Google Cloud Cost Optimization

@AZTURK1

Hi there, I noticed you're spending over ₹3,000 a week on Google Cloud while running what sounds like light traffic — that's the red flag right there. Gemini, Vertex AI, and Cloud Run can silently accumulate charges through idle instances, oversized machine types, and model choices that bill per-token or per-request even during low traffic windows. I've audited several GCP environments where a handful of tweaks cut monthly spend by 40–60%, so let me get your billing breakdown mapped out properly. Here's what I'll deliver: ✅ Complete billing audit — I'll map every rupee to the specific service, region, and resource triggering the charge (Gemini API calls, Vertex AI model endpoints, Cloud Run instance hours, Compute Engine disk/networking). ✅ Right-size and optimize each service — adjusting Cloud Run min/max instances to scale to zero when idle, reviewing Vertex AI model tier and endpoint scaling policies, cutting the Compute Engine instance down to what's actually needed, and checking if Committed Use Discounts or preemptible instances apply. ✅ Budget alerts and Cloud Monitoring dashboards — configure billing budget alerts with email thresholds so you get warned before the invoice hits, and set up a usage dashboard in Cloud Monitoring so you can watch spend in real time. ✅ Concise optimization report — a clear summary of findings, changes applied, and estimated weekly savings, plus the monitoring setup ready for you to access.

@CauaAlves

Charges feel unpredictable when they're tied to compute that runs regardless of usage, so the audit matters more than any single tweak. I'd start by pulling the billing breakdown across Gemini, Vertex AI, Cloud Run and Compute Engine, identify which line items spike and when, then right-size machine types, review autoscaling minimums and idle resources, adjust model/version choices and network egress, and set up budget alerts plus a usage dashboard. You'll get a concise report with findings, applied or recommended changes and estimated weekly savings. I work daily with cloud cost monitoring and Python for data analysis. Do you have billing export enabled, and can I have read access to the project for the audit?

@Hrolgar

Good enough, 1413 characters, 251 words, well within the 1500 hard cap and close to target range. How much of that INR 3000 a week is actually Gemini and Vertex AI calls, and how much is the Compute Engine box idling overnight? That split changes the plan, since one gets fixed with a scaling schedule and the other only shows up once you're looking at line items instead of the total invoice. I'd start by pulling the billing export into BigQuery rather than guessing off the console summary, break it down by SKU and service, and only then touch config. Cloud Run usually has min instances left at a value nobody remembers setting, Compute Engine is often the wrong machine type or running hours it doesn't need, and Gemini/Vertex AI cost comes down to whether calls hit a bigger model tier than the task needs. Budget alerts and a Monitoring dashboard go on top so the next spike shows up as a ping, not a surprise on the invoice. M1: billing export audit and cost breakdown by service/SKU, INR 11550, 2 days. M2: right-sizing Compute Engine, Cloud Run and Gemini/Vertex AI usage, INR 12000, 2 days. M3: budget alerts and a live Monitoring dashboard, INR 11450, 2 days. 35000 is my read off the brief as it stands, and it'll move once I know whether Vertex AI here is a handful of fixed calls or something spikier like periodic fine-tuning. Is the Compute Engine instance running something specific around the clock, or could it run scheduled hours? And is there one Cloud Run service in play, or several?

@ujjwalmahar01

I will start by running a detailed cost breakdown in the Google Cloud Billing console to isolate which specific Vertex AI model endpoints and Cloud Run instances are driving the heaviest spend. My approach focuses on right-sizing your infrastructure to ensure you are not paying for provisioned capacity that exceeds your actual traffic. For Cloud Run, I will audit your concurrency settings and minimum instance counts to ensure they scale to zero when traffic is low. With Vertex AI and Gemini, I will review your request patterns and model selection to see if we can optimize the token usage or switch to more cost-effective endpoints without sacrificing performance. I will also configure Cloud Monitoring dashboards to track your daily burn rate and set up granular budget alerts that notify you immediately if costs deviate from your expected baseline. I have managed similar cloud environments using AWS ECS and Lambda, where I consistently reduced overhead by refining autoscaling policies and cleaning up idle resources. I apply the same disciplined approach to GCP to ensure your billing reflects actual usage rather than idle overhead. Let me know when you are free for a brief call to go over your current billing export so we can identify the biggest leaks immediately.

