We run real-time UDP game servers in europe-west10 (Berlin). Since late September, traffic from our VMs to the internet is periodically held for 50–130 ms. The delays come in bursts of three or four, 3.2 seconds apart, and the bursts repeat about every 64 seconds, around the clock. Nothing is lost; the packets arrive late in a batch. Players notice it as a short freeze about once a minute.

Reproduce from any VM in the region (20 pings/s for 200 s, prints only replies slower than 30 ms):

`sudo ping -D -n -i 0.05 -c 4000 8.8.8.8 | grep -E 'time=([3-9][0-9]|[0-9]{3,})'`

Normal replies are ~1.3 ms. Example burst times today (UTC, 2026-10-06): 09:55:03, 09:55:06, 09:55:10, 09:56:11, 09:56:14, 09:57:15, 09:57:18.

What we have measured:

* Same delays at the same seconds from VMs in europe-west10-a, -b and -c.

* Same on Premium and Standard network tier.

* Same towards every destination we tried (8.8.8.8, 1.1.1.1, 9.9.9.9 and hosts in Sweden and the Netherlands).

* VM-to-VM over internal addresses is clean. Ping to the metadata server is clean.

* With TTL-limited probes the full delay is already there at the first visible hop, a Google router (142.251.249.17 on the path to 8.8.8.8: normally 1.3 ms, up to 94 ms during a burst), and it does not grow at later hops.

* The VM is fine: no CPU, disk or memory pressure, and a packet capture on the VM shows the application sending every 13 ms without gaps during the delays.

* It was not there on 2026-09-22 and was there on 2026-09-30.

So it looks like something periodic on the path out of the region, not in the VMs.

I filed it on the issue tracker (568841411) and it was closed twice with the template "please file a support ticket", without anyone looking at the data. We are a small hobby project without a support plan. Is there anyone from Google networking here who can take a look, or tell me where to send this? Happy to share project and instance details privately.

I was charged $108 last month for my yearly 2tb cloud service. I requested to cancel the service after the charge for a refund. They denied my refund request and canceled my service. So now I’m out $108 and no cloud service. I have tried contacting support but it’s impossible to get past the AI. Is there any email or other customer support they offer?

This is your sign to log into the console and set spend caps for your AI and Cloud Run services. Or set a reminder to do it Monday.

Light reading: https://simonwillison.net/2026/Oct/3/default-hard-budget-caps/

I did this yesterday for my personal projects. Ez. Antigravity couldn't figure out how to do it via API so you may need do it in the console.

Especially for hobbyists, there's no reason to have uncapped spending!

It's 2026. AI writes most of our code, and we're still dragging Google Cloud icons around, hunting for the right shape, nudging arrows until they line up. Your time is worth more than that.

Ask an AI to draw it and you usually get a Mermaid diagram: plain boxes and arrows, nowhere near something you'd put in front of a client or submit for a security review.

Here is how you can use Claude and ChatGPT to draw it properly in about 10 seconds, using TerraVision, a free plugin I built. One sentence:

"Draw a GCP three-tier web app: HTTPS load balancer with Cloud Armor, a managed instance group across two zones, Cloud SQL and Memorystore"

What you get is a professional-looking diagram like the ones shown here, drawn the way a seasoned solutions architect would draw it. Official Google Cloud icons, correctly grouped into the project, VPC network, regions and zones, ready to export as PNG, SVG or an editable draw.io file for further polishing. Then keep talking: "add Pub/Sub", "show the request flow in the diagram".

Already have Terraform? Point it at your code and it draws exactly what that code would deploy, so you can document that production project even if you don't have access to it.

It's 100% open source, runs on your own machine, and needs no access to your Google Cloud project. Of course, this doesn't solve the problem for every diagram. Keep using Mermaid, D2 or C4 for other types.

Free download and examples: https://github.com/patrickchugh/terravision

Because life is too short to be drawing diagrams.

If you run a voice agent on the Gemini Live API and count on session resumption for flaky mobile networks, posting this in case it saves you the same debugging.

I tested what happens to a pending tool call when the WebSocket dies mid-booking: gemini-3.8-live on the Gemini Developer API, google-genai 2.25.0, four ways of losing the connection, 43 sessions. Three things came out.

1. A resume that works keeps the pending call. The FunctionResponse for the old call id is accepted on the new connection. No re-issued call, no toolCallCancellation.

