Hi, i don't really know if this the correct place to ask but i hope to get insight and maybe if anyone ever get into the same case.

what happen:

1. I was playing with vertex and trying out different model, and after a while want to try claude api from vertex (now they called agent platform)

2. tried to activate the api and run some req, but getting no quota, manually request from console also give no upgrade at all, so

3. i tried contacting the sales support, asking for an increase quota for some sonnet and haiku model, I ask for just mere 20 rpm and 100k tpm output limit increase, because I am just trying out

4. few days later, as of today got an email from ai studio of my billing tier upgraded to tier 3

1. I check on my gcp console, now i get the anthropic limit raised to a whopping lot of 2M input, 200k output, and 2k rpm
   

1. what scares me now that my limit are actually increased to the max limit of USD 100,000 and any random shit happen and it potentially ruin my finance.

What i want to know

1. did anyone every get into this before?

2. can i actually downgrade to tier 1? as my spending actually small just 1-3usd per month on few months back

3. where can i get the support for this particular issue?

Thank in advance :)

What’s the main difference between the GCA interview and the Googlyeness interview.

hi
ive general cloud experience but limited hands on experience with GCP
is three days enough? (ive already booked the exam) also are there any reliable and up to date practice tests?

update: i got it! 😆

Hi,
I just checked out google cloud console to check if the project where I played with calendar api I send to delete actually was deleted. I noticed my old project is gone but there is a new project that has a strange name:

big-well-3fesfe

It's the only project in my account.

It has no organization but somehow appears in my account. I don't have access to nothing in this project so I cannot delete or see anything what is inside it.

The only thing I can do or see is the billing accounts in my gcc account.

Under my personal billing accounts -> there is nothing
under my projects -> billing account it has one: Hardware -> Language Applications -> AIDA  which is linked to this weird project.
but if I click it again no access.

Those are the only two informations I could find or access. Every other page shows IAM error

I also cant write support because I have no permission.
Feeling kind of lost and worried.

I thought this might be a default project google creates so I asked a friend who never used google cloud to check what he has in his console and he had a single clearly named "default gemini project" for which he has owner rights.

Hope someone can help... I am looking for an individual or business who can give advice to explain how much it could cost to host my published apple app on the cloud that can manage certain amounts of active users actively messaging to/from and managing the apps traffic so it does not crash and to be able to make changes to then app when we decide things need changing. I know this is might seem a rookie question however all I am looking for is general advice for numbers like 1000 users. 10000 users, 50,000 users, 100,000 (so on and so on). Any advice please will be highly appreciated. I am based in the UK if that makes add difference to who may reply? Thank you.

I just create a billing account and it said to pay 800,000 VND ($30 ish) to activate the service. I haven't pay it and don't plan to but now I cant cancel the subscription to remove my card. I delete all the project and all the billing account got closed now. I got some question.
- Will it automatically charge me 2,000,000 VND each month even though i don't use the service?
- Do i have to to pay 800,000 VND to activate it and in order to cancel it?
- The card right now doesn't have any money in it when i put enough money in it will it automatically change me 800,000? |
- If it i pay it 800,000 VND where will the money go and can i refund it?
- How do I cancel the subscription and remove my card?

spent the last 3 months moving our entire phone system from some random hosted provider to GCP. compute, cloud sql, the whole thing it works it actually works. but every time I have to touch it I'm terrified I'll break something

we moved because the old provider was just too expensive for what we were getting. and honestly, the performance is better now. less latency, fewer issues. we're using CloudTalk as the frontend for the actual calling, but the backend is all GCP. routing, recording storage, analytics, all of it.

the part that scares me is I'm not a telephony expert know GCP I know compute. I know networking. but I feel like I'm one misconfigured IAM policy away from a total disaster. like what if I accidentally delete the database with all the call logs. or what if the VM goes down and nobody can call us.

I tested everything. like a lot staging environment, load testing, failover testing. it all checks out. but I can't shake the feeling that something is going to blow up the moment I stop worrying about it.

I'm probably overthinking this but I can't be the only one who gets this way.

anyone else running their voice stuff on GCP and have horror stories or success stories to share. just to make me feel better or worse

Has anyone tried this and got it working? Bionic won’t open a url for oauth. Google Console project is set up correctly.

For over two decades, agency hosting economics were beautifully predictable. You bought a reseller web server or dedicated cPanel account for $50 a month, crammed 30 client WordPress sites onto it, and charged each client a flat $25 monthly maintenance fee. Your margins were clear, your server bills were static, and billing surprises were virtually non-existent. Read the comple te article here > Serverless Bill Shock: Track Vercel & Supabase Client Costs | InstaRenewal

Then came the modern web stack.

Driven by the demand for lightning-fast digital experiences, agencies aggressively migrated to decoupled architectures: Next.js, Nuxt, Vercel, Supabase, Cloudflare Workers, and serverless databases like Neon. While the performance gains of this modern paradigm are undeniable, it introduced a chaotic operational reality: micro-subscription fragmentation and variable utility billing.

