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Buyer’s Guide: How to Choose the Right AI Development Software

Explore top AI development tools and learn how to choose the right platform for your project needs.

By: R. Paulo Delgado
July 2, 2025
10 minute reading
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Artificial intelligence has found its way into all areas of business to improve productivity. Companies are hungry for AI software solutions that can automate business processes and streamline workflows. 

Many powerful AI assistants already exist. Large language models (LLMs) such as Claude from Anthropic, ChatGPT from OpenAI, and Gemini from Google are easily accessible to everyday users through their web interfaces. 

However, what if you want to leverage the power of these LLMs in your own apps? What if you’re a programmer who wants to create AI applications for clients? What if you want to train your own model or fine-tune an existing model for a proprietary app?

Here’s how to do it.

The difference between AI coding tools and AI development software

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We’ve written a separate article about the best AI tools for software engineering. These tools integrate into the development process to help with code generation, debugging, code suggestions, and many other coding tasks. 
AI coding tools act as coding assistants. Many are available as extensions to popular integrated development environments (IDEs), such as VS Code, Android Studio, PyCharm, and Eclipse. Some tools index your entire codebase so they can generate code in a contextually aware manner, while others offer less deeply integrated functionality. 
Many of them connect to git repositories. They provide AI agents that can develop entire sections of code without human intervention. You’ll still need help with expert software development to make sure the generated code has no errors, but these tools can be a massive timesaver. 
Examples of AI coding tools include Cursor, Replit, Windsurf, GitHub Copilot, and Tabnine. 
AI coding tools differ from AI development software in that they’re aimed at the development process itself. 
AI development software refers to the tools, frameworks, platforms, and software libraries used to build AI technology. 
For example, if you wanted to build your own natural language processing solution to improve business decision-making, you’d need a platform and tools that support that. AI coding tools might help during the actual development, but you’ll likely need access to a deep learning platform, AI code libraries, and AI-specific APIs for your app. 
This article is about the latter: The high-quality tools required to build real-world AI applications, such as for business optimization and data analysis tasks. 

Methodology: How we selected these AI development platforms

Only a handful of platforms and toolkits exist that provide robust capabilities in the area of advanced AI development. 
Whereas many tools now claim to provide AI development capabilities, they’re usually referring to some form of AI coding capability that uses AI to help you write code. Many of these tools are just wrappers around a GPT-4, Claude, or Gemini API. 
We chose the tools below based on four criteria: 
  1. Does the tool or platform allow you to develop advanced AI-enabled apps easily?
  2. Does the tool or platform let you create or fine-tune existing model datasets for advanced AI functionality?
  3. Is the tool accessible to a wide range of skills? For example, TensorFlow is a well-known library for training AI models. However, it’s more complex to learn than its successor, Keras, which also works with other libraries in addition to TensorFlow. So we skipped TensorFlow and included Keras on the list. 
  4. Is the tool accessible to small businesses and individual developers? 

Top 10 AI development software

Here’s our top ten list of AI development software platforms, toolkits, and libraries, in no particular order. 

1. Amazon Web Services (AWS)

Amazon Web Services
Amazon Web Services (AWS) is a cloud platform that offers tools to build AI-powered applications. AWS includes services like Bedrock, a tool that lets you use pre-built AI models for generating text or images, and SageMaker, a service that simplifies building custom AI solutions, even if you’re not a coder. 
Amazon AWS is focused on advanced AI tasks, especially in the area of training your own models. It’s ideal for enterprise customers or people building for the long term in AI. 
AWS provides several foundational models, which you can fine-tune. 
If you’re an experienced developer and want to leverage large models, AWS might be for you, but we don’t recommend it for companies that simply want to get an AI app out there rapidly. 
However, AWS does offer a handful of tutorials on how to build generative AI solutions in your business. 
Getting expert cloud management services from Fiverr can help you navigate AWS’s many offerings. 

2. Google AI Tools

Google
Google offers several tools for all levels of skill to build AI tools. 
For example, Google AI Studio is a user-friendly, web-based tool designed for people with little coding experience to create AI-powered applications. AI Studio helps you generate code snippets that you can then use in your Gemini-powered AI apps. For example, we prompted it to generate a recipe from an image using Gemini. 
Google AI Studio
Google then provides a code sample for how to integrate that prompt into your app. The sample is provided in a handful of programming languages, but you can use a code converter to convert it to your preferred programming language.
Google AI Studio
For enterprise customers, Google also offers Vertex AI, a platform for building generative AI tools. 
Google also has AI-powered translation tools, vision tools, video intelligence, chatbot tools, and other solutions to facilitate building an AI app. 
If you need some help figuring out what tool you need, you can use Fiverr’s expert AI consulting services for advice, and then get an AI programmer to build the tool for you.