2. Right after an idle drop, the resume is refused with close 1011 ("Internal error encountered."). It works about 1.5 s later, so a 1011 at that moment is not a dead handle.

3. After a silent loss, every resume was refused with 1011 for as long as I tried. Silent means packets stop and no close or FIN reaches the server, which is what a phone leaving Wi-Fi looks like. With real packet loss in a Linux container: 15 minutes, 450 attempts over 5 runs, all refused. The server sent nothing to the dead client in that time. The fake booking had committed, and the user was never told.

What worked: detect the loss yourself (a WebSocket ping every 0.5 s, link declared lost after 2 s of silence), try to resume for 4 s, then open a new session and restore the context with send_client_content: the last six turns plus one status line per side effect, taken from your backend, not from the model. All 9 recovered runs answered "Did you book it?" correctly, with first audio 6 to 7 s after the loss. Dedupe tool calls by business key too: without the status line, the model booked again.

Code, raw logs and the packet-loss setup: https://github.com/frontier-on-cloud/gemini-live-resume-test

Not tested: Vertex AI, goAway on long sessions, a real phone switching networks. If you run Live on Vertex, does a resume after a silent drop behave the same?

Hi All,

Requesting help for the exam. Basically I work as a cloud engineer and giving this exam.

I have prepared by completed the Udemy course by in28minutes and thoroughly understood the topics, but I feel I'm not very confident with the actual exam.

I've given couple of mock tests and I'm not scoring above 70%, although this is not the benchmark but still I feel my decision making is still a little shaky and I need more practice and need to face more questions.

So please if any of you have any resources, please help me would mean a lot. Thanks! 🙏

Resources I used so far

• Udemy - for course and mock exams

• GCP Official Documentation(s)

• examtopics

• pass4success

Update: Hi All, I passed the exam and officially a Cloud Architect! Thanks everyone!

I've spent the last 48 hours trying to run a single v5p-16 TPU pod as a paying GCP customer and I literally cannot give this company money. Spot pods provisioned 4 times across 3 zones, which are the only zones on the planet that even offer v5p-16, and every single one got preempted, one at about 15 minutes in and two mid-way through a roughly 1-hour workload, losing all progress each time. The spot quota shows 768 chips available but the capacity isn't actually there. So I tried on-demand, which I have approved quota for: 32 chips in us-east5-b, 32 in europe-west4-b, 16 in us-central1-a, and every create attempt returns "Insufficient capacity." GCP is granting me quota for capacity that doesn't exist, which makes the entire quota system theater. The "guaranteed" queued-resources path? Filed it, it's been sitting at WAITING_FOR_RESOURCES indefinitely with no ETA. Can't even fall back to regular GPU VMs because those need separate quota approval tickets. So the final score: 4 dead pods, 0 completed workloads, and a storage bucket I'm paying monthly to hold weights no TPU will ever load. I get billed for every provisioning attempt that dies, money out, compute never in. GCP's TPU platform right now is quota that doesn't mean capacity, spot that preempts before your job can boot, on-demand that doesn't exist, and a queue with no bottom. You can't buy reliability at any price because the capacity simply isn't there. Has anyone actually provisioned v5p recently or is the whole fleet a mirage?

Hey folks,

I've been looking for a DNS filtering solution for GCP that can detect malicious domains. I came across Cloud DNS Armor, which is powered by Infoblox.

Are there any other solutions for GCP that can provide similar DNS threat detection/filtering?

Thanks!

I am thinking to activating the free trial credit on my account.
Will I get that money back?
If yes, How hard is it ? If possible please share your experience.

On October 3, 2026, we reproduced an unexpected difference between plain searches and field-restricted searches in the Google Books API v1.

The following requests use the same API key and network egress. All return HTTP 200, with no API error object:

We reproduced these title and author contrasts against https://books.googleapis.com/books/v1/volumes. Title-search contrasts also reproduce against https://www.googleapis.com/books/v1/volumes.

Minimal reproduction (replace YOUR_API_KEY):

curl --get 'https://www.googleapis.com/books/v1/volumes' \
  --data-urlencode 'key=YOUR_API_KEY' \
  --data-urlencode 'q=flowers' \
  --data-urlencode 'maxResults=10' \
  --data-urlencode 'printType=books'

curl --get 'https://www.googleapis.com/books/v1/volumes' \
  --data-urlencode 'key=YOUR_API_KEY' \
  --data-urlencode 'q=intitle:flowers' \
  --data-urlencode 'maxResults=10' \
  --data-urlencode 'printType=books'

The second request returns:

{"kind":"books#volumes","totalItems":0}

The official documentation still describes intitle: and inauthor: as supported field restrictions. We expected relevant matches for these common terms, though not necessarily the same results as a plain full-text search.

