32 results found

1. Enable importing Google Cloud billing account historical data both standard and detailed BQ exports.

   Most Google customers do not have BQ export enabled for their direct / other partner billing accounts. The few who do, usually have only the standard BQ export options. Importing historical data makes the Navigator more useful faster and enables customers to compare their better new DoiT pricing with their older pricing. Allow customers to import all types of BQ exports and not just the new detailed schema which most customers do not use.

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2. Support Costs SPOG

   We think it would be helpful to have an "Understand my Support Costs" dashboard under Billing that would provide a SPOG for all support costs across Solve and PLPS for all Providers

   8 votes

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   Future Consideration  ·  0 comments  ·  Billing & Invoicing  ·  Delete…  ·  Admin →
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3. Introduce currency value for custom metrics

   Custom metrics don’t produce a “currency” value; it’s either a number or a percentage. Could we introduce a currency value for custom metrics like "GCP w/o Discounts"?

   3 votes

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4. EKS by Argo Rollouts

   Hello Team,

   The customer would like to have the ability to present the K8S costs by rollouts and not only by deployments.

   Currently, the kube-state-metrics doesn't natively export metrics for custom resources (CRDs) in the same way it does for Kubernetes native resources like Pods, Deployments, StatefulSets, etc.

   It would be important to get another layer of granularity that would allow to analyze EKS costs based on rollouts.

   5 votes

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5. Testing notifications to verify setup

   Hi Team, would it be possible to add a "Test Alert" button for Cost and Budget Alerts. This allows customers to verify if the channel is working correctly without altering the logic of their alerts.

   10 votes

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6. Right-sizing Serverless Workloads

   PerfectScale is making right-sizing Kubernetes workloads a breeze. Cloud companies (including Doit) have a lot of their workloads in serverless technologies as well, AWS Lambda and ECS/Fargate, GCP Functions and CloudRun. The basic principle is very similar, looks at the CPU/Memory/GPU/Network usage of the workload, and adjust configuration as needed.

   ---

   Cloud companies heavily rely on serverless and containerized workloads such as AWS Lambda, AWS ECS/Fargate, GCP Cloud Functions, and Cloud Run. These platforms promise automatic scaling and flexibility, but in reality, workload sizing presents significant challenges:

   • Many companies allocate excessive resources (CPU, memory, GPU) out of fear of performance bottlenecks. For example: A function that needs 256MB of memory might be allocated 2GB just to be safe, leading to massive cost inefficiencies.
   • If workloads lack the right resources, they experience latency spikes, throttling, or outright failures. For example: A Cloud Run service with too little CPU might struggle under load, delaying responses and causing timeouts.
   • Cloud monitoring tools like CloudWatch, GCP Metrics, and Datadog provide metrics, but they don’t auto-tune workloads. Engineers must manually adjust configurations, requiring deep knowledge of workload behavior.
   • GPU instances and AI workloads require precise tuning to avoid wasting expensive GPU cycles. For example: Allocating an entire A100 GPU for an inference task that could run on a fraction of the hardware.
   • Default autoscalers react to simple CPU or memory thresholds but don’t optimize for cost vs. performance trade-offs. For example: AWS Lambda scales horizontally, but if each instance is over-provisioned, scaling still leads to unnecessary cloud spend.

   By applying AI-driven right-sizing, we can continuously analyze workload behavior and automatically adjust resource allocation, leading to:

   • Automatically optimizes CPU, memory, and GPU allocation based on real-time usage patterns. For example: A Cloud Run service that usually needs 500m CPU and 1GB RAM but experiences occasional spikes can be set to adjust dynamically instead of being permanently over-provisioned.
   • Ensures just enough resources are allocated to meet performance SLAs without overpaying. For example: AWS Lambda functions are dynamically tuned to use the lowest memory setting that still completes execution within time constraints.
   • Monitors workloads over time and adjusts allocations dynamically to avoid drift. For example: A workload that initially needed 2 vCPUs but later drops to 1 vCPU is automatically resized to reduce costs.
   • Traditional autoscaling only scales horizontally, but this solution right-sizes individual instances, preventing waste before scaling. For example: AI inference workloads running on GPUs are allocated precise memory and GPU resources, minimizing wasted compute cycles.
   • AI can learn from historical data to suggest better workload configurations before problems occur. Engineers no longer have to manually adjust configurations based on guesswork or periodic audits.

