Need Support Understanding Vertex AI Costs & Safer Options for Small-Scale Educational App

General
1

Hi Google Cloud team and community,

I’m reaching out as a solo founder and creator of a project called AggroFish Analyzer, a bilingual (English/Spanish) poker training platform designed to help recreational and over-40 players understand the game better using AI insights.

:brain: Project Context:

I’ve built two Firebase projects:

One for the Landing Page (promoting the platform, showing blog/testimonials)

Another for the main App (hand analysis + AI feedback using Gemini SDK)

I started by integrating Gemini 1.5 Pro using the SDK to analyze 10–20 poker hands per student per day. I asked both Gemini and ChatGPT about costs, and both suggested that with 100 students (paying ~AUD $6–10 each), I’d expect to pay around AUD $100–$200/month — which would be sustainable.

:warning: What Went Wrong:

Later I tried upgrading to Gemini 2.5 Pro through Vertex AI, believing it would give better results. I wasn’t aware of the billing complexity and eventually received a surprise invoice of over USD $2.5K, even though I hadn’t commercialized the app yet.

I trusted AI too much without understanding cost controls.

Thanks to help from Claude AI and the community, I deleted some keys and paused services.

But now, my total bill is nearing $20,000 USD, and I feel overwhelmed.

:light_bulb: What I’m Trying to Do:

I don’t have an IT background. I’ve learned everything through tutorials, Firebase docs, and visual experimentation.

I’m now using Visual Studio to learn how to build apps and websites more securely.

I genuinely want to continue building this tool to help others — but I need guidance.

:red_question_mark: My Questions:

  1. How can I avoid mistakes like this again? Is there a beginner-friendly checklist or resource on billing safety when using Vertex AI + Gemini?

  2. Is using tools like n8n with Gemini or ChatGPT API cheaper/smarter than Vertex AI for a small, low-budget app (100–200 users)? My use case is just analyzing poker hands in a friendly, educational way.

  3. Where can I find realistic examples of monthly costs for apps using Gemini/ChatGPT with 100–1000 users running 10–20 requests per day?

  4. What pricing model should I use for students to cover analysis costs without making it too expensive?

  5. Any resources for non-technical founders on launching AI-based apps safely, especially those combining Firebase + AI services?

:folded_hands: I know this was a long message, but I truly appreciate any help or advice. I’m not trying to create a massive business — just something small and impactful that helps others.

Thank you for your time and support.

Best regards,
Victor Manolo Salazar
Founder, AggroFish Analyzer
Brisbane, Australia
[aggrofish.com]

2

I work as a product consultant helping companies build AI apps, so costing AI workloads is something I’m very familiar with. However, before costing, you need to understand your tech stack, because this will dictate the costing that follows.

Evaluating your tech stack (before even considering AI)

It sounds like you have a clear idea on the value proposition you want to offer your customers. But the key question is: what do you actually need AI for?

For poker hand analysis, there are many open-source libraries that will do the grunt work for you - eg. Analysing hand rankings, or statistical probabilities. Are you employing any of these libraries?

This is important to figure out first, because it may cut your AI costs hugely and may even negate the need for AI at all.

AI APIs will always charge you by input tokens (your system prompt + the info you send it) and the output tokens (the info returned by the AI). Output tokens are ‘usually’ higher than input tokens, and they’re more expensive in the APIs too.

So if you had an open-source poker hand analysis library doing most of the analysis (for free), but that libraries output was very clunky (eg. just numbers), then you do have an AI use case - you pass that clunky output to an AI to reformat and comment on.

Evaluating how you use AI

You’ll need to have a good idea of how many input/output tokens you are generating for each user request. As a general rule, 1 token = 3/4 of a word. Or 100 words = 133 tokens.

You’ll need to cost this specifically for your app, you won’t be able to use other apps as comparisons as they vary significantly. You need to find out:

  1. How many input tokens you are sending per request? And does this vary, or is it always approx the same number? (I suspect it will be fairly consistent for your app)

  2. How many output tokens are you receiving per request? And does this vary or have you limited it?

Then use a pricing calculator to estimate your current cost per request (both input/output). Extrapolate this upwards to the number of requests you allow the user to make on their subscription.

Restricting your output tokens

Because output tokens tend to be the most expensive part of an AI API call, you should restrict them. OpenAI has a featured called ‘structured outputs’ that allows you to define the exact structure of the object returned and the token count. Gemini has Function Calling + Zod to do the same. But it’s incredibly important you do structure your API responses and limit the output tokens, otherwise you risk costs running away from you.

Choosing the right AI API

Once you have nailed down the input/output tokens, then you need to research which AI API is best for you. Gemini 2.5 Pro - while very capable - is probably overkill for what you need. If you are using open source libraries to do the grunt work and then just using an AI to ‘humanise’ the output, then you can use a much less powerful model.

Vertex AI is usually used for complex enterprise workloads, pretty sure you don’t need this at the moment. One of the smaller Gemini flash models would probably do the job. Or even consider Grok 4 Fast, which is incredibly cheap and very capable. For context, the differences between the various AI lab API costs is vast - Grok 4 Fast is 20 times cheaper than Google Gemini 2.5 pro, and as capable in intelligence in many areas.

