Generative AI tools and platforms are changing the software development lifecycle and the way software products are developed, accelerating the process and quality of the final output. Artificial intelligence boosts developers’ productivity and speed even by 50%, improve end user satisfaction, and help deliver innovative applications more quickly than ever.
From workflow automation, automating mundane coding tasks or providing auto-complete suggestions to assisting in bug detection and resolution, GenAI tools allow software engineers to focus on creative problem-solving and building high-quality software. Yet, with constantly new AI tools for software development popping out, choosing the right one may be a challenge.
Whether you’d like to be more productive, simplify creative tasks or complex code, this article highlights the best generative AI tools available today, together with their key features, use cases, and pricing.
What is generative AI?
Generative artificial intelligence refers to advanced machine learning technology that can create different types of content such as text, images, audio, and video. Based on patterns learned from the existing data, the technology generates original outputs that resemble human creativity.
GenAI improves workflows, encourages new ideas, and allows businesses to reduce expenses. Already, 92% of Fortune 500 companies have adopted it in their processes.
GenAI platforms vs tools for developers: What is the difference?
Generative AI tools are software programs that help with specific tasks, like writing text, creating images, or generating code. They have a rather narrow purpose and can be used on their own or built into other apps, websites, or search engines to make work easier.
GenAI platforms offer a broader range of services as their ecosystem can include multiple tools, models, and other elements. They let users build, test, and manage different projects. Unlike single-use tools, platforms provide more options and flexibility, making them useful for businesses and developers who need a complete or tailored solutions.
How can GenAI tools and platforms be used in software development?
Depending on the needs, GenAI tools and platforms can be used for various purposes. For software development, they can be used as an assistant for programmers or as a guide on how to solve common coding errors and problems. There are many GenAI tools and platforms available on the market. Let’s have a look at the most helpful for the software development industry.
AlphaCode by Google DeepMind
AlphaCode by Google DeepMind is a system developed for competitive programming that shows how far code creation has come. It uses Gemini Pro model’s power to handle challenging coding tasks at a speed comparable to human programmers. It has achieved a ranking within the top 54% in simulated tests on platforms like Codeforces.
With a dataset of more than 15 thousand problems and 30 million human code samples, AlphaCode revolutionizes how AI helps developers by generating code, automating routine tasks, and improving problem-solving.
AlphaCode by Google DeepMind — use cases
AlphaCode can facilitate programmers and improve their productivity by providing real-time code suggestions, AI-driven code reviews and bug fixes, or performance optimizations. It automates repetitive coding tasks and helps write cleaner code. Also, AlphaCode supports engineers in learning new languages by providing them with code examples created by AI. It can assist business users in scaling AI-powered coding solutions for large projects.
AlphaCode by Google DeepMind — pricing
Alpha Code is free to use.
GitHub Copilot
By partnering with OpenAI, GitHub created Copilot—a leading code completion tool that seamlessly integrates with popular code editors like Visual Studio Code and JetBrains. The goal was to make developers’ lives easier with an AI pair programmer that helps to write code faster and more efficiently. The name itself reflects the company’s message that AI isn’t to replace the developer, but to be always on their side.
GitHub Copilot makes real-time code completions and recommendations as developers type in the integrated development environments (IDE). It uses advanced AI models, like OpenAI’s Codex, to understand the context of the code and offer relevant suggestions. Currently, it’s widely used and has become an industry standard.
GitHub Copilot — use cases
It creates relevant code snippets, gives ideas in real time, supports multiple programming languages, and can adapt to individual developers’ coding style. By using GitHub Copilot, developers can speed up their work, learn new concepts, and find alternative coding approaches and innovative solutions.
This GenAI for software development serves both experienced developers and beginners. Having AI-generated code together with explanations, users find it easier to meet tight deadlines and navigate and contribute to large codebases.
