided by G2
What do you like best about the product?
Komodor makes troubleshooting much easier for devops, SRE and developer team. The AI tools are really good at helping find root cause of issues, saving a ton of time. The UI is great and very easy to navigate, especially for teams that don't have deep Kubernetes experience and prefer not to use kubectl and command line. The Komodor onboarding experience is top notch. They have helped us throughout the whole process and been super responsive to any questions we've had.
What do you dislike about the product?
I would prefer to have a self-hosted version, but at this point the SaaS version is all that is available.
What problems is the product solving and how is that benefiting you?
Our developers do not have much experience with Kubernetes, so having a way to easily see resources, logs, live manifests has been super helpful to cut down their troubleshooting times as they develop applications. Komodor also helps our devops team with troubleshooting of issues that get escalated.
Luis B.
Faster Triage and Lower MTTR Despite Complex Setup
Reviewed on Nov 06, 2026
Review provided by G2
What do you like best about the product?
I like the correlated timeline of events and deployments and the clarity in diagnosis and root cause analysis. Komodor unifies everything, and before any basic triage required a lot of senior Kubernetes knowledge. With it, I have more speed in triage and autonomy for engineering, relieve the load on the SRE and platform team, reduce operational fatigue, and drastically decrease MTTR. The connection to the cluster was also quick.
What do you dislike about the product?
The installation of the agent via Helm Chart and the connection to the cluster were quick, but I find the configuration of permissions and advanced rules complex. Configuring granular RBAC, custom automation policies, and alert rules for multitenant environments, as well as adjusting permissions and noise, took time. The integrations with the ecosystem had medium difficulty.
What problems is the product solving and how is that benefiting you?
I use Komodor to automate and unify visibility, governance, and security. The timeline correlates events and deployments and clarifies the root cause, speeding up triage, giving autonomy to engineering, relieving SRE, and reducing MTTR and operational fatigue; before, basic triages required senior Kubernetes knowledge.
hemant s.
Easy UI, Seamless Integrations, and Top-Notch Performance
Reviewed on Nov 06, 2026
Review provided by G2
What do you like best about the product?
Easy UI and multiple tool Integration, Performance is always top notch, Satisfy with pricing model, support is very good, we have a monthly sync ups, AI helps in resolving critical issues
What do you dislike about the product?
I can’t add a proper alert summary to the alerts generated from Komodor.
What problems is the product solving and how is that benefiting you?
Technical Troubleshooting
Education Management
Steep Setup and Manual Tuning Needed as Komodor Scales Across Clusters
Reviewed on Nov 05, 2026
Review provided by G2
What do you like best about the product?
Komodor does an excellent job of simplifying Kubernetes troubleshooting by providing a clear, real-time timeline of everything happening across our clusters. The platform excels at tracking configuration changes, deployments, and dependencies, which drastically reduces our Mean Time to Resolution (MTTR). The addition of their AI SRE capabilities makes it much easier to pinpoint the actual root cause of an issue rather than just alerting us that something is down.
What do you dislike about the product?
While the UI is generally intuitive, the initial setup and learning curve can take some time, especially for team members who aren't deeply experienced with Kubernetes. Additionally, as we scale our multi-cloud environment and add more clusters, keeping track of costs and fine-tuning the alert noise requires continuous manual adjustments.
What problems is the product solving and how is that benefiting you?
Komodor solves the problem of "alert fatigue" and blind spots within our Kubernetes architecture. Instead of digging through endless logs across multiple tools, it gives our DevOps and development teams a unified pane of glass. This has drastically cut down the time we spend on root-cause analysis and has made our on-call rotations much less stressful.
Jhonas Alejandro M.
Clear visibility and faster fault analysis, despite complex configuration
Reviewed on Nov 05, 2026
Review provided by G2
What do you like best about the product?
I like the visual simplicity of Komodor for understanding the root cause of a failure. The timeline of deployments and changes in the cluster is super clear; it saves me a lot of time when analyzing failures and prevents me from having to manually review logs step by step. It also helps me see the status of the pods in real-time, quickly identify root causes, and speed up incident resolution.
What do you dislike about the product?
I find that the initial learning curve for integrating some clusters can be somewhat steep because they are complex and customized. When there is a huge volume of simultaneous events, alerts can become a bit noisy. I would like to have more granular filter management to prioritize notifications.
What problems is the product solving and how is that benefiting you?
I use Komodor to manage, monitor, and resolve issues in Kubernetes environments. It helps me see the status of the pods in real-time, quickly identify the root cause of failures during testing, and speed up incident resolution in the infrastructure.