3. Microsoft Azure AI

Microsoft Azure
Microsoft Azure AI is a coder-friendly cloud platform that helps you add AI features to your applications. It has several out-of-the-box AI services you can connect to through APIs, such as a Document Intelligence service that provides sophisticated processing of PDFs. 
It also integrates deeply with OpenAI, so if you’re using ChatGPT as your back-end, going with Microsoft makes sense.
Microsoft Azure

4. Hugging Face

Hugging Face
Hugging Face makes it easy to add language-based AI features to applications. It offers a large library of pre-trained models. 
Non-technical users can use Hugging Face’s Spaces, a no-code tool to build simple AI apps by selecting models and tweaking settings. Developers can use the Transformers library, which is a set of coding tools to integrate these models into custom apps. 

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Hugging Face is known for its community, offering free models and tutorials, making it accessible for beginners. It also offers an Inference API service that provides immediate access to pre-trained machine-learning models. Instead of installing and deploying your own model, you can just use the inference API to query an existing model. 
“Inference” is the process of an AI model coming up with an answer to the prompt you give it, which differs from model training. 
Hugging Face is ideal for startups or developers focused on language-based AI, although advanced features might require coding skills, which you can get from expert AI developers on Fiverr. 

5. Clarifai

Clarifai
Clarifai is an AI company that specializes in computer vision, natural language processing, and automatic speech recognition. It also provides a comprehensive AI platform that lets developers gain insight from unstructured data, including images, videos, text, or audio. 
They offer AI solutions across the entire AI lifecycle, from data exploration to labeling, model training, and deployment. They have solutions for both out-of-the-box and custom AI model development.
The platform is developer-friendly, with plenty of APIs and SDKs, and an intuitive user interface so that you can quickly integrate AI into your products. 
Clarifai
Clarifai has a much smoother on-ramp to getting started than other AI development platforms. It doesn’t assume you “know where to go,” and its initial dashboard provides all the necessary menu links for both beginners and experienced developers. 
Their AI playground, easily accessible via the dashboard, lets you test querying many of the popular AI models such as Llama, GPT-4o, DeepSeek, and Gemma. It then provides code samples for those queries in many popular programming languages, including PHP and Java. 
Clarifai

6. LiteRT (previously TensorFlow Lite)

Google
LiteRT (short for Lite Runtime) is Google's rebranded and expanded version of TensorFlow Lite, a high-performance runtime designed for on-device AI and machine learning inference. LiteRT lets you deploy ML models efficiently on devices such as Android phones, iOS devices, and embedded systems. It supports models from multiple popular frameworks, including TensorFlow, PyTorch, JAX, and Keras. 

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Running on-device inference is a massive stride forward in privacy-focused AI for industries such as healthcare that have highly restrictive privacy requirements. 

7. Scikit-learn

Scikit-learn
Scikit-learn is an open-source Python library for traditional machine learning rather than deep learning. It’s best for adding simple AI features to applications, such as predicting customer behavior or categorizing data. It’s beginner-friendly for coders and has clear documentation. 
If you’re not a coder, you can get help from one of Fiverr’s professional Python developers to integrate Scikit-learn into your apps. 
Scikit-learn is less complex than other tools, so it’s ideal for startups or businesses wanting straightforward AI without heavy customization.

8. OpenAI API Code Libraries

OpenAI
OpenAI provides an API that gives easy access to its many models so you can integrate ChatGPT functionality directly into your workflows. The API lets you connect to ChatGPT’s underlying algorithms in real-time to generate text, generate images, or improve your website visitors’ user experience through an AI-powered chatbot.
However,  robustly accessing an API directly requires huge amounts of boilerplate code, which is why API providers typically provide libraries or SDKs—software development kits—that encapsulate all of the underlying API’s functionality. A code library lets you call API functions using familiar programming language syntax, empowering programmers to create AI apps much more rapidly. 
OpenAI offers official code libraries for:
  • JavaScript
  • Python
  • .NET
  • Java
  • Go

9. ONNX Runtime

ONNX
ONNX (Open Neural Network Exchange) runtime is Microsoft’s high-performant machine learning inference engine. It’s open-source and multi-platform, and supports models stored using the ONNX format. It can run on various hardware platforms, including CPUs and GPUs. 
If you have a model in some other format (such as a TensorFlow or PyTorch model), you can convert it easily into the ONNX format. 
ONNX works in the cloud, on mobile, and in web environments. It has APIs and code samples for Python, C#, C++, Java, JavaScript, and Rust, as well as tutorials and quickstart guides. 
ONNX
It handles both traditional models and deep learning models efficiently, and it provides numerous pre-trained models for you to get started quickly. 
ONNX

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10. Keras

Keras
Keras is an open-source, high-level Python API that provides a user-friendly interface for building and training AI models. 
Keras abstracts much of the complexity of building deep learning models so that developers who are new to the topic can get started easily. It supports TensorFlow, JAX, and PyTorch as backends. 
As a model training tool, Keras is suited for back-end development early in the AI development cycle. 

