Generative AI Tech Stack for 2025: What It Takes to Build AI Apps

Social media has recently been flooded with the trend of creating Ghibli-style AI art following the launch of a new image-generating feature on GPT-4. As we all know, the technology behind the success of this trend is AI, but few people know that it is specifically generative AI (gen AI) - a specialized subset of artificial intelligence that focuses on creating new content (e.g., text, images, audio, video, code) rather than just analyzing data or making predictions.

Businesses nowadays are pouring money into generative AI apps as they see faster ROI, broader applications, and more competitive advantage compared to traditional AI. However, this is an intensely contested market. The challenge of surviving in gen-AI doesn’t come from simply connecting to the latest foundation model as anyone can do that. What separates long-term winners from short-lived experiments is the tech stack behind the AI application.

A well-designed generative AI tech stack is the backbone of any reliable AI system, ensuring scalability, cost efficiency, and adaptability to the next wave of innovation. So, how to choose the right tech stack for your next AI project? Let’s dive deeper into the topic through this article.

10 Core Components of a Robust Generative AI Tech Stack

There is no official definition of the AI tech stack. Some keep it very simple with only 3 layers including infrastructure layer, model layer, and application layer. Some expand it to 5 layers or more by adding data layer or MLOps for example. However, in this section, we will break it into 10 different components which cover almost everything a real-world AI project needs, regardless of its layer, to simplify the process of choosing the right stack.

Below is a summary table for reference. Let’s take a look before going through some key components.

In case you still don’t know where to start or what stack to choose, every section below will include a comparison table that highlights your best-fit scenarios.

1. Programming Languages

Programming languages are the foundation of every AI project because they are what developers use to actually write applications. As the choice of language can ultimately impact the performance, scalability, and integration of AI apps, it should not be a randomly picked choice.

In the world of generative AI development, Python is by far the most popular one thanks to its rich ecosystem of machine learning libraries like TensorFlow, PyTorch, and Scikit-learn, and community support. We obviously have many other alternatives like JavaScript and TypeScript. However, they are better suited for creating web or mobile interfaces that connect users to AI/machine learning models. For research-heavy or data-intensive projects, languages like R or Julia might be chosen for their strengths in data analysis or numerical computing.

2. Generative Models (The Brain)

A generative model is the heart of any gen-AI system that allows an app to generate creative content as desired. The recent surge in generative artificial intelligence has introduced more and more new AI models, in addition to large language models (LLMs). Most of these models come pre-trained, but they can be fine-tuned or customized for specific use cases, making them highly versatile engines of creativity.

However, it is worth mentioning here that not all models are open-source models. Rather, the majority are closed-source models, especially the ones developed by big tech companies. You can consider using both in such a situation. For example, open models are often used for private or specialized workloads, while closed APIs will be used for rapid features.

Depending on your AI model usage needs, image generation, for example, the provider chosen will follow the table below:

3. Data (The Fuel)

Generative AI cannot function without data. More than just unstructured data, AI models need clean and diverse datasets to produce reliable AI. To achieve this, you need pipelines not only to store data but also to clean, label, chunk, embed, and retrieve it in real time.

In generative AI technology, data is used in multiple distinct ways, including training data, knowledge base data, evaluation data, and feedback data. Each of these requires different tools, which add complexity to the generative AI stacks that revolve around data. For example, modern AI projects rely on databases and data warehouses such as Snowflake and Databricks to store and organize information, while vector databases like Pinecone and Weaviate give models “memory” by allowing them to recall and search knowledge quickly.

For specific data-related stages, which tools should you use? This table may give you some clues.

4. Tools & Frameworks (The Connectors)

While not visible to users, tools and frameworks act like the plumbing of the stack that makes everything run smoothly. They are essential to connecting the AI model to your data, user applications, and workflows so they actually work in real products. Think of them as the “middleware” that AI developers use to link all the moving parts of generative AI development.

Tools and frameworks are especially useful in situations where you want to save time and simplify tasks like connecting to databases. However, they can sometimes add complexity you don’t need. If you are just getting started, don’t do anything big, but begin with a basic setup: a framework like LangChain or LlamaIndex, a simple monitoring tool, and one vector database. You can then add more advanced tools for caching and optimization in later stages as your project grows.