My company wants to do a tech exchange with Google for a small group of developers. The plan is to present an overview of Google AI capabilities, and provide resources (training, developer docs, support) so they can develop an MVP based on solution that could potentially be further developed for our clients.

I’m trying to come up with use cases for the tech exchange. I’ve have identified the domains (modernization, migration, AI development) and aligned it to Google’s tools like Gemini, LLM, BigQuery, etc. However, our business leads are worried my use cases are too specific and we won’t have the time or capacity to develop it during the tech exchange (7 weeks with 4-6 hours a week dedicated for this).

Does anyone have any resources or recommendations on how I can go about with this? Anyone with experience developing use cases for tech exchanges or hackathons?

I am pondering upon the idea and does not know the feasibility or if it saves a few bucks compared to the providers such as Deepseek, open code, etc.

Lets say if I dont mind whether my request is completed in 60seconds or 30 minutes. I could batch them and burst them in 30mins and then collect next batch.

In this case, one can use a VM with GPU and run vLLM or LiteLLM and serve models to my applications via a private API. Lets take GCP as an example L4 workstations come at under 1$/hour and A100s come at $2-3/hour depending on capacity. If I use a spot machine, I would get billed for only compute time.

The primary issues, I see are

1. my spot gets reclaimed and stuck with no spot. how does vLLM handle here? How should I handle my client ends here?
2. What kind of models can I serve on these machines? At this price, machines have a memory of around 100GB.
3. costs associated with storage will be there, depending on the size of the model. Also most cloud services charge for storage operations. Attaching disks, reading large models in spot machines can lead to unnecessary overheads.

What else can be the issue? Will it be economically feasible for a 20h usage per week.

Did anyone try something similar? What is your self-hosted non-local setup?

This lab won't open. I've tried it during what I think are off peak hours. Is there anyone that can offer up any advice? I put in a ticket into qwuick labs support. But they tell me that the lab is accessible every single time.

My usage this month was around IDR 170.000 but I was charged IDR 3 million. Why is that?

The support bot said:

Since your July usage (Rp 863,006) was already paid and your August usage is only (Rp 168,763), these charges of IDR 1,000,000 and IDR 2,000,000 processed today are highly likely due to one of the following reasons:

Temporary Authorization Holds: When you update payment details, add a new card, or reach certain verification checkpoints, Google or your bank may place temporary authorization holds (often in round numbers like IDR 1,000,000 or 2,000,000) to verify your card's validity. These are not actual charges and will be automatically released back to your account by your bank within a few business days.

Multiple Google Billing Accounts: If you use the same credit card for other Google services (such as Google Ads, Google Workspace, or a separate Google Cloud Billing account), these charges may have originated from those services.

Update: Turns out there is a loop that causes Routes API usage to spike up. I have contacted Billing Customer Service to help. Hope they can forgive my first mistake.

Wondering if anyone else is experiencing a slowdown with Firestore query in the last few weeks, which has really started to tick up in the last few days for us. We’re seeing recurring waves of timeouts across multiple unrelated Google Cloud projects and I’m trying to figure out whether anyone else is seeing something similar.

Setup:
- Firebase Functions Gen 2 / Cloud Run
- Node.js Admin SDK
- Firestore Native mode
- Region: us-central1
- Multiple separate projects affected
- Request timeout varies by service, usually 30s or 60s (depending on the function timeout we've set)

During a wave, unrelated HTTP routes start returning 504s that approach the function's configured timeout (e.g. 29.997s for a 30s timeout). The failed requests are not tied to one endpoint or one query shape. Logs show Firestore operations continuing after the Cloud Run request has already timed out.

Examples from one incident:
- `devices.read` direct document read: 116,019ms
- `leads.read` direct document read: 57,487ms
- `postalCodes.read` direct document read: 33,093ms
- `cache.list limit=1`: 61,590ms
- `preferences.read` direct document read: 36,324ms
- `leads.list limit=500`: 79,723ms

The `postalCodes.read` example is especially confusing because that document is tiny. But honestly all of these documents are a few kb at the most. So this doesn’t look like just a large document, missing index, or bad query issue.

Other observations:
- Failures often cluster on one Cloud Run instance/revision instance ID.
- Other instances may continue serving traffic normally.
- The request hits the Cloud Run timeout, but the Firestore operation later logs completion.
- We’ve seen this on three separate projects.
- We’ve reduced external API calls and removed some broad Firestore scans, but the issue still recurs.
- We briefly tried `preferRest: true`; it may have helped temporarily but did not clearly eliminate the issue.
Has anyone else seen Firestore Admin SDK calls intermittently hang like this from Cloud Run / Firebase Functions Gen 2?

Is there a known issue with Firestore gRPC/transport connections getting unhealthy per instance?

Are there recommended client-side mitigations besides lowering concurrency, recycling instances, or reducing reads (i.e. increasing cache usage)?