Subscription costs

Once you’ve analysed the respective AI lab API costs, you’ll have a ‘cost per request’, which you can then use to calculate your subscription costs.

Ideally, you’ll be looking for an 80%+ uplift (margin) on your costs. For example, if a user is paying $6AUD monthly subscription, you’ll need to restrict the number of hands a user is analysing each month to ensure they don’t exceed your costs.

The key message here is try to consider whether you need AI for all of your tasks. Too often I see companies use AI for use cases that require a deterministic approach - meaning, ‘ordinary’ code can do a lot of the work. But the areas you do need AI for should be tight, curtailed and strictly managed.

3

Topic: Vertex AI costs & safer options for small-scale educational apps

Hello,
This is a good question and one I see often when educators or small teams start experimenting with Vertex AI. A few tactical points to keep in mind:

  1. Vertex AI Cost Profile

    • Vertex AI can become expensive quickly if you’re not careful with scaling or leaving endpoints open.

    • Even small experiments can accumulate costs due to model hosting, auto-scaling, or background jobs.

  2. Guardrails for Safer Experimentation

    • Budgets & Quotas: Set daily budget alerts in Google Cloud Billing. These will notify you before you overspend.

    • Rate Limiting: Cap API calls within your app logic (especially for student/educational projects).

    • Ephemeral Endpoints: Use short-lived deployments for demos and tear them down afterward.

  3. Safer Alternatives

    • Firebase Genkit + Gemini: tightly integrated with Firebase, and you can run flows with lower overhead than full Vertex AI hosting.

    • Hugging Face Inference API: pay-per-call, good for predictable classroom workloads.

    • Local Models (Ollama, GGUF builds): if your use case allows, this avoids cloud costs entirely.

  4. Educational Strategy

    • Start with free tier + cost alerts.

    • Prototype workflows with Hugging Face or Firebase Genkit, and only escalate to Vertex if you truly need scale.

This way, you gain flexibility without risking runaway bills.

We’re also working on practical doctrines for teams building with Firebase, Unreal Engine, and AI pipelines. If you’d like to compare notes or need tactical support, we’re gathering builders in a focused Slack workspace. DM if you’d like an invite.

4

Hi Betelgeuse,
Thank you for taking the time to provide such detailed technical guidance. Your breakdown of the cost structure (input/output tokens) and the suggestion to use open-source poker libraries first makes complete sense - I was definitely over-engineering with AI when basic math could handle most of the analysis.
Your point about using AI only to “humanize” the mathematical output is particularly valuable. I can see how this approach would dramatically reduce both complexity and costs while still providing the educational value I’m aiming for.
I’m going to research the poker calculation libraries you mentioned and implement proper token limiting. The comparison between Gemini 2.5 Pro and alternatives like Grok 4 Fast (20x cheaper) was eye-opening - I clearly chose the most expensive possible approach without understanding the options.
Your advice about the 80% margin calculation will help me build a sustainable pricing model. I appreciate the reality check that my current approach wasn’t scalable.
Best regards, Victor

5

Hello Antonio,
Your response really resonated with me, especially the emphasis on safety guardrails. The $20K lesson was painful, but your practical recommendations give me a clear path forward.
I’m particularly interested in Firebase Genkit as an alternative to Vertex AI - it sounds like it would integrate well with my existing Firebase setup while avoiding the enterprise-level complexity I clearly wasn’t ready for.
Your suggestion about budget alerts and rate limiting will definitely be part of any future implementation. I should have had these safety measures from day one.
I’d be very interested in connecting with other builders facing similar challenges. Could you share details about the Slack workspace you mentioned? Learning from others’ experiences with Firebase and AI integration would be invaluable.
Thank you for the perspective on starting small and scaling responsibly.
Best regards, Victor Manolo Salazar

6

Slack. Here is the link that will bring you to the Slack I am building. This part of helping others is still under construction. I was able to learn really fast what a real IDE should have in its Toolbox. That was indeed a painful lesson to learn @20k. (mines was a 1k price pain), not a comparison, it was meant to show that no matter the cost, learning this way can make or break an organization. For me I had to stop developing my Card Captor game, business account closing soon (second one), the dream derailed. In exchange I solved the problem with Firebase not delivering what I thought it should. The problem with this type of thinking is that the App had nothing to do with it. This was the result of not reading the instructions from the start and not knowing how to properly set up an IDE Coding Environment. Here’s a small list of things you should have working together: Git, Gist, Slack, VS Code, Visual Studio, NPM, Bash, PowerShell Genkit, Firestore Storage, and Data Connect along with Google Cloud Console (these last are where Price Pain Point Controls have to be established to maintain costs runs. My goal is to help you get the most out of Firebase Studio. Do not rely on Slack Channels for your needs this is other developers discovering ways to code better. Instead take time to know your applications needs versus what you as a developer would like or want.

Good luck to you developing a stellar app