   Current approach coping with these issues:

   • Engineers adjust CPU/memory settings through trial and error.
   • Defaulting to high allocations to prevent failures.
   • Basic CPU/memory thresholds trigger scaling events, but they don’t prevent waste.
   • Teams manually audit cloud bills and look for over-provisioned services, but by the time adjustments are made, money has already been wasted.
   • Companies often pay for unnecessary resources just to be safe.
   • Some workloads remain under-provisioned and struggle under load.
   • Configurations don’t adjust dynamically as usage patterns change.

   How would it work after implementing this solution?

   • No more manual tuning; workloads are dynamically adjusted based on real-time behavior.
   • Right-sizing prevents unnecessary spend while maintaining performance.
   • Optimized workloads reduce latency and failure rates.
   • AI ensures that every new instance is right-sized from the start.
   • Instead of firefighting resource allocation, engineers focus on building features.

   What must cloud users change in order to receive the full benefit of such technology?

   • Connect Lambda, Cloud Run, ECS/Fargate, Kubernetes to the optimizer.
   • Let AI analyze workloads and learn from usage patterns over time.
   • Move away from static configurations and let the system dynamically right-size workloads.
   • Instead of just setting CPU/memory manually, let AI balance cost and performance dynamically.

   By removing inefficiencies in serverless and containerized workloads, this solution makes cloud infrastructure smarter, leaner, and more adaptive—giving companies a huge competitive edge in cloud cost management and performance optimization.

   PerfectScale is making right-sizing Kubernetes workloads a breeze. Cloud companies (including Doit) have a lot of their workloads in serverless technologies as well, AWS Lambda and ECS/Fargate, GCP Functions and CloudRun. The basic principle is very similar, looks at the CPU/Memory/GPU/Network usage of the workload, and adjust configuration as needed.

   ---

   Cloud companies heavily rely on serverless and containerized workloads such as AWS Lambda, AWS ECS/Fargate, GCP Cloud Functions, and Cloud Run. These platforms promise automatic scaling and flexibility, but in reality, workload sizing presents significant challenges:

   • Many companies allocate excessive resources (CPU, memory, GPU) out of fear of performance bottlenecks.… more
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7. A color picker for reports

   How about a color picker allowing us to change the color scheme in the reports?

   I'm sure we all have separate dashboards that would benefit from consistency in the color scheme, or that we transition from former dataviz tools and would be glad to find the same colors here. Or that some of us simply would prefer another scheme.

   Anyway, while not critical of course, that would make my life easier.

   1 vote

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8. Invoice explainer for marketplace invoices

   Customers can opt into separating their Marketplace spend into a separate invoice. Whenever this happen, Invoicing explainer should work for Marketplace invoices as well.

   2 votes

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9. Support Cloud Carbon Footprint Reporting

   New legal requirements mean more companies need to report on the carbon footprint of their cloud usage. AWS' own tool is often incorrect, and Azure's is difficult to use plus it requires customers to go into PowerBI. Ideally this would be something Navigator could support in our own reporting view.

   11 votes

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10. Regex within Attribution Group

    Regex within attribution group will minimize the user overhead of adding manually attributions to the attribution group.

    When 10's or 100's of attributions are created with a naming convention (Example: End Customer Name) and all are added to a single attribution group, having a regex in this case will simplify the process of adding those attributions. Instead of manual selection, a single line of Regex will auto select the attributions created but also will automatically add the newly created attributions without the need of user intervention

    3 votes

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11. Enable Drag & Drop of images / files while opening Support Requests

    Enable Drag & Drop of images / files while opening Support Requests, similar to (for example) Gitlab experience

    2 votes

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12. AI fueled insights based on cloud logs

    Cloud infrastructure has a huge number of logs (AWS CloudWatch Logs / AWS CloudTrail / AWS VPC Flow Logs / GCP Cloud Logging / GCP Audit Logs). These include an enormous amount of rapidly changing information, often repeating errors that might have appeared and no one noticed. We could utilize our capabilities in cloud infrastructure and AI to highlight insights, and even offer suggestions on how to fix issues, based on the content of these logs.

    • Cloud environments generate millions of log entries per day. The sheer volume makes it impossible for engineers to manually review logs effectively.
    • A significant portion of logs are repetitive, contain expected behavior, or have no real actionable value. Critical errors can be buried under an avalanche of trivial events.
    • Most log monitoring today is focused on reacting to incidents after they cause problems, instead of proactively identifying potential failures or quickly responding no problems before they become significant.
    • Errors often lack deeper explanations or potential fixes. Engineers may spend hours investigating a single issue, only to realize it's a known problem with a simple resolution.
    • New engineers may struggle to recognize recurring patterns, and tribal knowledge about system quirks isn’t easily shared or accessible.