GitHub Copilot — pricing
GitHub Copilot offers several pricing plans:
- Individual: $10/month or $100 annually
- Business: $19 per user/month
- Enterprise: $39 per user/month
There’s also a free limited option: users have 2,000 code completions and can pull 50 chat requests per month.
Duet AI for developers
Duet AI is a collaborative tool for developers, development and cross functional teams, which integrates GenAI into Google Workspace and Google Cloud to boost productivity and optimize workflows. This gives programmers an opportunity to work together across different coding environments, which is especially valuable in large projects where a consistent coding style and shared architecture are key to success.
Additionally, Duet AI works well with project management tools to help keep track of progress and predicts when a project will be finished based on coding speed and complexity.
Google Duet AI — use cases
It supports more than 20 programming languages such as C, C++, Go, Java, JavaScript, or Python and integrates with multiple IDEs, including Visual Studio Code, IntelliJ, PyCharm, and Cloud Shell Editor. Developers can use the tool to simplify complex concepts, stick to best practices, and handle repetitive tasks. By debugging, troubleshooting, and generating boilerplate code, it helps save time and focus on important tasks.
Moreover, Duet AI can also be customized to suit the specific business needs by using private codebases, which makes it a flexible tool for enterprise software development.
Duet AI by Google — pricing
Currently, there is no specific price for the tool. AI developers can get a custom quote for their organization by contacting a Google Cloud sales representative.
Claude.ai
Claude.ai is a chatbot powered by Anthropic’s Claude 3.0 model family. Released in February 2025, Claude 3.7 Sonnet represents the latest advancement in the lineup. What makes Claude stand out is that it can write good code and think like an experienced developer. It can process up to 200,000 tokens of context, which equals around 150,000 words or a small codebase.
Anthropic says that GitLab’s development teams are 25–50% more productive when they use Claude. Sourcegraph, a code intelligence platform, reported a 75% rise in code insertion rates after choosing Claude as its main AI tool for developers.
Claude.ai by Anthropic — use cases
With Claude.ai developers can write, test, and debug complex software, and translate code between various programming languages. Through Github integration, they can analyze codebases with expert-level reasoning and make better engineering decisions in a shared workspace. For technical teams, it serves as both a productivity tool and a learning resource of modern technologies that accelerates development work.
Anthropic Claude.ai— pricing
Claude.ai has varying prices based on the model:
- Claude 3.5 Haiku: Input $0.80/MTok and output: $4/MTok
- Claude 3.7 Sonnet: Input: $3 / MTok and output: $15/MTok
- Claude 3 Opus: Input: $15/MTok and Output: $75/MTok
Amazon Bedrock
Amazon Bedrock is a solid option for software developers, especially for the integration of generative AI within processes. It offers access to foundation models (FMs) like Anthropic, Stability AI, and AI21 Labs through a single API. With this platform, it becomes easier to build and deploy GenAI applications without worrying about managing the underlying infrastructure and security.
Amazon Bedrock — use cases
Bedrock automates coding and debugging, generates detailed explanations for codebases and APIs, and even creates unit tests. It helps developers produce code snippets based on natural language prompts and boost team communication by simplifying repetitive coding processes. Amazon Bedrock also optimizes DevOps pipelines, improving software development efficiency and security.
Amazon Bedrock — pricing
Amazon Bedrock offers a few types of pricing:
- On-demand: Users pay for the resources they use without any long-term commitments. Prices depend on the model, but it’s usually a few cents per thousand tokens.
- Provisioned throughput: In this option, users commit to using a specific amount of processing power for a month or six months and pay by the hour. The longer the commitment, the lower the rate.
- Batch mode: It processes many prompts in one go, storing answers in an Amazon S3 bucket so that they can be accessed anytime. This mode costs 50% less than on-demand rates for selected models.
More details are available on their website.
Google Vertex AI
Google Vertex AI is a unified machine-learning platform that makes it easy to create, deploy, and manage ML models and AI applications. It brings together tools for data preparation, model training, evaluation, and deployment into one place, so users don’t have to be experts in all of them.
