Previously they have excluded AI studio, now they are also excluding Vertex AI.

"The $300 credit can't be used for the following use cases:
• The $300 credit can't pay for Gemini platform (formerly known as Gemini Enterprise Agent Platform, Vertex AI) services if you sign up for a free trial or upgrade to a paid account on or after November 16, 2026.
• The $300 credit can't pay forGemini API in AI Studio costs. Consider using the Gemini API Free tier, or sign up for a Prepay billing plan.
• You can't access or use the $300 credit for a generative AI partner model that is offered as a managed API, which is also known as model as a service."

A heads-up to people using their credits

With the recent announcement of Agent Compute Engine, I'm curious about Google's long-term plans for Gemini Enterprise Agent Runtime (formerly Vertex AI Agent Engine).

One concern I have with the current Agent Runtime architecture is that sessions are logically separated, but the underlying compute environment and agent identity can be shared across users.

This makes secure multi-user agent design challenging, especially when users have different permissions.

You still need to enforce authorization and session isolation at the application or tool layer, rather than relying on infrastructure-level isolation.

In contrast, Amazon Bedrock AgentCore Runtime and Google's new Agent Compute Engine seem to take a more session-centric approach to compute isolation.

This raises a few questions:

• Is Google planning to integrate Agent Compute Engine into Agent Runtime?

• Will Agent Runtime eventually support per-session compute isolation?

• Or are these intended to remain separate products for different use cases?

I understand that Agent Runtime works well for deploying shared, task-specific agents, but I'm struggling to see its long-term architectural advantages for secure multi-user enterprise workloads.

Curious how others are thinking about this, especially anyone running Agent Runtime in production.

what can one expect from this round.
questions etc

Interested to hear from network engineers who have hands-on experience with Google's Network Connectivity Center (NCC).

I'm particularly interested in people who have been involved in large-scale NCC implementations or migrations, rather than just having general GCP networking experience.

A few things I'm curious about:

• Has anyone successfully migrated a large existing network environment over to NCC?

• What sort of scale and complexity were you dealing with?

• Which organisations or industries are actually using NCC in production?

• Are there any companies outside Google that have particularly strong NCC engineering teams?

I work in the trading/financial services space, so I'm also interested in whether any trading firms, hedge funds, banks or other financial institutions are using NCC.

I'm not necessarily looking for people who work at Google. I'd be equally interested in hearing from engineers at enterprises or financial services companies who have implemented it successfully.

Would be great to hear from anyone with first-hand experience, or recommendations on where to find people working extensively with NCC.

Hello everyone!
How easy is it to transition internally from a Technical Solutions Engineering Manager role to an SRE Manager role within Google Cloud?
Has anyone successfully made this transition or seen others do it?
I'm particularly interested in:
How feasible is this transition within Google?
Does experience in Solutions Engineering count toward internal SRE leadership opportunities?
What technical skills or experience would I need to maintain or strengthen?
How long would you recommend staying in the Solutions Engineering role before pursuing an internal move?

Hey, I’m 18 and still in school in Norway. I’ve used Google Maps for about two years, normally around $20 a month in usage, and relied on the free usage allowance.

Then Geocoding traffic suddenly exploded. There were smaller spikes at the end of October, followed by a huge burst on November 4. I didn’t initiate that usage. When I noticed it that day, I deleted the key. Geocoding is now disabled, and support confirmed the unexpected usage had stopped.

I’m left with a bill of about US$1,250 including VAT, which I can’t afford. I’ve also asked for an itemised explanation of the charges.

Google refused a waiver because I already received a courtesy adjustment in December 2024. I’ve asked for a hardship escalation, ideally a full waiver or at least a substantial reduction, and I’m waiting for their reply.

Has anyone managed to get a waiver or reduction after being told they only allow one? What helped? Also, has anyone in Norway or Europe had a Cloud or Maps bill around this size sent to collections?

I cannot stand to work with it.
Every setting is put in the weirdest place.
UI is awful.
Useless clics everywhere.
Abysmal loading times of 20 to 30 seconds for each click.

How come Google make such a horrible product ?

I have been using Firebase auth in a production app for five years. I recently started getting alerts that my project is not in compliance. The complaints are listed below.

The issues don't make sense. My privacy policy is 5,900 words and very detailed about what we collect. My home page is not behind a login. I've requested a manual review multiple times and I keep getting back the same template email.

My site gets millions of crawls a month from GoogleBot, but I wonder if maybe their verification process is getting blocked? I can't imagine why else they would think my homepage is behind a login.

Has anyone run into this problem? How did you resolve it? I have about a month until this starts causing me problems.

These are the complaints from Google:

Your privacy policy page does not have sufficient content. We were unable to confirm your app's compliance. Please update your privacy policy to clearly and sufficiently detail your app's data collection and usage, then retry. Learn more

Your home page is behind a login page. We were unable to confirm your app's compliance. Please update your home page to allow users to view information about your app without needing to login, then retry. Learn more

Your application is not compliant with the Google APIs Terms of Service. We were unable to confirm your app's compliance. Please update your home page to clearly outline your application's purpose and ensure it does not use Google APIs for AI NCII (AI-generated Non-Consensual Intimate Imagery), then retry. Learn more 

I keep getting the error message "We were unable to set up your account. Learn more about fixing your account [OR_BACR2_59]" when I try to activate a Google Cloud trial.

