# Our Tech Stack at Space Runners As a seed stage startup, our top priority in choosing technology is the ability to move fast and iterate quickly. We must be able to test new ideas in market quickly and be willing to drop them and try something else if it’s not working. Additionally, as an AI image generation company, we must choose flexible and customizable tools that allow us to fully experiment with AI and allow our Users to fully express themselves. All the while, we need to balance this with costs. Here’s our opinionated tech stack, starting from the frontend and going all the way back to our image generation services. ![](https://cdn.hashnode.com/res/hashnode/image/upload/v1727373005273/3cfc6fee-cf16-48a0-a458-54e8dced9ab2.png align="center") # Frontend (web) We have 2 websites we’re actively maintaining and updating: 1. [ablo.ai](https://ablo.ai): Our core image generation and community design (and soon to be e-comm) product. 2. [spacerunners.com](https://spacerunners.com): Our informational, inspirational site for the company. ## Stack * **Analytics: Segment → Amplitude** [Segment](https://segment.com/) is great for piping your user data and analytics to multiple places and [Amplitude](https://amplitude.com/) is great for capturing the important events for your User’s behavior. * **Canvas manipulation: Fabric.js** [Fabric](http://fabricjs.com/) makes it easy to work with the HTML Canvas * **Customer Services: Intercom** We integrate [Intercom](https://www.intercom.com/) into our sites so customers can easily chat with us. * **Deployment: Render** [Render](https://render.com/) is free to host and deploy static sites and also gives us preview builds on pull requests, which is super handy for testing before we merge in code. * **Error reporting: Sentry** [Sentry](https://sentry.io) is great for catching issues in production and we use it full-stack as well. * **Framework: React.js** In our opinion, [React](https://react.dev/) is still the easiest framework to build fast in and has a huge community and toolset supporting it. * **Language: Typescript** We are full stack [Typescript](https://www.typescriptlang.org/). This allows our FE developers to more easily work on the BE. * **Real-time: Pubnub** [Pubnub](https://www.pubnub.com/) provides a simple API to get real-time notifications, such as when credits are used on our your account. * **Styling: Chakra UI** [Chakra](https://v2.chakra-ui.com/) is a full UI and styling library that enables us to build and style components super quickly (once you learn the basics :) ) # Services Backend (API) Our Services BE powers our public API used by Clients all over the world in their design tools and also internally for us in ablo.ai. ## Stack * **Analytics: Segment → Amplitude** [Segment](https://segment.com) is great for piping your user data and analytics to multiple places and [Amplitude](https://amplitude.com/) is great for capturing the important events for your User’s behavior. * **API Docs: Readme** [Readme](https://readme.copm) integrates nicely with our OpenAPI definitions that are generated automatically from our annotations in our Nest app. * **Cache: Redis** We cache responses in [Redis](https://redis.io/) to some of those heavy queries where the response doesn’t change often. This speeds up our system and reduces the load on our Postgres DB. * **Deployment: Render** [Render](https://render.com) makes it really easy to do CI/CD and includes our Postgres and Redis stores as well. * **DNS: Cloudflare** [Cloudflare](https://cloudflare.com) provides a nice proxy to all our endpoints and also provides out of the box DDoS protection. * **E-comm: Shopify** [Shopify](https://shopify.com) enables us to easily set up a headless store, integrates nicely with our printing and shipping partner (Printful), and handles all the payments for us. * **Error reporting: Sentry** We use [Sentry](https://sentry.io) full-stack for error reporting to catch issues in production. * **File store: GCP Storage** We store images mostly in [GCP Storage](https://cloud.google.com/storage) as that’s where our image generation workflows (e.g. Image Maker, Fontmaker, and Photo Transformer) live. GCP Storage is also great because they have a feature that automatically moves files to cold storage if they haven’t been used in a while. * **Framework: Nest.js** [Nest](https://nestjs.com/) gives us an easy to use modular framework for cleanly separating services in our monolithic BE, defining RESTful endpoints, generating API docs, and running scheduled jobs. * **Language: Typescript** We are full-stack [Typescript](https://www.typescriptlang.org/). Allows our BE devs to more easily work on the FE. * **Printing & Shipping: Printful** [Printful](https://printful.com) has a huge catalogue and enables us to send custom designs via API to get printed and shipped. * **Real-time: Pubnub** We call a simple [Pubnub](https://www.pubnub.com/) API to notify all Users when credits have been used on their Client. * **Email: Sendgrid** We use [Sendgrid](https://sendgrid.com) for sending transactional emails from our System via API. * **Subscriptions: Stripe** [Stripe](https://stripe.com) enables us to easily set up monthly credit subscriptions with overage charges and handles all the billing for us. * **Transactional data store: Postgres** [Postgres](https://www.postgresql.org/) is a tried and true, scalable, relational database. As we’ve scaled, we have had some performance issues with complex queries involving a lot of joins, and have had to make optimizations here and there to our queries and call patterns, but that’s part of the game. # Machine Learning Backend (AI) We use machine learning / AI to do a number of things, mostly related to image generation and manipulation: * Image Maker: Our text to image service * Font Maker: Our text to graphic font service * Photo Transformer: Our image to image service * Background removal * Upscale We’re always experimenting with the best way to do these things and the landscape is changing rapidly under our feet, but our current stack is something like this: * **Background removal: BiRefNet** [BiRefNet (bi-direction recurrent feature network)](https://www.birefnet.top/) is a performant, open-source, background removal library that doesn’t need a GPU. * **Deployment: Google Kubernetes Engine** [GKE](https://cloud.google.com/kubernetes-engine/) enables us to build our custom AI workflows in containers and utilize GPUs from GCP. * **Image generation engines: Stable Diffusion and Flux** [Stable Diffusion](https://stability.ai/) gives us the best combination of customizability and quality. [Flux](https://blackforestlabs.ai/) is a great combination of speed and quality. * **Image storage: GCP** **Storage** [GCP Storage](https://cloud.google.com/storage) is great because they have a feature that automatically moves files to cold storage if they haven’t been used in a while. * **Language: Python** All the hip machine learning and AI libraries are written in [Python](https://www.python.org/). It also has image manipulation libraries. * **Models: Hugging Face** [Hugging Face](https://huggingface.co/) has an easy to use repository of models to experiment with and a great community. * **Training: Replicate** For custom style training, we use [Replicate](https://replicate.com/) to train LoRAs and do inference. * **Upscale: SupIR** [SupIR (Super-Resolution using Iterative Refinement)](https://supir.xpixel.group/) is a great open source library for upscaling as it enables us to upscale to 16MP for high quality printing while preserving details from the original image. That’s a wrap! Let me know if there’s anything here you’d like to learn more about, and we’ll write a follow up post and go in depth.