Cortex.io Reviews

I have experiences in Cortex.io to centralize overall security and engineering insights and to track the overall health and risk metrics. I use it for monitoring scoreboards, metrics to measure systems' reliability, and the security posture overall. I also track the vulnerabilities and do remediation progress, provide visibility into the team performances and compliance data, and use dashboards to prioritize security and improve overall operations with my team. I drive data-based decisions to reduce risk and improve engineering efficiency.

I use Cortex.io daily to track my security and the operational health metrics for services. For example, I use it to monitor service scoreboards that measure things such as vulnerability backlogs, deployment risk, incident trends, and compliance metrics. When I saw a service with a growing vulnerability backlog, I worked with the engineering team to prioritize remediation tasks and track the progress in Cortex.io. I updated dashboards and metrics so that leadership could see improvements over time, which helped teams make data-driven decisions. It helped them reduce risk by ensuring vulnerabilities were addressed in a timely manner, and everything was fixed promptly.

Another example of how I used Cortex.io is that I used it to monitor security and compliance health across our services. I review service scoreboards and metrics that track open security findings, configuration compliance, incident response performance, and deployment risk indicators. When a service showed non-compliant configurations or any open findings, I notified the engineering teams, helped prioritize remediation, and tracked progress in Cortex.io dashboards. I provided updates so leadership could measure improvements. Overall, this ensured security issues were addressed proactively and in a timely manner, giving teams clear visibility into the risk. I check dashboards and metrics regularly as part of my daily workflow and use the data to guide security and operational improvements.

In my opinion, one of the best features of Cortex.io is the unified service catalog because Cortex.io automatically builds a centralized catalog for all your services and teams, detailing the ownerships, so you always know what exists and who owns that feature. I also love the scorecards and readiness checks; Cortex.io provides custom scoreboards that measure things such as security controls and best compliance practices, allowing teams to track health and improvements over time. I also love the progress tracking and how you can initiate and tie the scorecard goals so that work isn't just visible but actionable, allowing you to complete owners, deadlines, and also track progress that way. I appreciate the visibility and overall better reliability, as Cortex.io helps speed up onboarding, improves reliability, and frees engineers to focus on high-impact work throughout the team.

The scorecards control the readiness, security controls, and best practices, and they have helped my team make better tracking of health improvements and save a lot of time.

Cortex.io has positively impacted team collaboration because it provides clear visibility into service health, security posture, and operational metrics. Before Cortex.io, insights were scattered across tools and spreadsheets, making it harder to prioritize work and measure progress. With Cortex.io, my teams could see scoreboards and metrics all in one place, which allowed us to identify vulnerabilities and risks faster. We track remediation progress over time, make data-driven decisions more quickly, and improve collaboration through shared visibility. This enhances my team's efficiency, accountability, and helps reduce risk by addressing issues proactively and in a timely manner.

I experienced timely remediation because Cortex.io improved my team's efficiency and security visibility by centralizing service measures and risk data. Before using it, we tracked vulnerabilities and operational metrics in separate tools and spreadsheets, which made prioritization difficult. We reduced time spent searching for risk data because everything was on one dashboard, and my teams could quickly see scoreboards. We did remediation efforts to track it better and prioritize high-risk issues faster, and overall, it improved collaboration and decision-making within my security team. The operational health measures also made it more transparent.

One feature I would love to see in Cortex.io is more advanced real-time threat detection and automated alerting. I feel that this is very important, especially tailored for vulnerability severity and risk prioritization. Currently, Cortex.io provides excellent scoreboards and insights, but having real-time security alerts tied directly to risk levels, with automatic escalation and suggested remediation actions, will make the platform even more powerful in my opinion. This matters because it bridges the gap between observability and active security response, helping prioritize high-severity risks automatically, which can cut down the time spent manually correlating metrics and alerts.

One area for Cortex.io's improvement could be deeper automation and actionable recommendations. For example, the automation prioritization for vulnerabilities by risk could have more remediation actions based on historical issues, and also more security tools for real-time alerts.

I have been using Cortex.io for about five years.

