Stop Vibe Coding 🚫 Make Your AI SaaS Production-Ready with these 7 Tools ⚡️

Vibe Coding is Fun, but It's Not Production Ready!

If you’re building an AI SaaS that’s actually production-ready, you’ll need more than just an OpenAI API key and a slick frontend.

You need infrastructure, deployment pipelines, analytics, payments, and mobile reach.

And you need it all to work together smoothly!

To simplify your work, I've curated a list of 7 Tools that make your life easier.

Let's Start!

1. Backend & Infrastructure - Nitric.io

Writing backend logic is fun until it’s time to deploy.

You have to deal with cloud consoles, complex config files (Terraform, CloudFormation, etc), and vendor-specific services.

It slows you down and locks you in.

Nitric, an open-source backend framework, lets you build backends using languages you already know (like TypeScript, Python, or Go).

You just declare what you need - APIs, storage buckets, databases, queues, and it magically wires up the correct cloud resources across AWS, GCP, or Azure.

What You Can Do with It:

• Define APIs and events in code
• Automatically provision cloud resources
• Easily switch between cloud providers
• And a lot More...

In Simple Words, You write the logic → Nitric deploys it with the right infra → You get a production-ready backend without crying in YAML.

You can follow quick-start docs from Nitric to test and explore more functionality.

If you want your backend to scale as fast as your AI idea, Nitric is a no-brainer.

Star Nitric on GitHub ⭐️

2. Cloud Analytics with SQL: StackQL

Okay, your app is live (maybe using Nitric!). Congrats!

But what’s running? How much is it costing? Did someone leave a test bucket public? 👀

StackQL brings SQL to your cloud environment. Yes! You can query your cloud resources like a database.

Why Developers Love It:

• One query language for AWS, GCP, and Azure.
• Perfect for cost tracking, compliance, and security audits.
• Easy to script, schedule, and automate inside CI/CD.

You can also Set up a StackQL job that alerts you if a new compute instance is created in an unexpected region.

You can also use StackQL to generate reports by querying cloud infrastructure resources.”

SELECT
    project,
    resource_type,
    SUM(cost) AS total_cost
FROM
    billing.cloud_costs
WHERE
    usage_start_time >= '2025-03-01'
    AND usage_end_time <= '2025-03-31'
GROUP BY
    project, resource_type
ORDER BY
    total_cost DESC;

No more surprise bills or misconfiguration.

If you know SQL, you already know how to manage your cloud with StackQL.

Star StackQL 🌟

3. Quick API Integrations: SwytchCode

Every SaaS app needs third-party integrations - Stripe for payments, Twilio for SMS, SendGrid for emails.

But going through their Docs and implementing those takes a lot of time!

This is where SwytchCode becomes super useful.

Swytchcode is an AI-powered tool that generates working integration code for APIs.This saves both time and costs compared to using other LLMs for the same task.

Tell it what you need (“Create a Stripe Checkout session”), and it generates the boilerplate code for you - backend endpoints, API calls, even error handling.

Why It Saves Your Sanity:

• No more reading 20 pages of API docs
• Code is clean, customizable, and yours to own
• Works with dozens of popular APIs
• It has MCP support

With SwytchCode, You can focus on shipping things faster without spending much time on working on these integrations!

Check out Swytchcode

4. Build with Open-Source LLMs - Nebius AI Studio

Using OpenAI’s API is great, but it’s not cheap, and you’re tied to their rules.

Open Source Models are the Future of AI!

Nebius AI Studio gives you scalable, OpenAI-compatible API access to powerful open-source models - like Llama 3, Mistral, Qwen, DeepSeek, and even image models like Flux.

Here’s how easy it is to build a RAG app with Nebius 👇

You don't need to wrangle GPUs or manage Kubernetes clusters.

Why It Rocks:

• OpenAI Compatible APIs
• More control over models and pricing
• JSON/function calling for advanced workflows
• Lower Costs
• Handles massive scale

You can also fine-tune the Model based on your Needs, This adds a more professional touch to your Saas.