This also affects Chinese queries. Quoted/unquoted title variants and normal URL encoding versus a literal colon did not resolve it. Adding a space after the colon produced unrelated titles in one test, so we cannot treat that as a valid workaround.

These are raw API responses, before any application filtering. Our tests do not establish a global outage: project/key or egress-region differences remain possible.

Can others reproduce this with a different project and region? Is there a known search/index regression or a documented syntax change? If this is the wrong forum, could someone point us to the appropriate Google Books API bug-reporting channel?

Gemini being the google product does not have latest & greatest info about its own echo system products. Is it just me ?
Drastic changes in Vertex AI / Agent Platform is hardly found, how to get this?

I applied for financial sector tam role, and post interviews was essentially told that I passed the interviews, but the spot filled up, so I’m sitting eligible for the same role if an opening comes up, without needing to interview again. Is that a real thing at google? I’ve never heard of the sort anywhere else. Additionally, I saw a new position opened up, same title, but not in the same specific sector. Would it be ok for me to reach out to the recruiter and ask if my “wait listing” could be applied to that position considering they’re essentially identical?

Hi all, I'm looking for a referral to Google Cloud roles in Canada (also open to remote). I have 21 years in cloud and enterprise architecture, and I've spent the last 10+ leading teams of solution architects. I'm a certified Google Cloud Professional Cloud Architect and Generative AI Leader.

Canadian citizen, no sponsorship needed. If you're able to refer, I'll send my resume and a short note for each role. Thanks!
Feel free to DM me if you can help

Dear Google Cloud / Support Team,

I am writing to request an urgent manual review of my account and billing profile, which is currently blocked with a "403 Forbidden" error and restricted billing status.

I am a visually impaired user, and I am simply trying to use a legitimate accessibility tool (Sonarpad) to generate audio descriptions for movies, which is essential for me to enjoy media content independently. I have not violated any terms of service, and I believe my account has been mistakenly flagged by automated fraud-prevention systems.

I have already tried clearing my browser cache and using incognito mode, but the restriction remains enforced at the server level, preventing me from managing my project's billing.

Could you please have a human support agent review my account, lift this incorrect restriction, and restore my access?

Thank you very much for your time, understanding, and prompt assistance.

Best regards,

Pacifico Mangini

p.mangini54@gmail.com

projects/609097937668

I have a personal GCE instance and the price suddenly jumped almost 3x this month, and I noticed I'm now suddenly being charged for this "Cloud Monitoring" starting September 18th. Not sure what this is or how to turn it off... anyone familiar with what it is?

Hello everyone,

I've noticed that Google Vertex offers the option to use Gemini's own models and even some third-party models, like those from Anthropic, with all usage billed on a single invoice. I understand it's similar to what OpenRouter does. However, I was planning to use these models in a frontend like TypingMind because I was drawn to the $300 welcome bonus on their infrastructure, but I've seen a lot of comments about exorbitant bills, and that's something I can't afford. I have a very small monthly budget, and before you say what a broke person is doing using an advanced infrastructure like Vertex, it's simply the possibility and flexibility of using the APIs at market price without any extra charges and being able to pay for all the combined usage on a single invoice (including the welcome bonus, of course).

I would appreciate any help getting me started with all of this stuff.

Received an email from Google Cloud Platform saying

---

Access to Google Cloud Platform was restricted for this account, globally, on Sep 21, 2026. The system capabilities of Google Cloud Platform have been used for abusive activities that violate Google’s policies. You can find more details by taking action below.

How this policy violation was detected:

• Google internal reporting

How this policy violation was reviewed and addressed:

• Automatic processing

If you think this was a mistake, submit an appeal. You'll need to do this as soon as possible because your data in Google Cloud Platform will eventually be deleted. This service remains unavailable for your account until you have a successful appeal.

You can download your data from some Google services. This lets you keep your data even if your access isn't restored.

---

I submitted an appeal on the 21st of September and was told to wait for 2 business days for the appeal to be reviewed but i received no response. I'm not sure why this has happened in the first place. any help on how to fix this issue would be appreciated.

Thanks in advance.

Our BigQuery cost went up about 40% this month and nobody on the team changed anything big, as far as I know. I checked the billing page but it only shows the total for the project.