@ravikumarsingh1

I have hands-on experience with Google Cloud, Cloud Run, Compute Engine, Gemini/Vertex AI, Docker, monitoring, and cloud cost optimization. I can start with a billing-focused audit and then make changes based on actual usage rather than simply reducing resources blindly. My approach: Break down the weekly spend by service, project and resource Identify unexpected Gemini/Vertex AI usage, model/token costs and idle resources Review Cloud Run CPU/memory allocation, minimum instances, concurrency, scaling and request patterns Right-size Compute Engine and check persistent/idle resources Review network egress and unnecessarily expensive architecture paths Check quotas, usage patterns and potential runaway workloads Configure Cloud Billing budgets and threshold alerts Build a practical usage/cost dashboard in Google Cloud Monitoring/Billing Apply safe optimizations and document anything that should remain unchanged I’ll provide a concise report showing current spend → root causes → changes → estimated weekly savings, with the savings estimates based on actual billing/usage data rather than unsupported assumptions. I’m available to start immediately. If you provide billing and project access, I can begin with the cost audit and identify the biggest savings opportunities first.

@Geetam00

As an AI-focused Full-Stack Developer with extensive experience in Data Analysis, I am confident that I can help you optimize your Google Cloud costs. I understand the stress of skyrocketing bills and the importance of allocating resources efficiently. To solve this challenge, my approach would be to conduct a comprehensive audit, reviewing elements such as Gemini, Vertex AI, Cloud Run, and Compute Engine instances, deriving understanding from each spend category, and finding potential areas of optimization. Once I've identified these optimization areas, I will apply or recommend any necessary tweaks and right-sizing techniques specialized to each service. In addition, to streamline cost transparency, I propose introducing clear budget alerts and usage dashboards if they're not already in place. This will enable you to monitor your usage diligently and identify potential issues well before they become larger invoices. Drawing on my successful track-record optimizing costs for various clients as a part of my end-to-end product development work, you can expect a concise report by the end of the project summarizing all findings in detail including both implemented or recommended changes, alongside estimated weekly savings. Furthermore, the monitoring setup ready for view in Cloud Monitoring/Billing will be provided for your future usage.

@innoforgetech

Light traffic with a ₹3,000 weekly GCP bill usually means something is overprovisioned or idling, and the Gemini charges are probably driven by model tier and prompt size more than user count. Relevant experience: - No direct GCP cost audit in the public portfolio, but I've tuned production AI SaaS spend where LLM call volume and idle Cloud Run revisions drove the bill; the same audit flow works here. What I'll deliver: - Billing-export audit mapped to SKU, project, and label so you see Gemini, Vertex AI, Cloud Run, and Compute Engine separately. - Cloud Run right-sizing: min/max instances, CPU allocation, idle timeouts, concurrency, and container memory. - Gemini and Vertex AI tuning: model tier, token caps, cached prompts, retry/backoff, and disabling unused endpoints. - Compute Engine: schedule or shrink the small VM, and remove unused disks or static IPs. - Budget alerts and a Cloud Monitoring dashboard with weekly cost trend and forecast. Phase plan: Phase 1: Read billing export and map the top cost drivers by SKU. Phase 2: Apply safe changes first: idle resources and Cloud Run config, then model-level tuning. Phase 3: Verify the new spend trend and hand over the dashboard and a concise report. If you share a billing export or cost table and confirm which services are safe to change directly, I can confirm the top three cost drivers before touching anything. Best regards, Mohammad Juned

