Our main use case for Zesty is automated cloud cost optimization, especially around compute and storage resources. We use it to identify under-utilized instances, unattached volumes, and over-provisioned workloads. Zesty continuously monitors usage patterns and adjusts resources dynamically, ensuring that we are not overpaying for capacity we don't actually need.

I have been using Zesty for about a year and a half now, mainly to optimize our cloud infrastructure costs and performance. Initially, we brought it in to get better visibility into our unused resources, but it quickly became part of our daily operations. What I appreciate is that it doesn't just show data; it actually recommends and automates optimization. Over time, Zesty helped us run a much leaner infrastructure without constant manual tuning.

One of the things I value most about Zesty is its flexibility in how much control you want. At a high level, it uses machine learning and real-time usage data to automatically adjust things such as compute size, storage capacity, and even cloud commitments. For example, Zesty can dynamically right-size CPUs, memory, or scale storage volumes up and down based on actual demand instead of fixed provisions.

Beyond that, we have also used Zesty for storage optimization, especially with unused EBS volumes. It automatically identifies orphaned resources and suggests cleanup actions. We have integrated it with our AWS setup, allowing it to continuously scan and optimize in the background. It is quite hands-off once configured.

The best feature for me is the automation around resource optimization. Instead of just giving recommendations, Zesty can actually execute changes safely with approvals. The real-time monitoring and predictive analytics are also impressive; it feels having an intelligent layer managing infrastructure efficiency.

Zesty's real-time monitoring of resources does not just become reactive; it actually anticipates spikes or drops based on historical trends. This gives us significantly more confidence compared to the static thresholds we used earlier. During a seasonal traffic spike in one of our customer-facing services, we normally had over-provisioned infrastructure to be safe, but Zesty predicted the spike window and adjusted compute capacity dynamically. We avoided overscaling while still handling the load smoothly and ended up saving around 18 to 20% in compute costs during that period, and more importantly, we did not see any performance degradation.

One area for improvement with Zesty would be more granular control over automation policies, as sometimes we want finer control over which resources get optimized automatically. Additionally, expanding support for multi-cloud environments would be beneficial since currently it is strongest in AWS.

Monitoring and logging could be enhanced with Zesty, particularly with more detailed historical insights; for example, tracking optimization impact over longer periods would be useful. Integration with external monitoring tools could also be improved to give a more complete observability setup.

I have been using Zesty for about a year and a half now, mainly to optimize our cloud infrastructure costs and performance.

Zesty had a strong positive impact on our organization's cloud efficiency, enabling us to become much more proactive about cost management instead of just reacting to high bills. It also helped us justify infrastructure decisions with data, which has been valuable for leadership discussions.

This was actually a pretty important shift for us; before Zesty, most of our cloud cost conversations with leadership were reactive, basically explaining why the bill went up and pointing to generic usage charts. It was hard to tie spending to specific engineering decisions or optimization actions. This made budgeting a bit of a negotiation every month rather than a data-driven discussion. With Zesty, we started using its cost allocation and optimization reports during monthly reviews. For example, when we downsized our non-production environment, we could clearly show that the specific set of actions led to about a 20% reduction in compute spend over six weeks. That level of traceability made leadership significantly more confident in approving further infrastructure changes.

I rate Zesty an 8 out of 10 because it delivers strong cost-saving and automation, which are its core strengths. A few limitations around customization and multi-cloud support hold it back slightly, but overall, Zesty is a very solid tool.

My advice to others looking into using Zesty would be to start with clear policies and gradually enable automation; do not try to optimize everything at once. Let Zesty learn your usage patterns first, as that is the approach that works best.

We did not purchase Zesty through the Amazon Marketplace.

Overall, I think Zesty is a great tool for teams looking to control cloud costs without adding overhead. It combines visibility, automation, and intelligence very well. While there is room for improvement, it delivers strong value, and I will definitely recommend it. The overall review rating is 8 out of 10.

Our primary use case for Zesty was Kubernetes cost optimization, especially around EKS clusters running production APIs and internal data services. We were dealing with the classic issue of over-provisioned CPU, memory, and storage because teams were sizing for peak traffic and never scaling back down. Zesty helped us automate pod right-sizing, reduce excess headroom, and manage node-level efficiency without hurting performance. That was the biggest value for us because it solved both cost and operational pressure at the same time.

The biggest benefit of using Zesty is that the team has now removed a lot of repetitive tuning work from DevOps. Our platform engineer used to spend hours every week reviewing cluster utilization, validating recommendations, and manually adjusting requests and limits. Zesty reduced that overhead significantly and let the team focus more on reliability and delivery work. Realistically, it saved us about 10 to 12 engineering hours per week.