5. Cloud Services (The Power Source)

Generative apps require huge amounts of data, which are computationally heavy to operate. Most businesses cannot afford to build massive server farms to run such models. That is why developers prefer to use cloud services. By taking advantage of storage space on the cloud, you save costs and have the flexibility to manage your data with just an internet connection.

For reference, you can pay attention to names like AWS, Microsoft Azure, and NVIDIA AI. Providers like AWS, Microsoft Azure, and Google Cloud offer on-demand computing power, storage, and GPUs/TPUs, making it possible to train, fine-tune, and deploy AI models at scale. Specialized platforms such as NVIDIA AI and Hugging Face Hub provide pre-built environments and model hosting options.

6. APIs & Integrations

Imagine you build a generative AI chatbot. A framework like LangChain helps wire it internally. However, have you ever wondered how your application can connect with real users out there? The answer lies in APIs and integration.

APIs (Application Programming Interfaces) are like bridges that let your AI models communicate and connect with other applications. This is how generative AI capabilities move from isolated demos to practical business applications and actually work where the audience is. With the help of model APIs and integration tools, you can instantly access powerful AI features and ensure their compatibility with existing workflows without building anything from scratch. Your generative chatbot is no longer just technically impressive, but has become adopted in that way.

Some recommendations for you include:

• Model APIs: OpenAI API, Anthropic API (Claude), Hugging Face Inference API, Cohere API.
• Integration Platforms: Zapier, Make (Integromat), Retool AI - connect AI to business workflows.

7. User Interface & Experience (UI/UX)

The fiercely competitive market does not allow businesses to succeed with just a smart AI model. Even if you have the most advanced model under the hood, without a good UI/UX, users won’t use it. Not as specialized as the AI stacks above, the technologies used for user interfaces are similar to those used in regular mobile apps and web apps.

8. Security, Safety & Compliance

As businesses adopt generative AI, trust becomes a major concern. Making sure an AI system is trustworthy and ethical not only helps your business align with standards such as GDPR or HIPAA, but it’s also a way to attract and retain users. Users won’t use a generative AI app that doesn’t guarantee their privacy. That’s the truth.

Fortunately, there are plenty of tools to help you in such a process. They encompass policies and processes for overseeing the lifecycle of AI models, as well as legal regulations, ethical principles, and organizational goals.

9. Monitoring & Optimization

Generative AI doesn’t stop evolving once deployed. As its model outputs can vary, the AI model needs constant supervision to continuously measure accuracy and spot problems such as hallucinations, bias, or performance drift over time. There are many tools and platforms that can help you with this. For example, tools like Langfuse or LangSmith capture every prompt and response to make it easier to debug issues, while evaluation libraries like Ragas or DeepEval test whether answers are factually correct and relevant.

In addition, for other tasks in the monitoring and optimization category, you can use more of the above tools.

10. Collaboration & Version Control

Building a gen-AI app is not a solo effort. AI projects often involve teams of developers, data scientists, designers, and business users, which means you need ways to coordinate work, track changes, and keep projects reproducible. If you’re looking for tools to help streamline project collaboration, the tools below can help in keeping track of code, data, models, and prompt versions so nothing gets lost.

Choosing the Right Tech Stack for Your Gen-AI Solution

Rather than developing the “best AI model”, the challenge of surviving in gen-AI market depends more on how intelligently the tech stack is built and managed. However, with such great diversity, it’s understandable that developers may feel lost in choosing the right tech stack for generative AI development.

As your reliable software development partner, Orient Software is here to help you maximize your AI project potential. With experience working with clients across the globe in AI development, we specialize in building AI-driven applications that align natural language processing capabilities with consistent and seamless integration with your data, workflows, and customer-facing platforms - all tailored to your needs. Whether you are exploring enterprise-grade AI assistants or simply needing advice from industry experts, don’t hesitate to get in touch with us for end-to-end AI development and consulting services.