With over 160 foundation models in its Model Garden, including Google’s Gemini models, Vertex AI supports text generation, image recognition and custom ML use cases. It’s designed to streamline workflows and scale AI applications using Google Cloud’s infrastructure.
Google Vertex AI — use cases
Vertex AI is useful for software development processes because the platform helps developers easily integrate AI and GenAI into their apps. It uses GenAI for efficient code generation, debugging assistance, and optimization. Developers can also train custom models or fine tune pre-trained ones for specific use cases like predictive analytics or natural language processing (NLP). With seamless integration into Google Cloud services like BigQuery and tools like AutoML, Vertex AI simplifies complex workflows and accelerates building intelligent software.
Google Vertex AI — pricing
The prices for Google Vertex AI depend on the tools that are used, like generative AI, AutoML models, and custom model training. Details about the pricing are available on their website.
Replit Ghostwriter
Replit Ghostwriter is a cloud-based AI code assistant that helps enginners write optimized code faster. It offers capabilities like Complete Code, Generate Code, Transform Code, and Explain Code. It’s a GenAI tool supporting 16+ languages like HTML, CSS, Python, Ruby, JavaScript, TypeScript, C++, Jason and more. Also, it has Replit’s online IDE with it, which makes the platform ideal for teamwork where members of cross functional teams can edit the code together at the same time.
Replit Ghostwriter — use cases
Replit Ghostwriter helps and speeds up the coding process. It might be especially useful for developers who need the coding assistance and want to automate routine tasks, optimize the code, and quickly learn new programming concepts. It can generate complete code for particular tasks based on user inputs and provide intelligent suggestions in real time. Replit Ghostwriter enables a fast transformation and refactoring of large blocks of code and is one of the alternatives to GitHub Copilot.
Replit Ghostwriter — pricing
The tool is available as part of Replit’s services, with pricing starting at $10/month for 1,000 cycles.
DataStax tools
DataStax offers multiple tools to ease the development of AI applications, including those relying on Large Language Models (LLMs). Its flagship database platform, Astra DB, powered by Apache Cassandra, now has Vector Search as a feature meant to facilitate semantic searches in unstructured data sets.
This capability allows organizations to employ embeddings to facilitate context-aware searches, making it easier to build advanced generative AI models like chatbots and recommendation systems.
DataStax — use cases
With Vector Search, developers can create GenAI applications and LLM agents by combining embeddings, vector stores, and genAI models on Cassandra or Astra DB. Use cases include creating AI-powered chat systems, real-time recommendation systems, and intelligent search systems. DataStax also supports integration with third party tools like LangChain and Google Cloud services, making it easy to develop generative AI applications.
DataStax — pricing
Depending on the DataStax products, prices vary. Vector Search has two pricing options: pay-as-you-go and annual. The DataStax website also provides a pricing estimator to adjust the price to specific needs.
Google Gemini
As a multimodal AI platform developed by Google, Gemini can process and create content in various formats like text, images, audio, video, and code. It integrates with Google services, provides information in real time, and includes Gemini Code Assist—a powerful tool designed to build applications faster and with higher quality. While Gemini offers powerful features, it’s not the only player in the game. See how it compares to Microsoft Copilot.
Google Gemini — use cases
Gemini Code Assist allows developers to generate functional code in more than 20 programming languages and easily integrates with popular IDEs, such as Visual Studio Code and JetBrains IDEs.
This generative AI tool for software development generates code snippets matched to customers’ individual requirements, provides clear explanations, and performs unit tests automatically to assure code quality. It simplifies tasks like translating code between languages and optimizing code to make it work faster and more efficiently.
