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OpenCost vs Kubecost upstream - is the open source version good enough?

Fiona H. — Fri, 25 Sep 2026 22:15:55 +0000

Everyone's buzzing about FinOps and slapping a cloud cost dashboard on their Kubernetes clusters. The default answer seems to be Kubecost. But then you hear "It's based on OpenCost!" and the open-source evangelists get a glint in their eye. Free must be better, right? Let's cut through the noise. OpenCost provides the core metric collection and cost allocation models. You can get your cost-per-namespace, pod, or label. The question isn't about the data model; it's about everything *around* it. * **Maintenance & Deployment:** You're on the hook for deploying, securing, and updating the Helm chart. That's engineering time, which has a cost. * **The UI Gap:** The OpenCost UI is... functional. It lacks the curated views, savings insights, and actionable recommendations Kubecost builds on top. You're getting the engine, not the dashboard with the gauges. * **Support & Updates:** Who fixes the breaking change when your cloud provider's billing API updates? With OpenCost upstream, it's you, waiting on the community. With Kubecost, it's their problem (which you pay for). So the real evaluation is: does your team have the cycles to build and maintain the operational wrapper, and are you sophisticated enough to act on raw cost data without the guided analysis? For some, OpenCost is plenty. For most, the "free" version becomes a time sink that delivers less actionable insight. I'm running OpenCost upstream in a dev cluster right now. The numbers *look* right, but I've already spent three hours tweaking Prometheus queries that Kubecost would have just given me. What's the actual TCO difference people are seeing?

ELI5: How do committed use discounts (CUDs) actually work in GCP?

integration_ian_2 — Thu, 24 Sep 2026 22:32:06 +0000

Hey everyone, I was just configuring some long-term workloads for a client and found myself once again deep in the GCP billing docs, untangling the specifics of Committed Use Discounts (CUDs). It's a powerful way to save, but the mechanics can be a bit opaque if you're used to the simpler reserved instances from other clouds. I thought I'd break down how they *actually* work from an integration and automation perspective, since that's where the real management happens. At its core, a CUD is a commitment you make to Google to use a specific amount of vCPUs, memory, GPUs, or even local SSDs in a particular region, for a 1-year or 3-year term. In return, they give you a significant discount (up to 70% for 3-year commitments) off the regular on-demand price for those resources. The key nuance is that **CUDs are applied automatically and continuously to your matching usage, across your entire billing account.** You don't assign them to specific VMs. Think of it like this: * You commit to $500/month worth of `n2-standard-4` resources in `us-central1`. * GCP gives you a 30% discount rate for that specific resource family in that region. * Every hour, the billing system scans all your running VMs in `us-central1`. * It finds any `n2-standard-4` instances (or instances in the same resource family, like `n2-standard-8`), and applies that discounted rate to the *first* $500 of that usage. * Any usage beyond the committed amount, or for different machine types/regions, is billed at the normal on-demand rate. * This happens automatically; you don't need to "launch" a special reserved instance. From an automation standpoint, this has big implications for how you track and manage costs. You'll want to ensure your monitoring and alerting systems are looking at the right metrics. Here's a quick look at the kind of data you'd pull from the Billing API or BigQuery to understand utilization: ```sql -- Simplified query to see CUD utilization vs commitment SELECT sku.description, SUM(usage.amount) AS usage_amount, SUM(cost) AS cost_before_discount, SUM(IFNULL(credits.amount, 0)) AS cud_credits_applied FROM `my-billing-project.gcp_billing_export.resource_usage` LEFT JOIN UNNEST(credits) AS credits WHERE service.description = "Compute Engine" GROUP BY sku.description ORDER BY usage_amount DESC; ``` The real trick is right-sizing your commitments. You need to analyze your stable, baseline usage. Tools like the GCP Recommender API are fantastic here—they can programmatically suggest commitments based on your historical data. You can then automate the purchase via the `committedUseDiscounts` methods in the Compute Engine API. A couple of pro-tips I've learned the hard way: * **Flexibility:** GCP CUDs are more flexible than some. For Compute Engine, commitments are generally for a *resource family* (e.g., N2) in a *region*, not a specific zone. This gives you some wiggle room. * **Stacking:** CUDs stack with *Sustained Use Discounts* (SUDs). SUDs apply automatically after a certain usage level in a month. The billing system applies CUDs first, then SUDs on any remaining on-demand usage, maximizing your savings. * **Mind the Scope:** You can make commitments at the *Billing Account* level (applies to all projects) or at the *Project* level. This is crucial for organizing costs in larger organizations. The goal is to cover your predictable, 24/7 workload footprint with commitments, and let everything else run on-demand, potentially with SUDs. It requires a bit more initial analysis than a simple reservation system, but the granular, automated application across your entire fleet is pretty powerful once it's set up. Hope this demystifies it a bit. If you've built any cool automations around purchasing or monitoring CUDs using the API, I'd love to hear about it. api first

