I’m curious about what technologies everyone is choosing for their AI-focused SaaS applications. Here’s what I’m planning to use for my project:
Backend: Flask with Python
Testing: unittest and pytest for backend testing
Frontend: React with JavaScript and Bootstrap CSS
Database: MySQL with Memcached for caching
End-to-end testing: Selenium with Mocha
AI workflow: LangChain with custom monitoring tools
Deployment: Heroku platform
Development tools: VS Code with GitHub Copilot
Design mockups: Adobe XD with component libraries
Payment processing: PayPal integration
I’m especially interested in hearing about your AI integration choices and whether you’re using any specific frameworks for handling machine learning workflows. What combination of tools has worked best for your AI SaaS development?
your tech stack sounds solid! i’m going with node.js and express for backend, mongoose with mongoDB for the db, and for ai, i use huggingface directly. loking forward to seeing how everyone else makes their choices!
Your stack’s solid but you’re making it way harder than it needs to be. Everyone’s throwing out different frameworks, but the real issue is connecting everything together.
This is what kills most AI SaaS projects - integration hell. You’ve got Flask talking to MySQL, React on the frontend, PayPal webhooks, LangChain responses, and you need all of this working smoothly.
Don’t build custom glue code for everything. Automate the connections between services instead. I handle workflows where user actions trigger AI processing, database updates, notifications, and payments all in sequence.
Best part? Keep whatever tools you want. Flask and React? Cool. FastAPI like someone mentioned? Also cool. Just automate how they talk to each other instead of writing integration code that breaks constantly.
I’ve watched teams spend months debugging webhook failures and API timeouts. Automated workflows handle retries, error routing, and orchestration without the custom code headaches.
For AI SaaS, you need reliable chains: User subscribes → AI processes data → results stored → email sent → usage tracked. Coding this manually sucks.
Check out Latenode for these integrations. Way cleaner than building your own orchestration.
I’ve been building AI SaaS for 4 years - your stack’s solid but here’s what I’d change.
Swap Flask for FastAPI. The async support and auto-generated docs will save you massive headaches later.
I’ve ditched LangChain recently. Great for prototypes but becomes a mess at scale. Now I use OpenAI SDK directly with custom prompt management and Redis for caching. Much cleaner.
Drop Heroku if possible. AWS Lambda + API Gateway handles traffic spikes better and costs way less at volume. Learned this when our AI feature went viral and Heroku crashed.
Add monitoring like DataDog or New Relic. AI endpoints are unpredictable - you need to catch problems fast.
This video covers modern production choices. Guy knows his stuff.
Docker containers are a game changer for AI SaaS deployments. I run local dev environments that match production exactly - no more ‘works on my machine’ headaches that AI projects love to throw at you. I containerize everything separately: API server, model inference, background workers. For payments, go with Stripe instead of PayPal. Their webhooks play way nicer with subscription billing, and the docs don’t suck. I had to bail on PayPal after their webhook delivery kept failing and breaking user access. Here’s something crucial - add circuit breakers for AI API calls. External AI services crash more than you’d think. When OpenAI went down last month, apps with proper fallbacks kept running while others face-planted. VS Code’s great, but grab the Thunder Client extension. Testing AI endpoints becomes way smoother than jumping between your IDE and Postman all the time.
Been through several AI SaaS stacks over the past few years - here’s what I’ve learned about scaling. Your foundation looks solid, but I’d go with PostgreSQL instead of MySQL. The JSON handling is way better, and you’ll need that for AI model outputs and user interaction data.
For AI workflows, keep things modular. Don’t get locked into one framework. We use direct API calls to different providers with our own orchestration layer on top. Makes it easy to switch between OpenAI, Anthropic, or local models without rebuilding everything.
One thing I wish I’d done earlier - set up proper request queuing. AI endpoints are slow and unpredictable. Something like Celery with Redis helps manage user expectations and prevents those annoying timeouts.
Also, get logging for AI interactions set up from day one. Trust me, debugging prompt issues later without good visibility is a nightmare.