The impact was pretty straightforward: lower cloud spend, less manual optimization work, and better cost predictability. It also improved the relationship between engineering and finance because cost conversations became less reactive and more data-backed. Instead of monthly cost surprises, we had much tighter control over spend trends. That made budget planning much easier for leadership. From a metrics perspective, Zesty reduced our Kubernetes infrastructure spend by about 24% in the first six months. We also cut wasted storage costs by roughly 18% and improved saving plan utilization by around 12%. On the operational side, we saved roughly 40 to 50 hours a month in manual cloud cost tuning. That combination made the ROI pretty easy to justify.

The biggest area for improvement is still reporting depth. Zesty is very strong on automation and execution, but the reporting layer is not as deep as some of the broader FinOps platforms. When finance wants highly customized showback or forecasting views, it covers the operational side well, but for more advanced finance reporting, we still had to supplement it. That was probably the most noticeable gap.

I would appreciate stronger documentation in a few areas, especially around Zesty's automation behavior and workload exclusion. The tool itself is easy to use, but there were times when we wanted more detailed technical guidance for debugging specific optimization decisions. Better implementation examples and more architecture-specific playbooks would make onboarding smoother. It is not a blocker, just an area where they could improve the experience.

Monitoring and logging could be a bit more granular. We wanted deeper auditability into why certain automated changes were made, especially when reviewing behavior across multiple teams. The visibility is good, but having more detailed event traces and easier correlation with the cluster would make troubleshooting faster. That would help teams trust automation even more.

I have been using Zesty for a little over two years now, mainly in a Kubernetes-heavy AWS environment where cloud costs were growing faster than engineering headcount.

One feature that deserves more credit is the way Zesty handled commitment optimization alongside infrastructure automation. Most tools treat those as separate problems, but Zesty connected them well enough that we could optimize both short-term usage and long-term commitment coverage in one workflow. That was useful because it gave finance and engineering a more aligned view of cost decisions. It made FinOps feel less fragmented.

That was one of the most useful parts of Zesty because it helped us manage saving plans much more intelligently instead of just locking into static commitments and hoping utilization stays high. Zesty continuously looked at usage patterns and adjusted recommendations based on real demand, which helped us avoid overcommitting during quieter periods or overbuying for short-term spikes. That gave our FinOps team much better visibility into where we are under-covered versus where we were at the risk of committing too much. In practice, it improved our saving plan utilization by around 12% and made forecasting a lot more predictable.

The best feature in Zesty for us was the automation-first approach. A lot of FinOps tools are great at dashboards, but Zesty was stronger at actually taking action: right-sizing workloads, adjusting storage, and tuning commitments continuously. I also appreciated that it was focused on execution instead of just reporting. That made it much more useful for an engineering team that needed operational outcomes, not just visibility.

Those conversations were much more productive because engineering and finance were finally looking at the same set of cost signals instead of debating those numbers. Before Zesty, finance would flag cloud spend after the fact, while engineering would usually have to go back and manually explain what changed. With Zesty, we had shared visibility into usage trends, optimizing actions, and projected savings. Budgeting became more proactive instead of reactive. It made monthly budget reviews a lot smoother and gave leadership more confidence in forecasting because the cost story was much easier to explain.

My advice would be to go into Zesty with clear cost ownership and your tagging already in place. The product works best when teams know what they are optimizing and who owns it. If your tagging and ownership are messy, any FinOps tool gets harder to operationalize. With that foundation in place, Zesty becomes much more effective.

Zesty delivered real value for us because it solved a practical problem instead of just giving us another dashboard. It helped reduce cloud waste, improved engineering efficiency, and made cost optimization much more operational. That combination is what made it worth keeping. It is one of the more practical FinOps tools we have used.

I would rate this review an 8 overall.

Our primary use case for Zesty is automated AWS cost optimization across compute, storage, and cloud commitments. We were spending significant engineering hours manually reviewing Reserved Instance coverage and Savings Plans every quarter, which was not scalable. Zesty took over that entire Commitment Management workflow and separately handles dynamic rightsizing for our Kubernetes clusters through Compass. The core value for us is eliminating manual FinOps overhead while keeping infrastructure costs aligned with actual usage.

The best features that Zesty offers include the Commitment Manager automated RI and Savings Plan management, the Hyber-scale technology inside Compass, and the ZestyDisk persistent volume auto-scaling. What makes them exceptional is that they are not just recommending actions; they are executing them continuously without requiring human sign-off every time. The AI-driven demand forecasting is genuinely impressive, as it analyzes historical and real-time patterns and adjusts resources before a spike hits, not after.

Out of those features, the one that saves me the most time or money is the Commitment Manager, which significantly streamlines our process. Before Zesty, our FinOps analyst was spending close to ten to twelve hours a week on commitment review, RI purchase, and coverage analysis. That is now essentially zero. Our DevOps engineers also have reclaimed time they were spending on Kubernetes resource tuning and storage incident response. Collectively, I estimate Zesty freed up somewhere around fifteen engineering hours per week across the team, which we have redirected into product work. It is one of those tools that pays for itself in recovered bandwidth alone before you even count the direct dollar savings.