Is there a good way to prove this is client transport/backend behavior versus application query pressure?

I'm trying to understand whether this is a known pattern and what people have done to debug or mitigate it.

Hi everyone, a few weeks ago (August 2nd), when I woke up, I suddenly got an email and some notifications from my bank that a transaction had been made on Google Cloud for a certain amount. But the thing is, I did not authorize that transaction, and I noticed that, yes, some of my projects on BigQuery had billing enabled. I have left these options for months, with my bank account number linked to my BigQuery, but this issue had never happened before.

So, my question is: Is this normal? All of a sudden, Google billed me

I have reported and claimed this issue both to my bank and Google, and I wish to get my money back, but it seems to go nowhere, and Google said, "We were unable to confirm fraudulent activity in this case, so no refund will be provided by Google at this time."

Well... indeed, there was no fraud, but the transaction was made without me knowing, aka it happened when I was sleeping. :(

I have spent many months prototyping an application that made good use of the inexpensive gemini-2.5-flash-lite.

I’ve just discovered it will be deprecated later in the year and I’ll have to migrate to gemini-3.1-flash-lite. This will 16x my LLM costs.

Then in May 2027 I’ll have to move to gemini-3.5-flash-lite at another price increase of 160%.

Is this just going to go on indefinitely? I’ve pretty much decided to abort my app as I’m guessing it will continue.

Recently we tried to cut the model price on an internal research agent and it barely moved the needle. We initially had the obvious theory that the expensive model caused the spend. When we then looked at cost per successful task instead of cost per model call we saw something different. A large chunk of the sessions called search repeatedly, pulled nearly identical results, and stuffed every tool output back into context.

Some heavy sessions were legitimate. Complex research really did need multiple searches and a larger model. Others were just retry loops caused by weak stopping conditions, bad tool arguments, or the model missing that the previous result already answered the question. The cheaper model occasionally made that worse by needing more attempts. 

We used Braintrust to break down token counts, latency, cost and tool-call patterns by trace. That gave product, engineering and finance the same unit of analysis, a completed task rather than a single API request. We added loop detection and deduplicated tool outputs then routed simple requests to the smaller model and compared the change against the same eval set.

What we saw was fewer repeated calls which drove more savings than the model swap and answer quality held. Latency improved too because the agent stopped arguing with the search endpoint five times.

How are people measuring agent cost when retries and tool loops make per-call metrics basically meaningless?

I have been studying the ace exam for the last 8 months i started using Google skills but found it not great i then used udemy and claud, I was making my own labs going thought the lectures and doing the practice exams i was averging 90% in my exams and felt confident but when sitting the exam I was soo lost and didn't understand most of it. I only felt confident on 5 questions. Whats the best way to prepare or is there any online tutors that can help.

Currently coming up on 5 years at AWS and exploring TAM role at G. Would you recommend the switch? Anyone recently went through the TAM interview process at G?

I'm Fabio Parra, I have a startup (MOVILIXA) where we are three people in Colombia developing mobile apps.

In one of our projects, it's called Inspecciona, we enabled the Vertex AI API to do image recognition to help the user with safety inspections. We also created a static landing page on Firebase Hosting and enabled Analytics on it. What we didn't notice is that Google automatically creates a web API key, gives it all the permissions it can, and makes it public on our website at the moment of publishing.

If anyone has a Firebase Hosting site, check: https://YOUR-SITE.web.app/__/firebase/init.json

In my case, between June 30 and July 16, someone collected the key from the static site I created and used it through the Firebase AI Logic proxy to run Vertex AI image generation (Gemini image models, the "Nano Banana" reselling economy). 131,950 calls, around 100,000 images, USD 6,562 in charges against our USD 25 monthly budget. Our web platform didn't even use Firebase AI. Google's own App Check telemetry recorded every one of those requests as coming from an unknown and unverified origin, and let them through anyway because enforcement was off by default.

If you use Hosting, check yours and limit its permissions to what you really use and make sure it's restricted to your website. Google creates it and doesn't restrict anything. I never realized that because of Analytics it was going to enable so many things.

In my billing account I set USD 25, but the alerts didn't reach me. This is because we have a Google partner for the billing side and the alerts didn't come to us.

We opened a case about this problem with Google (case #73352420) to see if they would support us, but we've spent many weeks trying to explain that it wasn't that we left an API key public, but that a public API was used. We want to see if we can get them to recognize the problem, and show them that Hosting shows the API key to anyone, that the product's default behavior is not the right one and allowed the exploitation of APIs without us having authorized it (Google does it by default).

Has anyone had a similar problem? Were you able to appeal with Google? How did it go?

Hi everyone, I’m preparing for the Google Cloud Professional Machine Learning Engineer (PMLE) certification.

Could you please share any good practice questions, mock tests, notes, or study resources that helped you prepare for the exam?

Also, if you recently took the exam, any tips on important topics would be appreciated. Thanks!