How to choose an AI development software

The tools we chose above are all very different from each other, and you’ll often use several together. Here are some tips on how to choose one. 

Core AI capabilities and pre-built models

The core capabilities of your AI development software depend on your use case. 
If you’re building a highly unique AI application for a specific business domain, you’ll likely want to train your own models with a library like TensorFlow, PyTorch, or Keras. 
If your project requires a deep learning model, instead of a traditional machine learning model, you’ll likely want access to foundational models that you can fine-tune.

Ease of use

AI and ML are advanced topics. We’re not all data scientists, so low-level libraries like PyTorch and TensorFlow aren’t for all of us. 
Still, companies are demanding AI tools more and more, so traditional developers must find tools that have a low barrier to entry. 
Many cloud platforms provide easy-to-use SDKs and APIs that encapsulate much of the AI functionality you’ll need. 

Programming language support

Although Python is considered the language of choice for many ML tasks, it’s far from the only choice when it comes to consuming AI APIs. 
Python also has limitations when it comes to front-end capabilities, which are handled better by other languages and frameworks.  
If you’ve been developing in the Microsoft ecosystem for a while, you’ll likely lean towards Azure, which integrates seamlessly with Microsoft tools. If you’re a Go developer, you’ll probably prefer staying within the Google ecosystem. 

Scalability

AI tools are incredibly resource-heavy, and you have to consider scaling from an end-device perspective and from a back-end perspective. 
If you’re building an AI tool that will process data entirely on the device while also accumulating more data, you’ll eventually run into a scaling issue. 
Similarly, if you’re building an AI service in the cloud that doesn’t have a robust backend, you’ll run into the same, but at the server level. 

Integrations and ecosystem

We see AI in almost everything these days. Users want AI to run across many different services and tools. 
When choosing an AI tool, make sure it integrates widely with other services. 
If you’re building on-device solutions, choose a toolkit that runs on all the devices and platforms you’re targeting. 

Community

Community support is crucial for such a new technology. An active community will share code samples and new ways of doing things, such as by writing fresh tutorials or helping out on community forums. 

Ethics and legal

Two major ethical challenges exist in the LLM area: Copyright infringement and data privacy. Almost all of the major generative AI providers are involved in copyright infringement lawsuits for allegedly using copyrighted materials to train their models. The outcome of these lawsuits might have important ramifications for developers and end users. 
Data sharing is the other significant hurdle, and you have to consider what user data you share with the AI back-end or whether you’ll run a model entirely in-house. This choice will determine which platform to use. For example, a complete in-house solution would require an offline, open-source LLM, such as Llama or Mistral. 

Pricing structure 

The final consideration is pricing. Large AI models aren’t cheap to run. If you’re building an in-house solution, you’ll need powerful hardware with cutting-edge AI processors. 
Cloud solutions exist, but their pricing quickly spirals as your solution scales. 
Have a clear idea of these costs before going in because it’s challenging to get out of a cloud solution once you’re mired in it. 

Hire a freelance AI developer today

AI software development presents an immense opportunity for business, but it’s complex. Whether you’re building a predictive search engine or prototyping a new mobile app, you’ll need knowledge of both data science and computer science. 
We’ve come a long way since GPT-3 was the only public-facing LLM available. Now we have several proprietary LLMs that can be easily accessed via API. We also have many more open-source models that you can run completely in-house. 
Whether you need data science expert services or AI development professionals, Fiverr is the marketplace for you. 
Fiverr is a freelancer platform with thousands of experts across all major business categories, from data professionals to AI consultants to graphic designers. 
Getting started is easy: Just open a free account and start searching for your AI expert today. 

AI development software FAQs

What software is used to develop AI?

Many tools exist to develop AI solutions. For training AI models, you’ll need a machine learning library such as Keras or TensorFlow. 
For inferring answers from those models, you’ll need to know a programming language to call its APIs or access the model directly. 

Which AI tool is best for software development?

Many AI-powered software development tools exist, such as Amazon Q Developer, Tabnine, Windsurf, and GitHub Copilot. The best AI tool depends on your needs. 

Is ChatGPT the best AI?

ChatGPT was the first publicly available, chat-based LLM. Many competitors exist today that rival ChatGPT in benchmarks and other tests. Those rivals include Grok, Claude, Gemini, and DeepSeek.

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R. Paulo Delgado

R. Paulo Delgado is a tech and business freelance writer with nearly 17 years of software development experience under his belt, including WordPress programming. He is also a crypto journalist for Moneyweb, and proudly a member of Fiverr's Pro Seller program — hand-vetted professionals, verified by Fiverr for quality and service.