Something I wish I knew before starting that could serve as good advice for others looking into using Cortex.io is to integrate it into your daily workflow. If possible, use it more than just part of your daily routine. I feel that it should be used actively as a decision-making tool and not just for visibility. The scorecards and metrics are powerful, and they are most valuable when teams actively use the insights to prioritize security and operational improvements. I advise others to regularly review scorecards and health metrics and to treat the dashboards as decision-making tools. Collaborate on remediation based on insights, track progress over time, focus on metric improvements, and maintain clear ownership and accountability, as Cortex.io works best when integrated into team processes, leading to better overall improvement. I would rate my overall experience with Cortex.io as an 8 out of 10.

Cortex.io is used at Cedric Masters to automate our after-sales operations, specifically when customers bring in vehicles for service. From the moment a customer walks in with their vehicle through invoicing and departure, to the purchasing of parts, Cortex.io supports the entire process.

When a customer walks in with their vehicle, customer service personnel enter the issue the customer came in with, whether they need a part change, car part replacement, or servicing. The issue then goes directly to the workshop if a part change is needed, and workshop personnel receive a notification. Upon receiving the notification, workshop personnel automatically check if that part is available, how many units are in stock, the costing, and all relevant details. After obtaining this information, they inform the technician, who verifies part availability and receives approval to begin the job. Once the technician completes the work, they log into Cortex.io and access the portal, after which customer service receives an invoice detailing the total cost of all services provided. The customer then pays based on this invoice.

Cortex.io also helps us manage inventory within the workshop.

Cortex.io has positively impacted our organization by making our processes faster. The main achievement is in processing speed, where delays in obtaining quotes and prices for particular services offered to customers have been eliminated and are now visible immediately. Previously, confirmation of prices required calling the workshop chairman by phone, but now every detail is on the system. There is no need to reach out to any staff to confirm prices because they are already available and seamless on the platform.

Cortex.io's best features for me include the pre-cloud deployment that we used. The pre-deployment feature was seamless for my team and was not difficult because Cortex.io has inbuilt databases, all of which are within the same environment. It was not difficult navigating through different deployment folders to deploy our applications, and it was easy for us to generate links to access our application.

Cortex.io's governance and security capabilities are fine. Regarding Cortex.io's reliability and accuracy, I can give a 100% rating for both capabilities.

Since we are using Cortex.io for the first time, I am impressed with what is already in place. As we continue using it for other applications, we might observe one or two aspects that could be improved, but for now, it has addressed everything we needed.

This is the first time we are implementing Cortex.io, as we used it to build one of our internal process automation systems here at Cedric Masters.

Cortex.io is stable.

Cortex.io's scalability is something I would give a range for.

I have not had any need to contact customer support at this time, so we are satisfied.

Before Cortex.io, we managed these operations manually. We did not previously use a different solution, as we have never used any previous solution.

I cannot share any relevant metrics such as money or time saved or fewer employees needed, so I do not have a return on investment to report at this time.

Regarding my experience with pricing, setup cost, and licensing, my developers used the free licensing for now, and we do not have access to the paid environment yet. This is something we will need to explore.

I did not evaluate other options before choosing Cortex.io because my developers were comfortable with it and had worked with it before in their previous applications.

We took advantage of Cortex.io's cloud environment for now, and we are trying to determine how we can incorporate an Azure cloud environment while also speaking to other cloud providers. Currently, we do not have any infrastructure in place, so we took advantage of Cortex.io's cloud environment.

My advice for others looking into using Cortex.io is to take advantage of the environment because it is seamless, scalable, and reliable. I rate Cortex.io a seven out of ten.

I use Cortex.io for cataloging and architecturing the catalog. I have used Cortex.io for eliminating tribal knowledge by creating a single automated system. Cortex.io maps all microprocesses, mono records, and machine learning models. It also has a Kafka topic integrated into it. Once we get a topic, we use that living context graph and then we can have a proper systematic deployment. The teams can instantly look into whatever specific component or dependencies it has. They can activate a deployment status, which can have a Slack channel integrated into it. Cortex.io has helped all the teams to integrate and work with a single skill.

If I want to have massive redundancy in the meantime, which is MTTR, then I can use Cortex.io. If I connect Cortex.io for data across 50 plus integrations, like PagerDuty or Jira, I can give it on a context graph for it, which reduces almost 75% of MTTR. For accelerating velocity, I can use Cortex.io. We have one-click self-service in Cortex.io. There are different services while shifting left security audits, and I can use Cortex.io there as well.

