8 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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   Future Consideration  ·  1 comment  ·  Integrations  ·  Delete…  ·  Admin →
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2. Improve Insights API / CRE UI to enable semi-automated submissions by CREs

   Hi,

   I think it would be great for customers and CREs alike if CREs could, quickly and seamlessly, create multiple Custom Insights based on findings from deep technical dive meetings they hold with customers.

   A pilot use case I had in mind is Technical Kick-Offs (TKOs): These are multi-hour engagements that comprehensively cover the customer's architectural design as it involves walking through a long check-list of things to ask about. I have a CSI board task set up (https://doitintl.atlassian.net/jira/core/projects/CSI/board?selectedIssue=CSI-1507&text=insights) which describes the following workflow I'd like to build that, if enabled by an upgrade to the Insights API / web UI, would be an enormous time-saver for CREs and would encourage follow-through by the customer on action items uncovered during the TKO and other long-form calls:

   TKO/Customer Call Recording --> Insights Generator
   The idea behind this app would be to:

   1. Take a video recording (e.g. a TKO which can sometimes last hours) and use an LLM or a Transcription API to generate a transcript

   2. Send that transcript through another LLM conversation to uncover recommendations for the customer and format them to be compatible with the DoiT Insights API

   3. Call an Insights API that pre-populates the fields for a customer in a web browser UI that allows the CRE to review auto-generated Insights, update any of the fields, add new Insights or delete some, etc. before they are finalized & submitted to be visible to the customer.

   The feedback I have gotten from the CREs running TKOs is that in addition to the hours-long customer meeting, they wind up spending 1 to 2 full days writing up a summary of all of the findings and then working with the customer + TAM to deliver this information and encourage follow-through. Imagine if that 1-2 days could be converted to 5-10 minutes of post-call work from the CRE after a cutting-edge LLM parses the recording and sets up >95% of the recommendations correctly. CREs would just need to review the content of the Insights to make sure they're accurate, fix a few minor things or rephrase how some are written, and hit Submit for the customer to see. You could even have submission of CRE-submitted Insights ping the TAM or AM on the account to ensure they are never missed by the xAMs.

   I talked with Peter Mihal a few weeks back and he thought this was a great idea, but the product team needs to make time/prioritize to implement it. The Insights API is pretty bare-bones today and wouldn't fully support this use case as-is; see the up-to-date API docs here: https://doitintl.atlassian.net/wiki/spaces/ENG/pages/1060700170/Insights+APIs

   I think that having a web UI pop for the CRE to review that is auto-populated with the Insights submitted by an LLM is key to this, so it's not just an API support ask. I'm not sure how much work this would involve, but I can see it being reusable in a wide variety of use cases. Not just TKOs, but all general calls with customers could leverage this. It could even become a standard part of the CRE workflow: If you are meeting with a customer, ask if they're OK with recording it, and send that recording through an app that auto-generates Insights based on what was discussed.

   Hi,

   I think it would be great for customers and CREs alike if CREs could, quickly and seamlessly, create multiple Custom Insights based on findings from deep technical dive meetings they hold with customers.

   A pilot use case I had in mind is Technical Kick-Offs (TKOs): These are multi-hour engagements that comprehensively cover the customer's architectural design as it involves walking through a long check-list of things to ask about. I have a CSI board task set up (https://doitintl.atlassian.net/jira/core/projects/CSI/board?selectedIssue=CSI-1507&text=insights) which describes the following workflow I'd like to build that, if enabled by an upgrade to the Insights API… more

   5 votes

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3. Submit Support Tickets directly from Slack (Ava Chatbot to Human Led)

   I have a few customers who are requesting that we offer a chatbot or an ability to directly submit support tickets and manage them through Slack. They had this feature at their last partner and it made it very easy for them to communicate and update support tickets.

   4 votes

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4. 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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5. 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
   2 votes

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6. Open AI integration with DoiT Cloud Intelligence

   The rapid adoption of OpenAI, has revolutionised business operations. However, the increased utilisation of these powerful tools often leads to rising and unpredictable costs, compounded by the escalating expenses associated with cloud infrastructure.

   DoiT Cloud Intelligence (DCI) integration with OpenAI is designed to provide customers with a comprehensive solution for monitoring and managing their OpenAI expenses alongside their existing cloud costs, ensuring unified visibility and control over their IT investments. Additionally you can leverage DoiT existing and new FinOps capabilities to exercise better control over your costs, and ensure that AI investments yield maximum value while remaining within budget constraints.

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7. Ingest AWS email notifications and AWS Personal Health Dashboard events to the Cloud Incidents

   Be able to ingest AWS Health events from the Master Payer Account and linked AWS accounts and show them in the Cloud Incident panel.

   If possible, be able to read each customer's MPA email to extract any notifications we might come up with. eg: Abuse reports, Automated alerts sent via email

   5 votes

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   Refinement/Prioritization  ·  0 comments  ·  Integrations  ·  Delete…  ·  Admin →
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8. 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.

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