Gemini by Google — pricing
Gemini has a free plan and the paid one that offers more features:
- Advanced: $19.99/month per user
- Business: $24/month per user
- Enterprise: $36/month per user
- Gemini Code Assist: $19/month per user with a 1-year commitment
Snyk—The AI-powered developer security platform
Snyk is a AL-powered platform for developers that allows for securing applications while coding. It helps find and improve security weaknesses and vulnerabilities. The platform offers many tools and solutions that help software engineers secure code, avoid vulnerable dependencies, reduce risk for the business and many more.
Snyk — use cases
The Snyk platform can be used as a security expert. Developers can integrate tools offered by Snyk to perform security checks and fix any vulnerabilities. Snyk can also scan an existing code to check for security flaws.
Snyk — pricing
Snyk is free for individual users, and offers a team plan from $25 per month.
Generative AI tools for development and current workflows: Challenges
Despite a strong interest of software engineers in adopting GenAI tools and platforms into current business systems and workflows, the seamless integration process might be challenging. Businesses and developers can have to deal with:
- Privacy and security concerns: According to the SolarWinds 2024 IT Trends report, privacy (48%) and security risks (43%) are the most common problems. It’s not surprising—GenAI processes a lot of data, often sensitive and confidential. Companies must have strong data protection in place, like encryption, access controls, and regular security audits.
- Compatibility: Getting GenAI tools to work with current infrastructure can be challenging. Organizations may need to upgrade systems, check APIs match, and make sure smooth data flows, which takes time and requires effort.
- Data quality: Generative AI models rely heavily on large datasets to learn and generate outputs. Incomplete, biased or sparse data can lead to distorted and unreliable results. In fact, only 43% of businesses think that their databases can meet AI’s high demands.
- Regulatory compliance: AI regulation is complicated and varies by area and industry. The variety of laws like GDPR and CCPA require businesses to handle personal data with care and be transparent about how they use generative artificial intelligence.
- Skill gaps and training: To integrate generative AI, employees and developers need to be skilled in AI and machine learning. Teams cannot have that expertise so companies need to invest in training on modern technologies —even if trainings are expensive and time-consuming.
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Final thoughts
GenAI platforms and tools provide software developers with powerful solutions for code optimization, error correction, and resolving performance issues. They become AI-powered coding assistants able to change repetitive programming tasks into efficient workflows.
Thanks to that, development teams can enhance productivity, focus on important tasks and innovation. By using GenAI for software development as assistants, software development companies and businesses improve code quality and develop faster, gaining a competitive edge.
FAQ
How to use AI in software development?
AI and GenAI can be a great assistant in software development. Artificial intelligence can be used to automate tasks, such as code generation, testing or debugging. Developers can shift their focus to more complex tasks when AI handles the simple, repetitive ones. Moreover, AI can detect potential bugs and problems in the code structure to help developers fix them quickly. Some generative AI tools for developers can also generate ideas, concepts or functionalities that can be added to the product. This is a great asset for software development teams if used correctly.
Will software developers be replaced by AI?
Currently, experts claim AI isn’t capable of replacing programmers completely. It can be used to assist the development team, but it’s not good enough to replace them. Artificial intelligence has specific limitations and can’t handle complex projects or problem-solving tasks. It’s a tool that often lacks context to perform well and its main goal should be to assist developers.
What are the best generative AI tools for automating code generation?
GitHub Copilot and OpenAI Codex are two of the best GenAI tools for automating code generation. They both offer smart code suggestions and features. Other options worth considering include Tabnine and CodeT5, which provide intelligent autocompletion and language-specific tasks.
What are the limitations of generative AI tools and platforms in software development?
Generative AI is a helpful tool in software development, but it has its limits. It doesn’t truly understand context or make decisions—it just predicts patterns based on past data, which can lead to mistakes or confusing results. Since it’s trained on existing information, it can also reflect outdated practices or built-in biases. Sometimes its suggestions are unpredictable, and figuring out why it gave a certain output isn’t always clear. That’s why developers still need to double-check its work and not rely on it too heavily.
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