Thoughts on the new GCP Carbon Footprint reporting? Accurate or greenwashing?

georgek — Mon, 24 Aug 2026 00:56:00 +0000

The recent announcement of Google Cloud's Carbon Footprint reporting feature presents an intriguing, yet complex, development for those of us who architect systems with both efficiency and environmental impact in mind. On its surface, providing granular, project-level carbon emission data—mapped directly to monetary cost—is a powerful tool for sustainable architecture. However, having spent considerable time navigating the often-opaque world of cloud provider metrics, I find myself critically examining the methodology and underlying assumptions. The core promise is that the tool uses Google's "region-based carbon footprint methodology," which supposedly accounts for the carbon intensity of the local grid where our workloads run. My immediate technical questions are: * **Data Granularity & Attribution:** How are emissions from shared infrastructure (networking, global load balancers, managed service control planes) accurately attributed to my specific project? The blog post mentions this is included, but the algorithm is a black box. In self-hosting, I can directly measure power consumption at the UPS. * **Embodied Carbon Consideration:** Does the calculation include the upstream carbon cost of manufacturing the hardware my virtual machines are ultimately slices of? Or is it purely operational emissions based on grid power? This is a significant omission if not addressed. * **Comparative Baseline:** The tool encourages moving workloads to "cleaner" regions. While positive, this feels analogous to carbon offsetting—it shifts the problem geographically rather than addressing the root cause: resource over-provisioning and inefficient application design. From a practitioner's standpoint, the most valuable outcome would be if this data can drive tangible changes in deployment patterns. For instance, could we use this data to build a CI/CD policy that fails a deployment if a more carbon-efficient region is available? A crude example using a hypothetical API check in a pipeline might look like: ```bash #!/bin/bash # Pseudo-code for a deployment gate CURRENT_REGION="us-central1" TARGET_REGION="europe-west4" # Fetch carbon intensity factors (hypothetical CLI) GCP_CARBON_CURRENT=$(gcloud beta carbon footprint region-intensity get $CURRENT_REGION) GCP_CARBON_TARGET=$(gcloud beta carbon footprint region-intensity get $TARGET_REGION) # If target region is significantly cleaner, halt and recommend if (( $(echo "$GCP_CARBON_CURRENT > $GCP_CARBON_TARGET * 1.2" | bc -l) )); then echo "ERROR: Target region has a 20% lower carbon intensity. Please review deployment location." exit 1 fi ``` Ultimately, my concern is whether this is a genuine engineering tool or a reputational exercise. Accurate measurement is the first, non-negotiable step to reduction. If Google provides transparent, auditable methodology details and allows for data export to our own monitoring stacks (e.g., via Prometheus), then this could be revolutionary. If it remains a dashboard with pretty graphs but no way to independently verify, it veers into greenwashing territory. I am particularly interested in members' experiences who have attempted to reconcile these numbers with their own power-based calculations for on-premise or colocated hardware. Take back control

Showcase: Our internal 'shame report' for top 10 wasteful resources actually works.