    Currently, organizations rely on dashboards, search queries, and alerting systems to deal with logs, but these approaches are manual, time-consuming, and ineffective at recognizing complex patterns across services.

    By leveraging AI and deep expertise in cloud infrastructure, we can transform raw logs into actionable intelligence, allowing companies to shift from a reactive approach to a proactive, self-improving system.

    • AI can analyze millions of logs in real time, identifying patterns and anomalies across multiple services.
    • It can group related errors together, reducing noise and presenting only meaningful insights.
    • By correlating logs with historical data, AI can suggest fixes automatically. For example: If an error related to IAM permissions appears, the system can propose the correct policy update or provide a link to relevant documentation.
    • Instead of waiting for outages, the system can predict issues before they escalate. For example: If repeated database connection timeouts are detected, the system can recommend scaling adjustments or preemptive mitigation steps.
    • AI can build and maintain a knowledge base of recurring issues and their resolutions. So that new engineers can quickly see previous incidents and fixes, reducing onboarding time and reliance on tribal knowledge.
    • AI-driven suggestions cut down troubleshooting time from hours to minutes, leading to faster incident resolution and significant cost savings in engineering effort and downtime.
    • AI insights can reveal long-term trends that impact system architecture, cost, and security. For example: Identifying inefficient compute resources based on usage logs and recommending cost optimizations before the next billing cycle.

    Current approach by many cloud consumers:

    • Engineers manually review logs using Kibana, CloudWatch, GCP Logging, or Datadog.
    • Alerts are created for known issues, but unknown patterns go undetected.
    • When an incident occurs, engineers grep through logs, cross-reference with documentation, and escalate when needed.
    • Resolving critical failures can take hours or days, leading to revenue loss and operational strain.
    • Engineers spend significant time searching for issues instead of solving them.
    • Dashboards only track known issues, missing emerging problems.
    • Log reviews don’t improve system resilience beyond immediate fixes.
    • More log ingestion requires higher monitoring costs, yet insights remain limited.

    How does it look like after this solution is implemented?

    • Instead of digging through raw logs, users receive curated insights that highlight critical patterns.
    • Engineers get direct recommendations on resolving issues, including code snippets or config fixes.
    • The system detects emerging problems early, reducing downtime and preventing outages.
    • AI updates its RAG knowledge base, allowing teams to learn from past incidents without manual effort.

    What must cloud users do in order to receive these benefits?

    • Organizations must route their logs through the AI engine for analysis.
    • Teams need to trust AI insights and incorporate suggestions into their workflows.
    • Where safe, companies should allow AI to auto-fix certain issues (e.g., auto-scaling, permissions).
    • Feedback loops must be created to enhance AI predictions over time.
    • Engineers must move beyond "fixing problems as they arise" to preventing issues before they occur.

    This is the kind of step-change improvement that turns cloud logging from an expensive burden into a strategic advantage.

    Cloud infrastructure has a huge number of logs (AWS CloudWatch Logs / AWS CloudTrail / AWS VPC Flow Logs / GCP Cloud Logging / GCP Audit Logs). These include an enormous amount of rapidly changing information, often repeating errors that might have appeared and no one noticed. We could utilize our capabilities in cloud infrastructure and AI to highlight insights, and even offer suggestions on how to fix issues, based on the content of these logs.

    • Cloud environments generate millions of log entries per day. The sheer volume makes it impossible for engineers to manually review logs effectively.
    • A significant… more
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13. EKS Widget or Metric to show un-used compute within 'Allocated'

    Hello Team,

    the product currently provides preset metric on EKS Capacity Utilization which measures the utilization of resources across Amazon EKS clusters. However, there is no metric, or widget that would tell the customer the 'un-used compute within the Allocated nodes.

    It would be great to have this quick visibility to overview the Allocated EKS layers.

    2 votes

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14. Summary of my Alerts Configuration

    Hi Team,

    would it be possible to have a summary in plain english of what is the expectations for my Alerts setup? Ideally a feature that looks at my Alerts configurations (both Budgets and Cost Alerts) and describes in plain english that I should receive an email when X happens, Y happens, etc. in a single pane of glass. It would be great to set expectations and avoid misconfigurations of customer alerts

    6 votes

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15. Filter Insights by Attribution

    As an individual on a team or working on a specific product, I'd like to see which portion of my company's total insights can be implemented by my team. This helps me understand which cost opt / security recommendations are within my team's control.