It is simpler if we use pretty simple language. For example, if I have microservices inventory into a dynamic or automatic execution engine, and I just tell it that I want to have a deeply interconnected automatic edge for every microprocess, it will create a backstage or confluence for it first. It requires access, and once we give them manual upkeep, it pulls all the metadata directly from the tool, from GitHub or AWS or Kubernetes. It maps on a real-time basis depending on the ownership. We can automate the service which can break at, for instance, 2:00 a.m. So at any time, anyone on call instantly sees who owns it, who is upstreaming the dependencies, and who is using an active Slack. Cortex.io has pretty good features.

For visualization, I have used it, but I cannot remember the specific details for now. We have used it for engineering intelligence. We have used DORA metrics.

I have not utilized Cortex.io's custom algorithm development feature.

I do not have a lot of knowledge about Cortex engineering.

I have done real-time analysis in Cortex.io.

For now, I do not have any recommendations regarding improvements.

Maybe once I have used it more widely, then I can have a suggestion. But for now, it has only been two to three months.

Regarding pros and cons, I do not still remember a lot of it from Cortex.io. One issue is that if we are taking, for example, if I want to automate any mapping or context mapping, then sometimes it gives a little problem. But cons are that for a predefined data structure, the internal engine basically does not give a proper data model. That can be a con.

I have not faced any issues with stability or scalability.

For now I do not have anything regarding limitations or performance issues with Cortex.io.

I have previously escalated questions to the technical support team regarding other matters, but not regarding Cortex.io.

I have used ITSM, but I cannot remember it properly now.

The setup process of Cortex.io is straightforward with no challenges or complexities. It was easy and straightforward.

Cortex.io is pretty much good and cost-effective.

I had a pretty good idea about the setup cost, but I cannot recall it precisely. I think it is around two to three dollars per credit if I use AI credit.

I did not purchase Cortex.io through the AWS Marketplace. I got it directly from the official website itself.

I have not used other solutions, but I think Cortex.io is better with Snowflake. It integrates well with Snowflake.

I have used the SaaS version of Cortex.io as well as AWS with Cortex.io.

Cortex.io is pretty much good, and I can rate it as an eight out of ten.

I have not explored all the features of Cortex.io. I have not used Cortex.io for investigating a large or huge amount of data. I have not gotten the development footprint from Cortex.io, so I cannot directly say it is a perfect ten out of ten. But for automation and all, I can give ten out of ten for automation and software cataloging. Cortex.io is pretty good.

Those data models with the internal engine that I mentioned are the reason that I give it an eight out of ten.

I can recommend Cortex.io in many other terms. Basically, if I compare it with Databricks LLM's model, then Cortex.io is pretty much faster. My overall rating for Cortex.io is eight out of ten.

My main use case for Cortex.io is that it provides the centralization of data from all the tools like CI/CD, monitoring, or incident management, making it a single source of truth and giving us a centralized command center for managing the health and reliability of the software services and the teams.

I use Cortex.io as a centralized command center to manage many microservices and engineering squads in a centralized way, which provides a centralized service catalog that centralizes data from all the microservices, allowing us to know who owns each service, its current health, and any documentation gaps.

The best feature Cortex.io offers is visibility, which is most relevant to the teams or services struggling with consistency and operational toil. This visibility gives us complete insight across our entire services and organization, which stands out the most for me.

The visibility feature has helped our teams by resulting in a 75% reduction in mean time to restore, allowing us to get alerts and incident history instead of relying on scattered spreadsheets and unreliable tools, thus saving around 15 to 20 hours per newly created service.

Cortex.io positively impacts our organization not only with visibility but also through its scorecards that grade each service against engineering standards, helping us check for on-call owners, passing tests, and production readiness.

The scorecards feature has changed how our teams work by providing me, as a manager, with a bird's eye view of operational maturity across teams and insight into pending incidents and responsible personnel.

I wish Cortex.io could cater to smaller teams struggling with visibility and operational toil, as a lighter version would be really great.

I believe AI adoption tracking can be enhanced and made more useful, as it currently lags behind market trends, especially in the area of security tracking for AI adoption. I think the interface and support could improve in that field.

I have been using Cortex.io for the last six months.

Cortex.io is very stable.

The scalability of Cortex.io is high.