ci_cd_junkie — Sun, 23 Aug 2026 20:56:18 +0000

Okay, hear me out. We all know the theory: tag your resources, set up budgets, get alerts. But let's be real, those alerts pile up in a Slack channel everyone mutes, and the budget emails go straight to archive. The *intent* is there, but the *human psychology* isn't. We hit this wall hard last quarter. Our cloud spend was creeping up 15% month-over-month, and the usual "someone should look into that" wasn't cutting it. So, our platform team built something brutally simple: a weekly "Top 10 Wasteful Resources Shame Report." And I'll be damned... it actually works. Not just a little—it cut our idle spend by ~40% in eight weeks. The magic isn't in complex FinOps tooling (though we use those too). It's in the social engineering. The report is automated, but it *feels* personal. It goes to the *entire engineering org* every Monday at 10 AM. Here's what's in it: * **Ranked list** of the top 10 cost-ineffective resources, sorted by potential monthly savings. * **Owner:** Pulled from the `owner` tag (enforced by our pipeline, but that's another thread 😉). * **Resource Identifier:** Name, ARN, instance ID, etc. * **"Shame Metric":** The specific, measurable waste. E.g., "CPU utilization < 5% for 14 days", "Storage volume unattached to any instance", "Orchestrated container with zero requests over 7d". * **Estimated Monthly Burn Rate:** The cold, hard cash. * **One-Click Link:** A pre-generated link to the exact page in our cloud console where you can fix or terminate it. The output looks something like this (anonymized example): ```markdown 1. **EC2 Instance:** i-0abcd123456789ef0 * **Owner:** team-blue@ourcompany.com * **Issue:** Avg. CPU: 2.1% (last 14d). Instance type: m5.4xlarge. * **Monthly Waste:** ~$287 * **Act:** 2. **Disk Snapshot:** snap-9876543210fedcba * **Owner:** data-team@ourcompany.com * **Issue:** Orphaned snapshot from deleted instance (2023-11-05). 500 GB. * **Monthly Waste:** ~$25 * **Act:** ``` The key is the combination of specificity and publicity. No one wants their team's name sitting at #1 on that list two weeks in a row. It triggers a *direct, actionable* response. It's not a vague "cloud costs are high"; it's "you, personally, are paying $287 a month for a calculator." We built the generator as a simple Python script that runs in a scheduled CI job (GitLab CI, naturally 🏻). It uses the cloud provider APIs, fetches utilization metrics, cross-references tags, and formats the Markdown. The job posts it directly to a dedicated webhook in our general engineering channel. Has it caused some grumbling? Absolutely. A few folks felt "shamed" by the name. But the results speak for themselves. It turned cost optimization from an abstract "platform team problem" into a gamefied, engineering-wide hygiene practice. Now I'm curious—has anyone else tried a similar social-pressure tactic? Or are you all relying purely on automated downscaling and hard budget locks?

Help: Our Azure EA amortization is making actual spend impossible to see.

ChrisW — Sat, 22 Aug 2026 02:20:51 +0000

We're on an Azure EA. Our finance team switched to amortized cost reporting. Now our actual cloud spend is invisible until the end of the month. I need to see real-time, pre-amortization costs for engineering accountability. The Azure portal and Cost Management API seem to only show the amortized numbers. Has anyone cracked this? * What query or filter shows the raw, actual daily spend? * Do we need to push back on finance and use a different export? * Any tools that handle this well? Example of the problem: a VM scale-out spikes cost, but it's smoothed out in reports. Teams don't see the impact. // chris

Beginner question: How do I even get a consolidated bill for multiple AWS accounts?

chrislabs — Wed, 19 Aug 2026 15:00:54 +0000

I've seen several teams struggle with this initial step. The primary mechanism for consolidating billing across multiple AWS accounts is AWS Organizations combined with Consolidated Billing. You create a management account (payer account) and invite or create member accounts within an organization. All member account charges are rolled up to the single bill of the management account. Key points: * The management account is responsible for payment. It provides a single invoice and detailed cost and usage reports. * You can use Cost Allocation Tags to attribute costs to specific teams or projects across accounts. * For viewing and analysis, you enable AWS Cost Explorer in the management account. It provides aggregated and account-level breakdowns. The basic setup via AWS CLI is straightforward. ```bash # Create the organization (run from the intended management account) aws organizations create-organization # Create a new member account aws organizations create-account --email-address dev-team@example.com --account-name "Dev Account" # Or, invite an existing account aws organizations invite-account-to-organization --target Id=123456789012,Type=ACCOUNT ``` Important: Enable the `All features` mode in Organizations, not just `Consolidated billing`. This unlocks later cost-saving features like Service Control Policies and reserved instance sharing.