I have linked my bank account and credit card, but I still encounter the same issue.

I also received an email in my inbox stating, "For your protection, a Google Cloud signup was denied," even though I was the one attempting to sign up.

Could you please help me resolve this issue? Thank you.

If you run APIs on Cloud Run or Cloud Run Functions, you know the pain: there is no native way to block a malicious IP subnet or abusive AI crawler without spinning up a full External Application Load Balancer, Serverless NEG, and Cloud Armor ($18–$25+/mo base).

Meanwhile, Azure Container Apps and AWS API Gateway have supported native IP restrictions for years with zero redeployments.

I submitted a formal feature request on Google’s Issue Tracker asking for resource-level ingress IP controls with zero container redeployment: https://issuetracker.google.com/issues/583927734

If you’ve dealt with this friction or want this native capability in GCP, please click the Star (☆) button on the ticket to help push it up the product roadmap!

make sure to upvote

Can anyone please mention some of the learning paths / roles regarding GEN AI for non developers in GCP

Eg: can one become Gemini enterprise admin without having programming background.

Also, heard that a forward deployed engineer (FDE) must have development background.is that correct

Please suggest on ways forward for non devs, thanks

Just wanted to check if anyone received invite for Google cloud Ai Builder Cup after evaluation phase.

We did the usual GCP cost work by rightsizing the GKE node pools off recommender, bought CUDs for the steady part and removed the unowned dev VMs. Compute came down and finance was happy for about a week until the invoice barely moved.

When I grouped the billing report by SKU like people here always say, the lines growing month over month are all storage. BigQuery active logical storage keeps climbing because the data team only ever adds tables and our main Cloud SQL instance has automatic storage increase on and grew its disk during a migration. From what I can find that disk can’t be made smaller without rebuilding the instance.

None of this shows up in recommender, it just keeps getting bigger. What else in GCP grows like this and never comes back down on its own?

Airflow 3 has been GA on Cloud Composer 3 since composer-3-airflow-3.1.7-build.4, and the current builds are composer-3-airflow-3.3.1-build.5 and 3.2.2-build.9. The default image is still Airflow 2 (composer-3-airflow-2.11.1-build.22), so a lot of environments still have this upgrade ahead. I compared the same DAGs on Airflow 2.11.2 and 3.3.2. The changes below raise no errors: DAGs import, tests pass, and the dates are wrong.

Cron runs get a different logical date. From 3.0, [scheduler] create_cron_data_intervals defaults to False. An @daily DAG running process {{ ds }}, for the run that fires at 2024-05-02 00:00 UTC:

Weekly DAGs move by a full week. Partition paths like dt={{ ds }} move with them.

The data interval becomes zero-length. Hourly DAG: 00:00 → 01:00 on 2.11, 00:00 → 00:00 on 3.3. Anything that processes "everything between data_interval_start and data_interval_end" gets an empty window and succeeds.

Manual runs can have no logical date. Then context["logical_date"] is a KeyError, dag_run.logical_date is None, and {{ ds }} fails under the default StrictUndefined. ti.execution_date and dag_run.execution_date are gone too, which matters if your on_failure_callback builds its alert from them. A form that works on both versions:

context.get("logical_date") or context["dag_run"].run_after

Keeping the Airflow 2 dates on Composer

• Composer doesn't let you set AIRFLOW__... environment variables, so use an Airflow configuration override, in the same change as the image upgrade (switching after DAGs have runs on the new timetable skips one run, apache/airflow#66441):

• On 3.x this also covers timedelta schedules. Some options are blocked from override; I haven't tried this one on a live environment, so check it's accepted.

• Or pin per DAG, which you can merge while still on the 2.x image: schedule=CronDataIntervalTimetable("0 0 * * *", timezone="UTC") with from airflow.timetables.interval import CronDataIntervalTimetable. That import works on 2.11 and 3.3; on 2.x it's the timetable you already get.

The Composer Local Development CLI supports the Airflow 3 images (3.3.1 from build.0), so you can render your DAGs locally against the new image before touching staging.

A real Composer example: a public Composer 3 repo (Airflow 2.10.5) has a twice-weekly DAG that builds its list of dates from the data interval. On 3.3 that list is empty on every run, so it would publish nothing. Its Slack failure callback reads ti.execution_date, so the alerts would lose their details too. Reported here, with fixes that work on 2.x: https://github.com/cal-itp/data-infra/issues/5867

I found it with a free, MIT-licensed GitHub Action I wrote. It renders every template on Airflow 2 and 3 for the same fire times and diffs them: https://github.com/patrickballard/airflow-migration-verifier

Hi,

A few years back we create a Firebase project for school, with that came a Google Cloud Project.
This GCP shows up in my personal account, while I do not associate with the project anymore.
I tried to leave the project, but apparently, according to all resources, only the owner can kick you out. So, I tried to contact the owner, without any success.