    Similar concept to why we allow users to filter for anomaly alerts by an Attribution or might recommend customers to create customers dashboards made up of reports that are filtered to an Attribution (rather than looking at a Lens). Less noise + I know what I can take action on immediately. I believe this would also increase likelihood that Threads is used alongside an Insight.

    With the experience today, I'd have to look at the breakdown in an insight and understand whether the associated accounts that can be optimized belong to my team/purview. Or I'd have to click the "Open report" button in an insight and then filter by my Attribution.

    As an individual on a team or working on a specific product, I'd like to see which portion of my company's total insights can be implemented by my team. This helps me understand which cost opt / security recommendations are within my team's control.

    Similar concept to why we allow users to filter for anomaly alerts by an Attribution or might recommend customers to create customers dashboards made up of reports that are filtered to an Attribution (rather than looking at a Lens). Less noise + I know what I can take action on immediately. I believe this would also… more

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    Future Consideration  ·  AdminAnonymous (Admin, DoiT) responded

    Attribution needs a wider-net UX consideration across all of our products

16. S3 File-Level Cost Allocation Tags

    This feature will allow users to assign AWS cost allocation tags at the file level within S3 buckets. This will provide more granular insights into resource usage and cost distribution.
    Users can tag individual files based on project, department, or any other relevant identifier, and the DoiT console will then track and report costs associated with these specific tags.
    This should be a feature that should be an opt-in per bucket due to the possible cost associated with analyzing and retrieving that metadata.

    1 vote

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17. Split Cost Allocation for ECS and EKS

    Split Cost Allocation for ECS and EKS support. Would be really good if the reports were able to support split allocation data.

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18. Pagination In Report Query Endpoint

    Issue: trying to query large and granular datasets from the Billing data source.
    This is not possible without pagination since the rate limit and payload size limit are insufficient to do it in scale.
    Pagination is already implemented in other endpoints in the analytics API but not in the endpoint that will be most affected by it.

    4 votes

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19. Bill Explainer - Your Invoice Without DoiT

    Hi team.
    I’ve received feedback from several customers who are confused by the AWS bill explainer. Currently, the cost explorer shows two columns: one for the AWS bill, which is lower than the DoiT invoice ($110.4k vs. $118.6k). From the customer’s perspective, this discrepancy makes it seem like they are overpaying to use DoiT. The difference is causing confusion, especially since they understand that there is an SP delta in the calculation, but they struggle to fully grasp how the gap is determined. Customers are asking for a clearer and simpler way to understand the breakdown of their costs.
    Proposed Solution:
    I think we can make this clearer by adding a third table below and it will be called the “Invoice without DoiT”. Here’s how I envision it:
    DoiT’s invoice to you
    Invoice without DoiT
    AWS Invoice to DoiT
    This approach would give the customer a clearer view of the cost breakdown and highlight exactly where DoiT is adding value. It also removes the need for customers to manually calculate the differences between the AWS bill and the DoiT invoice, which is often where confusion arises.
    Why this helps: By laying out the cost structure in this way, it makes it easier for customers to see the difference between what they would pay without DoiT and what they’re actually paying with DoiT.
    This was the process (screenshot below) used to show the calculation for one of our customers, e5.ai, where we break down how their bill looks with DoiT, without DoiT, and the comparison with the AWS cost explorer. Having all the numbers laid out side by side helped them understand exactly where the discrepancies lie and what value we’re adding.
    Happy to work on in this feature together and implementing it.
    Best,
    Dmitry

    Hi team.
    I’ve received feedback from several customers who are confused by the AWS bill explainer. Currently, the cost explorer shows two columns: one for the AWS bill, which is lower than the DoiT invoice ($110.4k vs. $118.6k). From the customer’s perspective, this discrepancy makes it seem like they are overpaying to use DoiT. The difference is causing confusion, especially since they understand that there is an SP delta in the calculation, but they struggle to fully grasp how the gap is determined. Customers are asking for a clearer and simpler way to understand the breakdown of their costs.
    Proposed… more

    1 vote

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20. Allow for Invoice Explainer downloads

    Customer would like to be able to download all data from invoice explainer into a table.

    3 votes

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