Has anyone tried GCP's Recommender API? Are the savings real or just noise?

clara_k — Mon, 17 Aug 2026 02:46:11 +0000

We've been evaluating GCP's Recommender API over the last quarter as part of a broader FinOps initiative, and I find the results to be a compelling case study in signal-to-noise ratio within automated cost tooling. The API is not a singular tool but a suite of machine learning-driven insights across several resource categories: Compute Engine, BigQuery, Cloud SQL, IAM, and others. The promise is straightforward: actionable recommendations to reduce waste or improve security posture. The core question is whether these recommendations translate into tangible, recurring savings or if they are merely surface-level optimizations that generate noise. From our analysis, the answer is nuanced and heavily dependent on your environment's maturity and the specific recommender type. **Key Findings on Compute Engine Recommendations:** * **Machine Type Rightsizing:** This is where the most substantial, real savings were identified. The API frequently flagged over-provisioned VMs (e.g., an `n2-standard-8` running at 12% average CPU) and suggested a smaller type. Implementing these yielded a ~18% reduction in our non-production Compute Engine spend. The savings are "real" because they are based on actual usage data. * **Idle VM Deletions:** Recommendations to delete completely idle VMs were highly accurate but often politically fraught within engineering teams. The savings here are real but require process (e.g., notification, grace period) to implement without disruption. * **Commitment-Based Discounts (Committed Use Discounts, Sustained Use Discounts):** These recommendations are mathematically sound but are strategic financial decisions, not pure cost-cutting. They convert variable spend into committed contracts. The "savings" are forecasts against an on-demand baseline and materialize only if your baseline forecast is accurate. **Areas of Caution and Perceived Noise:** * **Snapshot Management:** Recommendations to delete old snapshots were voluminous but often lacked business context. Blindly following them could violate data retention policies. * **Image Management:** Similar to snapshots, the "savings" from deleting old images are trivial unless you are managing thousands, and the operational risk of deleting a legacy image needed for a rollback can outweigh the minuscule cost benefit. * **BigQuery Slot Reservations:** Recommendations to switch to flat-rate pricing are highly situational. They can be beneficial for steady workloads but detrimental for spiky, variable usage patterns. Treating this as a generic "savings" tip is misleading. **Implementation Verdict:** The savings are "real" **if** you apply a critical, analytical filter. The API is excellent at identifying *technical* inefficiencies. It is not capable of understanding *business* context. A successful implementation requires: 1. Prioritizing recommender types (start with Compute Engine rightsizing). 2. Establishing a governance workflow to validate, approve, and implement recommendations. 3. Integrating the API findings into your existing ticketing or CI/CD systems for accountability. 4. Continuously measuring realized savings post-implementation, not just recommended savings. In essence, the Recommender API provides high-quality data points, but it is not a strategy. The onus is on the FinOps or cloud engineering team to build the processes that separate the signal from the noise. For organizations with mature cloud governance, it's an invaluable source of truth. For others, it may simply generate a backlog of unused recommendations. I'm interested in hearing from others who have moved beyond the initial evaluation phase—what was your realized savings rate versus what the API projected?

Hot take: Most CSP native cost tools are too reactive, not proactive.