Fine-Tune Your LLM in MINUTES with Nebius ⚡️

Arindam Majumder ・ Mar 13

#ai #deepseek #rag #python

If you're working on GenAI Projects, give Nebius a Try!

Check out Nebius

5. Go Mobile, the Smart Way - Eachlabs

You’ve built an awesome AI feature on the web-app, let's say, image generation.

But now users want it on mobile. Do you rebuild everything in Swift or Kotlin? Not ideal.

Eachlabs saves you from the mobile headache.

It helps you bring AI to mobile apps via a visual backend builder + prebuilt workflows. You can plug AI features into iOS/Android with minimal backend work.

Why Mobile Devs Love It:

• Drag-and-drop AI logic builder
• Access 100+ pre-integrated models
• Easily extend features like voice AI, headshot generation, or dubbing into mobile apps.

My Friend, @astrodevil wrote an awesome guide using Eachlabs, You can check it out as well:

How to Build a Ghibli-Style AI Video App Using Pixverse and Eachlabs🎉

Astrodevil ・ May 1

#webdev #javascript #ai #programming

If you're developing a mobile app that offers Generative AI Features, you should use Eachlabs' pre-built workflows and models for smooth user experience.

Check Out Eachlabs

6. Fine-Tuning + Deployment: Jozu.org

Fine-tuning your own model is exciting, but once it’s done, how do you deploy it, version it, and monitor its performance?

Jozu makes it production-ready.

It gives you an all-in-one platform to host, version, and serve your models with robust infrastructure underneath. Think of it as your model’s home in production.

For example, you can use KitOps CLI to create a ModelKit and push it to Jozu:

# Initialize a new ModelKit
kit init my-modelkit

# Add files to the ModelKit (e.g., datasets, code, configurations)
cp my-dataset.csv my-modelkit/
cp my-model.py my-modelkit/

# Push the ModelKit to Jozu Hub
kit push jozu.ml/my-organization/my-modelkit:latest

Then, using Jozu, you can generate Docker containers for your ModelKit, which you can deploy locally or in a Kubernetes cluster:

# Pull the ModelKit container
docker pull jozu.ml/my-organization/my-modelkit:latest

# Run the container locally
docker run -it --rm -p 8000:8000 jozu.ml/my-organization/my-modelkit:latest

With Jozu, you don’t need to stress over how to deploy or update your AI/ML project.

This tool is perfect for machine learning developers or companies that need a simple, secure, and scalable AI/ML workflow for their enterprise deployment needs.

Check out Jozu

Bonus Tool – Velt.dev

If You Want to add that "wow" factor to your saas, you should explore Velt.

Velt lets you add seamless in-app collaboration, just like Figma or Notion.

With a lightweight SDK, you can bring in:

• Contextual Comments
• On-screen Annotations
• Presence Indicators
• Video Feedback

And it works beautifully for both internal QA and live user collaboration.

Why Velt is a Useful Tool:

• In-Context Feedback – No more guessing what users mean. Comments appear right on the UI elements in question.
• Faster Iteration – Designers, developers, and QA teams can communicate clearly, cutting down dev cycles.
• Built-In Collaboration – Want to let your users co-work on generated content or dashboards? Ship Velt inside your SaaS.
• Dead-Simple Integration – Add it with a few lines of code. No overhaul needed.

I've also written an article on how you can implement Figma Style components with Velt. You can read it here:

Vibe Coding with Cursor AI: How to Build Figma-Style Comments

Arindam Majumder ・ Apr 17

#webdev #javascript #beginners #react

If you want to make your app feel truly collaborative, Velt is your secret weapon.

Check out Velt

That's a Wrap!

If you found this article useful, share it with your peers and community to spread the word about these incredible tools.

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Developer Advocate | Technical Writer | 600k+ Reads | Mail for Collabs

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Thank you for Reading!