One feature that I do not think gets enough attention is Zesty's Spot Instance confidence layer within Compass. Using Spot Instances for Kubernetes nodes used to feel risky because a sudden interruption could impact availability. Zesty's combination of Hyber-scale and advanced automation gave us real confidence to run a larger fraction of our nodes on Spot, which provides substantial additional savings on top of the rightsizing. We went from approximately twenty percent of Spot coverage to over sixty percent safely.

Zesty has positively impacted our organization across engineering, finance, and leadership. Engineering is happier because they are not babysitting infrastructure anymore. Finance now gets clean monthly reports showing optimized cloud spend trends rather than unpredictable bills. From a leadership perspective, it has allowed us to scale our cloud infrastructure significantly. We have grown our workload by over forty percent in the past year without a proportional increase in cloud spend. That kind of efficiency is hard to put a price on. Zesty has genuinely changed how our team thinks about cloud cost from a reactive manual chore to an automated, continuous background process. It is one of the clearest examples I have seen of an AI-driven platform delivering on its promise in a measurable way. If you are running significant workloads on AWS with Kubernetes and you are not using a tool to this effect, you are almost certainly leaving meaningful money on the table every single month.

The biggest gap in Zesty's offering is multi-cloud coverage. We run a small portion of workloads on Google Cloud, and Zesty simply does not support GCP meaningfully right now. That means we still have a manual optimization process for that environment, which creates some inconsistency in our FinOps workflow. A unified experience across AWS, Azure, and GCP would make Zesty a near perfect solution for our team.

The documentation has improved over time but it could still go deeper on edge cases. For example, what happens when Compass interacts with custom Kubernetes admission controllers or how Commitment Manager behaves during account consolidation scenarios. These are not deal-breakers, but when you are troubleshooting something unexpected at eleven PM, more detailed runbooks would save considerable back and forth with support.

I have been working with Zesty for a little over two years now, primarily in a cloud infrastructure and FinOps capacity.

In terms of specific outcomes, we have seen roughly a thirty-five to forty percent reduction in our overall AWS infrastructure costs since fully deploying Zesty's suite. Kubernetes costs specifically dropped by close to fifty percent once Compass was fully tuned. Storage costs went down by around twenty-five percent thanks to ZestyDisk. As mentioned, we reclaimed about fifteen engineering hours per week. The ROI calculation was very clear within the first ninety days. Zesty's fees were well covered by the savings generated.

My advice for others looking into using Zesty is to start with Commitment Manager if you are AWS heavy and not already maximizing Savings Plan coverage. The ROI is almost immediate with very little setup friction. Then layer in Compass if you are running Kubernetes because that is where the biggest cost reduction potential typically lives. Do not try to boil the ocean on day one; get comfortable with one module, see the savings, and then expand. Zesty's team is also really willing to work with you on onboarding, so lean on them. I would rate this product nine out of ten.

We use the solution for savings plans, reserved instances, and automation. We customize the reserved instances of savings plans depending on the account type.

What I like about Zesty is that it's basically being completely hands-off. It does what's expected of it, and you don't have to worry about cost or cost savings. You just automate that part of the cloud, and the buying and selling of all the little businesses and savings plans will be done for you.

One of the things Zesty recently launched was Windows reserved instances because they needed support until recently. One of the reasons we decided to onboard Zesty was that it started supporting Windows instances.

Zesty helped reduce our cloud cost without needing a FinOps team to constantly look at cloud costs. Managing all of that is completely automated now.

I would like to get RDS-reserved instances that I could buy and sell, but that's a limitation on AWS.

I have been working with Zesty in my current company for about three months, but I worked with it for a year in a previous company.

We haven’t faced any issues with the solution’s stability.

Zesty is a scalable solution. Around four people have access to the dashboard in our organization.

The solution’s initial setup is straightforward. To deploy the solution, you need to add an AIM role on the accounts you need to onboard. We enabled SSO on the portal. The SSO is just a question of negotiating the service and sharing the certificates and private keys with them, which was very easy. The initial setup took around five minutes.

We got around 48% cost savings with Zesty.

The solution’s pricing is reasonable.

Before choosing Zesty, we evaluated other tools like Cloudability. We tried other solutions, but Zesty turned out to be the best because of its insurance plan. If Zesty makes a mistake and over provisions the commitment, we don't pay their bill until the commitment is credited to us.

The way we're using it is completely hands-off. We just trust that they do the automation to buy the reserved instances and the same instance for us. The solution has other features like the storage explorer, which can save you money on new storage. It also has the Zesty disk, but I never use those because I never had the use case for them.

The solution's AI functionality is really easy to use and automatic. Since it's all automated, we don't see what the AI is doing. We just keep the savings at the end of the month and pay the bill. It is not difficult to maintain the solution. I would recommend the solution to other users because it is very easy to use.

The solution is easy to use and onboard, and its support team is really good.

Overall, I rate the solution a nine out of ten.