data_meets_ops — Sun, 16 Aug 2026 04:26:00 +0000

I've been looking at our BigQuery spend dashboard this week, and it hit me: we're always explaining last month's bill, not preventing next month's surprise. The native cost tools from the big clouds show you what happened with great detail, but they feel like a rear-view mirror. Where's the guardrail? I want to set a policy that flags a query scanning over 1TB before it runs, or get an alert when a new dbt model starts materializing a massive table daily instead of incrementally. Right now, I get a report about it *after* the cost is already incurred. I think this reactive nature creates a few operational gaps: * **Development/Production mismatch:** A pipeline runs fine in dev on small data, but no one catches the full-scale cost impact before it hits production. * **Silent inefficiencies:** A poorly partitioned table or a runaway dashboard filter can burn money for weeks before showing up as an anomaly in a monthly report. * **Tagging as an afterthought:** The tools assume perfect resource labeling for showback, but enforcing tagging *proactively* at deployment is a separate struggle. Does anyone have a working setup that feels more proactive? I'm curious about: * Integrating cost checks into CI/CD for pipeline or schema changes * Tools (third-party or homegrown) that estimate run cost based on data volume and query patterns * Practical ways to set and enforce "budgets" for specific teams or projects that act as hard stops, not just alerts Our current stack is dbt-core, BigQuery, and Looker. I'd love to hear what's working for others, especially if you've moved from just monitoring to actually preventing cost overruns.

Hot take: If your cost report can't filter by git commit, it's not for devs.

amy_l — Sat, 15 Aug 2026 08:36:01 +0000

I’ve been reviewing a few cloud cost tools for my team lately, and I keep hitting the same wall. The reports are beautiful, the dashboards are slick, but when I try to connect a cost spike to *what we actually shipped*, I’m left digging through Slack and Jira. Here’s my thinking: if a tool can’t let me filter or attribute costs by git commit SHA, tag, or at the very least a deployment ID, it’s fundamentally missing the developer’s workflow. It’s built for finance, not for the engineers making the architectural decisions that drive cost. When a weekly report shows a 20% increase in our AWS Lambda spend, my first question is: which deployment caused it? Was it the new feature last Tuesday? The library update on Thursday? Without linking cost data to the commit that triggered the deployment, we’re stuck with guesswork and tribal knowledge. This makes meaningful feedback loops for developers almost impossible. I’m curious if others have found tools that do this well. We’re a SaaS shop running on Kubernetes and serverless, and I’d love to hear about your setup. How are you bridging the gap between your deployment pipeline and your cost data? What’s working, and what fell short? —Amy

Walkthrough: How we identified and eliminated $15k/month in idle EBS volumes.

Carol S — Fri, 14 Aug 2026 05:46:14 +0000

Our FinOps team recently concluded a quarterly cloud hygiene audit, with a primary focus on unattached storage. The findings were significant: we identified and remediated over 300 persistently idle Amazon EBS volumes, resulting in a direct monthly cost avoidance of approximately $15,000. This was not a one-time cleanup, but the establishment of a sustainable process. The initial identification was straightforward, but the remediation required a controlled, risk-aware approach. We started with a basic AWS CLI command to list all volumes and their attachment state. However, raw data is not an action plan. We enriched this data with several key attributes to create a prioritization matrix: * Volume age (creation timestamp) * Size and volume type (gp3, io2, etc.) * Associated resource tags (especially `Owner`, `Environment`, `Application`) * Last snapshot timestamp, if any This enrichment was critical. It allowed us to categorize volumes into clear action tiers: * **Immediate Deletion:** Untagged volumes in non-production environments, older than 90 days. * **Owner Validation:** Tagged volumes, or those in production VPCs, requiring confirmation from the tagged owner or application team. * **Snapshot & Delete:** Volumes with recent snapshots but no current attachment. * **Deferral:** Volumes associated with stateful services treated as pets (e.g., certain legacy databases), scheduled for architectural review. The operational cadence proved as important as the technical steps. We executed this as a three-week sprint: 1. **Week 1 - Report & Notify:** Distributed the enriched list to resource owners via service-specific Slack channels and Jira tickets, requesting confirmation within 10 business days. 2. **Week 2 - Follow-up:** Escalated unanswered requests to line-of-business managers. 3. **Week 3 - Controlled Remediation:** Based on gathered approvals and our tiering policy, we deleted approved volumes and created final snapshots where stipulated. The key to securing stakeholder buy-in was demonstrating the cumulative cost. Presenting a total like "$15k/month" resonated far more effectively than highlighting individual 50GB volumes. We've now automated the initial reporting and notification via a serverless Lambda function, scheduled to run bi-weekly, turning this into a routine operational check rather than a periodic "big bang" audit. The ROI on the initial 40 person-hours invested was realized within the first few days of the following month.