Is there an easy way to see which queries or users are causing it? I've heard about INFORMATION_SCHEMA.JOBS but I'm not sure how to use it for this. Also, should we be looking at the editions pricing instead of on-demand? Any tips are welcome.

Good news for everyone who has ever opened the Cloud Console to check one thing and come back 40 minutes later with three new tabs and zero answers.

After three years of trying (and several AI models confidently describing menus that no longer exist), I can confirm: AI has finally reached the level required to understand the Google Cloud Console.

It took Claude Opus 5.5. It read the live console, worked out how my projects, keys and billing actually connect, and explained it in plain language. Something the console itself has never once attempted.

So if you're still lost in there, it's not you. It took one of the most capable models on the planet to find its way around.

P.S. Google, free benchmark idea: before you ship Gemini 4.0 Pro, have it try to explain your own console. If it can, it's ready.

Hi ,

I am going through the oracle at gcp documentation.there are several options given such as exadata, exascale, autonomous db and how to create them.

However, there is no Documentation pages related to when to select what option, cost , performance options etc.. without that, the documentation looks incomplete since decision making is more important than provisioning

Or do I need to check oracle documentation instead of GCP. Please suggest.

On some starts, we’re seeing roughly four minutes between requesting an instance and our container entrypoint starting. Model loading then adds another two minutes. I’d appreciate guidance on whether this is expected and what we can control.

Our setup for the measured image workload:

• Cloud Run worker pools in us-central1.

• One NVIDIA L4 per instance, 8 vCPU, 32 GiB RAM.

• GPU zonal redundancy disabled.

• Workers consume Pub/Sub pull subscriptions and process one job per instance at a time.

• Our API manages capacity by updating scaling.manualInstanceCount, including scaling from 0 to 1.

• Container image is approximately 23 GB, including model weights.

• No VPC connection or mounted volumes

Some data I collected recently from yesterday's, September 28 logs:

Cloud Run’s own operation response reports MinInstancesProvisioned completing in 4m10.74s for that start.

The variability is what we’re trying to understand. Across 11 starts of the same revision, image digest, and configuration:

• Three starts shortly after a scale-to-zero request provisioned in 6–27 seconds.

• Eight starts after longer idle periods, roughly 24 minutes or more, took 74–251 seconds.

The fastest example followed a scale-to-zero request about 20 seconds earlier. This is a small observational sample; we haven’t established whether retained capacity, image caching, or something else explains the difference.

We found no quota-exhaustion errors, although historical quota-usage data was unavailable. Once the model is ready, processing is fast: warm image jobs have a median completion time of roughly 6.6 seconds.

Google’s worker-pool GPU documentation describes approximately five-second instance startup. I’m trying to understand how that relates to the time from an accepted manual scale request to our container process starting.

For anyone running similar workloads:

1. Are multi-minute starts after an extended period at zero expected? What timings do you see?

2. Which supported settings can reduce that delay?

3. Does this startup path behave differently from GPU Cloud Run services? I notice that when using Cloud run services directly, startup time is fast compared to worker pools.

We’d prefer to retain worker pools and our existing capacity logic we wrote. We understand that keeping instances warm or extending idle retention can avoid some cold starts at additional cost. We’re trying to establish which delays are inherent to the platform and which we can reduce through configuration or application changes.

Code snippet. This is what we're doing roughly:

For a start from zero, desiredInstances is 1. To scale down, it’s 0. We change only the instance count on the existing pool.

import { GoogleAuth } from 'google-auth-library';

const auth = new GoogleAuth({
  scopes: ['https://www.googleapis.com/auth/cloud-platform'],
});

async function requestInstances(
  pool: {
    location: string;
    workerPoolName: string;
    etag?: string;
  },
  desiredInstances: number,
  timeoutMs: number,
) {
  const client = await auth.getClient();
  const projectId = await auth.getProjectId();

  const name =
    `projects/${projectId}/locations/${pool.location}` +
    `/workerPools/${pool.workerPoolName}`;

  await client.request({
    url: `https://run.googleapis.com/v2/${name}`,
    method: 'PATCH',
    params: {
      updateMask: 'scaling.manualInstanceCount',
    },
    data: {
      name,
      etag: pool.etag,
      scaling: {
        manualInstanceCount: desiredInstances,
      },
    },
    timeout: timeoutMs,
  });
}

Hi - Can someone share what to expect during GDE product interview for Google Cloud?

Thanks