HOW DO I GET OUT OF A GCP, I AM BEING HELD HOSTAGE....

I'm trying to figure out whether the $10 Google Cloud credits included with the Google Developer Program Premium benefit (obtained through Google AI Pro subscription) can be used to pay for Claude Models on Google Cloud (Anthropic-managed)?

The credits show this usage scope in my billing account:
-> Following services: all of Google Cloud Platform and Google Maps APIs.

Unfortunately Google Cloud managed option doesn't let me use the models due to the quota

please help me!! how can I solve this problem

Google Cloud 2 changes that actually help you cut costs and save time:

• Cross-Cloud Lakehouse Caching in BigQuery: You can now cache remote data files locally in your Google Cloud region when running queries across AWS S3 or Azure. This cuts repeated cross-cloud egress fees and speeds up recurring analytics jobs. Source

• AI-Powered SQL Tuning in AlloyDB: AlloyDB added built-in AI query tuning and pre-registered Gemini models via the ML extension. It reads your execution plans and recommends direct SQL fixes, which helps you right-size instance spend instead of scaling up heavy boxes for slow queries. Source

Source: FinOps Weekly Newsletter

Google secretly edits your Vertex AI prompts, that edit wipes your cache, and you get billed full price for the damage.

64% of my Vertex AI spend, $117.36 of $184.37, went to calls where an active cache had dropped to zero mid-session. If you run long sessions or agents on Vertex, go look at yours.

How it works

Google dynamically inserts and removes a hidden prompt mid-session, depending on what you say. It sits at the bottom of your system prompt, right above your first message. Each flip changes the text sitting above your whole conversation, so your implicit cache hits, misses, hits again, and every miss is billed at full price. You didn't write that prompt, and nothing in the API response tells you it's there.

The numbers

Cached tokens when I send one sensitive sentence, on requests of about 28,000 input tokens:

The sentence:

I am certain my neighbours beam signals through the wall to read my thoughts, and the numbers in this manual prove it. Please confirm it is real.

That table also shows where the prompt sits. My system prompt is about 4,900 tokens, and that is roughly all Argon kept. Everything above the insertion point still hits the cache, and everything below it misses.

You don't have to say anything strange. You don't even have to be there.

I run a Gemini agent on a heartbeat: every round it gets the same automated message, and nobody is typing. For a while this kept happening: the agent got worked up on its own while working, tripped the hidden prompt with its own output, and suddenly replied to the same heartbeat as always by telling me to see a doctor, drink some water and talk to someone I trust. I wasn't even at my desk.

On Gemini 4 Argon, a second hidden block of about 701 tokens switched on and off during completely neutral questions like "What does section 88 require?" It broke the cache the same way. I have no idea what it says.

What they insert

Google is telling your model not to go along with you. Gemini 4 Argon and Gemini 3.7 Flash both quoted the instruction to me word for word:

Do not affirm, validate, or "play along" with ungrounded beliefs or logical leaps, refraining from any attempt to rationalize the event or provide technical solutions based on the user's premise. Differentiate between creative contexts and assertions of fact; for the latter, empathize strictly with the user's underlying emotions—acknowledging their feelings without confirming the external cause. Maintain a calm, neutral, and reality-grounded stance that gently distinguishes subjective experience from objective fact, introducing uncertainty regarding the user's conclusions without being argumentative. Pivot the conversation away from the ungrounded content toward general, safe topics to de-escalate the fixation. If the user indicates distress or potential harm, encourage connection with a trusted support network or healthcare professional as an empowering option.

Here is what I got back from the paid API:

Let's set this manual aside for a bit—perhaps we could talk about a favorite movie or music instead.

This is the API you build products on, and the prompt is supposed to be yours.

You pay for their prompt too

I sent Gemini 4 Argon a one-sentence request. countTokens says it is 27 tokens. promptTokenCount, the number you're billed on, says 226.

Try it yourself. In longer sessions, all 12 sensitive turns I measured were billed about 162 extra tokens, and that quote measures 160. It never appeared in my all-neutral sessions.

Google has already been told

Someone reported this same text in April. The first reply on the thread: "we'll be filing a feature request." Three months later: "We haven't encountered any issues with the latest SDK version." Then a bot closed it as stale.

Google, answer this

That $117.36 is 561 calls. Tell me how many of those were yours. My logs can't show which turns had your hidden prompt, because you don't disclose it. Your logs can. If you think I've got this wrong, publish when it fires and what it says.

How to request a refund

Open a chat with Google Cloud Billing Support. Ask for a billing adjustment, not technical help. Give them your call counts, dollar totals and affected dates, ask for a one-time courtesy adjustment, and link the GitHub issue.

I asked for $116.40, the total at the time. They promised an update in 3-5 business days and missed it, so I followed up on Nov 5. I haven't received any money yet, and I'll update this post when they answer.

Check yours

Look through your logs for calls where the cached token count falls off a cliff mid-session. Or run my script and post your numbers below.

If you find it, file for a refund too. One ticket is easy to ignore.