Customer support is very professional, and I receive complete answers within a fraction of time.

I haven't used any other solution previously.

I deploy Cortex.io using both public cloud and on-premise solutions.

For my public cloud deployment, I use AWS, and for my self-hosted deployment, I utilize Kubernetes plus Helm.

I don't have complete visibility on how Cortex.io was purchased, but I believe it is through AWS Marketplace.

I have been seeing a 70 to 75% reduction in MTTR after using Cortex.io, which is the only metric I can share.

Regarding pricing, it is fair, and the setup cost for the self-hosted version is really simple. I am on a starter deal with five product tiers that include engineering intelligence and DORA matrices. Although I have faced budget restrictions regarding engineering intelligence for renewal, initial setup costs are justified and licensing fees are broadly acceptable in the industry.

Before choosing Cortex.io, I evaluated Spotify Backstage, which I believe is open source, and also looked at Port.io, as these are the two options I discussed or analyzed.

My advice to others looking into using Cortex.io is to start with a service catalog first, as scorecards are the real power feature, and it is beneficial to invest in Cortex Academy early for teams to learn how to use it effectively.

If Cortex.io gets better AI adoption tracking improvements and they provide solutions for smaller setups, that would also enhance its value.

I would rate this product an 8 out of 10 overall. I feel completely satisfied, as I do not have any other improvements needed, and have no additional thoughts about Cortex.io.

We are partners, and I stand more like an end user. We usually give Cortex.io's data visualization tool to customers, and that's how we use it.

Cortex.io is user-friendly, and its intuitive user interface is beneficial for non-technical team members. Our customers benefit from using Cortex.io and find this product beneficial for them.

Deployment for Cortex.io is not a simple process and is somewhat complex.

I have been working with Cortex.io for three years now.

Cortex.io's technical support is helpful and responsive. I would rate this aspect a nine out of ten.

Depending on the kind of project, two or three people usually take part in implementation.

I am familiar with Cortex.io. From my point, we are the support team, and we do not do the deployment. The engineers do the deployment, so I cannot give you the timeframe they use in doing those deployments. I have not used it myself, so I cannot tell you what I appreciate in this solution. My team is using it, and they usually give it to customers. In case they have any issue, they call us. From my department, I cannot give answers about why customers want this because I am more on the support side. I have no idea whether customers utilize Cortex.io's custom algorithms development feature because I am not aware of that. I am not sure how Cortex.io's automated data clearing impacts data accuracy, so I cannot give you that information either. My overall review rating for this product is ten out of ten.

I use Cortex.io for cloud security to check vulnerabilities and for application security to check misalignments, misconfigurations, and open scanning of vulnerabilities in the environment, in the cloud environment. My main use case for Cortex.io is cloud security.

The best features Cortex.io offers are the dashboard, which is very detailed, and the fact that you can run different kinds of testing such as application security testing, vulnerability management testing, and cloud security testing, allowing you to see the levels and rankings of how vulnerable those situations are, including the risk factors.

Out of those features, the dashboard stands out as the most valuable to me because it contains all of the information and analytics that is required.

Cortex.io has positively impacted my organization by helping as an accelerator in fixing a bunch of different code reviews and cloud-based systems, and we also use it for client work just to ensure that everything is properly shipped.

The specific outcomes from using Cortex.io include quicker insights represented by numbers and graphs, allowing us to see the severity level, the number of open vulnerabilities, and the number of open tickets, which helps us check which one to resolve first.

For now, I cannot think of how Cortex.io can be improved. I do not have more about the needed improvements, even small things that could make my experience better.

I have been using Cortex.io for about a year.

I would assume Cortex.io is stable.

I think Cortex.io is quite scalable for multiple users depending on the task and demand.

I haven't had to reach out to customer support, so I wouldn't know how it is.

Negative

I did not previously use a different solution.

I am not sure if we purchased Cortex.io through the AWS Marketplace, but it is provided by Palo Alto.

I cannot really tell if we purchased Cortex.io through the AWS Marketplace, but it is provided by Palo Alto.

I have not seen a return on investment, so I cannot share relevant metrics.

I am not sure about my experience with pricing, setup cost, and licensing.

Before choosing Cortex.io, I did not evaluate other options because I did not make the decisions; it was a business decision.

I think Cortex.io is a good space to start.