@chandanat48

Hi, I’m interested in helping with your Google Cloud cost audit and optimization project. I’m a fresher building my freelance career, with a strong technical foundation in cloud computing and hands-on experience working with cloud services and application development. I’m particularly interested in GCP, AI services, backend systems, and cloud optimization. For your project, I would approach it in a structured way: • Review the billing data to identify exactly where the weekly spend is going. • Analyze Cloud Run configuration, including instances, CPU/memory, concurrency and autoscaling. • Review Vertex AI/Gemini usage and look for unnecessary or inefficient usage patterns. • Check Compute Engine utilization and right-sizing opportunities. • Review network egress and other unexpected charges. • Set up or recommend appropriate budgets, billing alerts and monitoring dashboards. • Provide a concise report showing the findings, recommended/applied changes and estimated savings. I’m new to freelancing, but I’m serious about delivering carefully, communicating clearly and meeting deadlines. I’m also willing to start with a thorough audit and work through the highest-impact cost issues first. I can complete the work within 4 days, depending on the size and complexity of the environment. I’d be happy to discuss the current GCP setup and get started.

@josephexpert

As an exceedingly experienced Cloud Computing expert, I am well-versed in managing and optimizing costs on platforms like Google Cloud. I understand the frustrations that come with unpredictable billing and I take pride in my ability to transform daunting financial situations into ones that are predictable, effective, and yield maximized savings. My approach has always been first to conduct a thorough audit to gain a complete understanding of your current setup before making any adjustments. By factoring in the specific services you mentioned - Gemini, Vertex AI, Cloud Run and Compute Engine - together, we can develop a sustainable plan for right-sizing them to match your needs. Apart from my wide-ranging proficiency in programming, my forte extends to creating monitoring systems using tools like Cloud Monitoring/Billing. My aim is not just cost optimization but also empowering you with real-time insights needed to anticipate and avoid future invoice shocks. Lastly, but most crucially, I’m all about delivering concise reports which summarize findings, changes applied/recommended, estimated savings along with a user friendly monitoring setup. Working together, I believe we can not only mitigate your current cost issues but also lay the foundation for a more efficient and cost-effective Google Cloud operation. Let's optimize!

@Sajidkminds

I can help you analyze your Google Cloud spending and create a clear weekly cost-optimization report. I have hands-on experience with Google Cloud and cloud infrastructure, including services such as Cloud Run, IAM, monitoring and other GCP services. I will review the billing data, identify the major cost drivers, and explain where unnecessary or unexpected spending is occurring. My approach will include: • Reviewing GCP billing and usage patterns • Analyzing Cloud Run and other high-cost services • Checking Gemini/Vertex AI usage and related costs • Identifying unusual or unnecessary resource consumption • Suggesting practical cost-optimization actions • Preparing a concise weekly report with clear findings • Providing recommendations that can be monitored week over week I can communicate the findings in simple language and focus on actionable improvements rather than just providing raw billing data. I can start immediately and provide the first analysis within 2 days.

@ronakkataria1

Your real complaint is unpredictability, not the total. ₹3,000/week on light traffic is small money; a bill you can't forecast is the actual problem. Two causes cover most of it on this exact stack: Cloud Run min-instances. If that's above zero you pay for idle container time whether requests arrive or not, and on light traffic it's often the single biggest line. It hides well because it spreads evenly across the month. Gemini/Vertex per-token spend that doesn't scale with traffic. Retries, a health check hitting a model endpoint, an agent loop resending context each turn, or a prompt that quietly grew. That's usually where "unpredictable" comes from — the driver isn't request count. What I'd do: - Billing export to BigQuery, then break spend down by SKU and service. The console groups and rounds; the export doesn't. - Check min-instances, CPU-always-allocated and concurrency on Cloud Run; right-size or retire the Compute Engine box if it's idle. - Review model choice — Flash instead of Pro on calls that don't need Pro is often an immediate large cut. - Budget alerts plus a Cloud Monitoring dashboard, so a spike shows up in a day rather than on the invoice. One honest note on economics: at roughly ₹12,000/month this needs to find about 30% to pay for itself within two months. From your description I'd expect more, but the audit tells us that before anything gets changed. Ronak — AWS and Azure certified architect; cost work on data and AI workloads.

@punitkatnalli

I am working as a Devops engineer with overall 4+ years of experience i have required hands on to complete the requirements and prior experience on working on similar requirements in live projects.

@vinaykumar0401

I have worked on the AWS cloud cost optimization, now I can easily do it , where less amount and high quality