What tech stacks are some founders
using to build AI startups?
From generative AI like Midjourney and Open AI to customer service tools like Frame AI and Aivo, artificial intelligence is a big business in 2023.
The big question founders are asking at the moment is, what tech stack do you need to build an AI startup? It’s vital that any components you choose are high-quality, support each other, and scale as your startup grows.
Of course, the definitive tech stack will depend on the AI tool you are building, your ultimate goals, the skillset of your team, and your budget. However, here are some of our thoughts to get you started.
1. Programming language
Examples: Python, R, Java1
The first step is choosing the right programming language for your AI application.
Python is far and away the most popular choice, as it offers a wide range of libraries, is relatively easy to learn, and can be combined with other languages.
However, other programming languages like Java and R, and C++ can be useful too.
2. Machine learning frameworks and deep learning frameworks
Examples: TensorFlow, PyTorch (Machine Learning), Keras, MX net (Deep Learning)
Before we begin, let’s explain the difference between machine learning frameworks and deep learning frameworks. Both frameworks are essential for handling and processing data, meaning you can develop the learning model that powers your AI platforms.
However, while a machine learning framework is more generalist and can handle a wide range of data and tasks, a deep learning framework is more specialist. It might be that a machine learning framework is sufficient when building your AI app. However, if it needs to carry out more complex tasks like image classification or speech recognition, you may need a deep learning network in place.
3. Data storage and processing
Examples: MySQL, Cassandra, Apache Spark
AI platforms handle large amounts of data, so significant data storage and management is needed.
The right option depends on not just how much data you need to process, but how structured and consistent your data is. Depending on the data you handle, you may need to use different types of databases.
4. Cloud computing and hosting
Examples: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform
The growth of cloud computing has made it easier than ever for AI startups to thrive. Cloud computing makes it easier to scale and collaborate, offers solid security features, effortlessly integrates with other technologies, and as it is affordable, startups only pay for what they need.
Which cloud computing platform is right when building your AI startup? It depends. It’s essential to not just look at the cost and the features available, but how reliable the platform is and how you can customise it to your needs. Some platforms are more flexible than others.
5. Data visualization tools
Examples: Power BI, Seaborn, Tableau
When your AI platform processes a lot of data, you need to keep on top of it all. A powerful data visualisation tool can help you understand trends and patterns, make better business decisions, and communicate results to stakeholders like investors.
It’s important to choose a data visualization that can scale with your startup and can handle the volume of data your platform processes. Most importantly, your tool of choice needs to align with the expertise in your team and integrate with the rest of your tech stack.
6. Natural language processing
Examples: SpaCy, Google Cloud Natural Language, Natural Language Toolkit
If your AI platform concerns text and speech (like a chatbot or virtual assistant, for example), then a natural language processing, or NLP, tool is essential. NLP helps your system to understand and generate human language, meaning it can communicate with users in a genuine and authentic way.
When identifying the right NLP tool to use, it’s crucial to ensure it supports the tasks you need your AI platform to achieve. You also need to ensure it’s secure and operates within the data and privacy regulations of your region, as you might be handling a lot of sensitive data.
Cost is a crucial factor too. Some NLP tools are open-source and free to use, while others work on a subscription basis.
7. Version control
Examples: Git, SVN, Mercurial
When you’re building your AI platform, it’s important to log all changes made, especially if you have developers working on different components at the same time. Effective version control makes it easier to keep track of coding changes, identify errors, and work on new features.
Git is the standard when it comes to version control; however there are other version control tools available. Consider what features you need, whether you want to host the system on your own servers or in the cloud, and how secure it has to be.
8. Deployment
Examples: Docker, OpenVINO, Seldon Core
You’ve worked hard on the code for your AI platform, and it’s time to deploy it to end users. The right platform means your platform runs quickly, is safe and secure, and is easy to use.
There are a wide range of deployment tools out there. Consider how the tool works with your programming language and frameworks, how flexible it is, and what the response time is like — especially if you need your platform to make fast decisions. If you frequently update your AI model, you may want to consider a tool that supports continuous deployment.
9. Collaboration tools
Examples: Slack, Trello, Zoom
And finally, it’s important to have a solid collaboration tool in place, especially if your team works remotely. The more reliable your communication platform of choice, the less likely mistakes will be made, and the faster you can launch.
We hope this article has provided valuable insight into the tech you need to build your AI startup. If you’re still unsure, an experienced custom app development company can recommend the right tech stack to suit your unique needs.
