There’s a common belief that AI solutions can only be developed in Python. This has been true for a long time since Python's extensive libraries and frameworks have made it the go-to language for AI development. However, the programming landscape has evolved, and nowadays, we have other languages like Java, C++, and JavaScript that are also viable options for AI solutions. 

But why does this matter to your business? With cloud solutions, microservice architecture, and modern technologies enabling multiple programming languages, this question is more relevant than ever. The key takeaway is that the choice of programming language can have long-term implications for your AI project, affecting everything from maintenance to performance. You do not need to rely on the technical side to select the correct option. Consider your business aspects and rely on them while weighing the pros and cons.

Let’s dive into the details of the main reasons you should take into account while selecting the best technical solution for your business:

Core business factors in AI language selection

When it comes to the selection of programming language for AI development, it’s worth evaluating some core aspects to align your AI strategy with your business objectives and infrastructure. Let’s take a look at 3 main parameters:

1. Product maintenance

Focusing solely on the development phase without considering long-term maintenance can lead to increased costs and operational challenges. Projects that span multiple languages, such as Python for AI components, Java for backend operations, and JavaScript for the front end, complicate the maintenance process. This complexity can lead to higher support costs and slower updates. A unified approach, using a single primary language across the entire application, often results in smoother maintenance and lower long-term costs.

2. Performance

Python's ease comes with a trade-off in performance. For applications where speed and efficiency are paramount, Python might not be the best choice. In such instances, languages known for their speed, like C++ or Java, are often better suited. Transferring AI components to these languages can significantly enhance performance, particularly for computationally intensive tasks like real-time data processing or machine learning model execution.

3. Code reusability

The use of multiple programming languages within the same project can lead to inconsistencies and integration challenges. For example, if AI components are developed in Python, but other critical parts of the system utilize Java, this can create bottlenecks and dependencies that slow down the development process. Integrating Python with Java ORM frameworks, for example, might introduce latency issues and necessitate additional synchronization efforts whenever updates occur.

Genuine’s real-life example: Integrating AI into a Java-based application

One of our clients needed to incorporate a question-answering system into their Java-based application to leverage an existing knowledge base effectively. This integration presented several developmental approaches:

  • Developing a microservice in Python

While Python remains a preferred choice for its extensive AI libraries, like TensorFlow and PyTorch, integrating a Python-based microservice into a Java application introduces potential integration and performance issues. The richness of Python's AI capabilities often comes with the cost of slower runtime performance and complex cross-language integration.

Opting for AI solutions provided by cloud platforms like AWS or Azure offers advantages in scalability and maintenance. However, these services can also pose challenges such as vendor lock-in and limited customization, which might not align with every business’s operational requirements. Services that index and manage AI functionalities, like LlamaIndex, provide frameworks for integrating AI capabilities but need careful consideration regarding data sovereignty and operational flexibility.

  • Custom Java solution: Llama3.java and Langchain4j

The decision to develop a custom solution in Java was driven by the need for seamless integration and performance optimization. Using Java-based AI frameworks like Llama3.java and Langchain4j, we developed a system that operates cohesively within the client's existing infrastructure. These frameworks support the full utilization of Java's capabilities, aligning closely with the core application's architecture and avoiding the pitfalls of multi-language development. This approach not only simplified maintenance but also optimized the system's responsiveness and scalability.

Why to use Langchain4j? 

We encourage you to make this decision once you assess your business requirements and expectations.  For businesses operating with Java-based infrastructures, Langchain4j offers an efficient way to incorporate AI features without the need to switch languages or build complex integrations. 

Let’s go through some advantages of using Langchain4j:

  • Ease of swapping providers. AI solutions are rapidly advancing, and Langchain4j simplifies the process of integrating or swapping LLMs and models for vector extraction, enabling seamless and scalable AI applications. For instance, you can quickly switch from OpenAI's GPT to a local LLaMA model for language understanding tasks.

  • Seamless integration with web technologies. Langchain4j offers seamless integration with web technologies, supporting plain Java applications as well as modern frameworks like Quarkus and Spring Boot. This makes it ideal for building scalable AI-powered solutions across diverse Java-based ecosystems.

  • Reduced server load. Java applications are known for their robust speed and efficiency compared to Python, making them an excellent choice for performance-critical applications like real-time chatbots. The optimized execution in Java reduces server load, ensuring faster response times and smoother user experiences.

  • Flexibility and accessibility. With Langchain4j's ability to integrate with different tools and AI models, you become flexible in customizing your needs, while staying within the Java ecosystem. Moreover, if the company already has Java-based systems, there are no obstacles to scaling existing infrastructures by implementing AI features.

  • Adaptation to various use cases. Modular design supports a wide range of frameworks, including plain Java for creating streamlined solutions, Quarkus - for building lightweight microservices, and Spring Boot - for enterprise-scale systems. With such options, developers can select the framework that best fits their use cases.

  • Real-time user experience. Langchain4j enhances user experiences by enabling such possibilities as providing instant feedback, real-time chatbot responses, and timely data analysis. You do not need to switch between languages since everything is located within the Java ecosystem. This helps mitigate the latency issues, ensuring smooth and seamless user experiences.

  • Community & innovation. Langchain4j is an open-source framework designed to empower the Java community with tools for building advanced AI solutions. As an open-source initiative, Langchain4j is accessible to everyone and encourages collaboration and innovation within the Java ecosystem.

icon mail icon mail

X

Thank you for Subscription!


When to use Python for AI development?

Despite the advantages of other languages, Python still excels in rapid AI prototyping and big data processing. Its extensive libraries facilitate quick testing and iteration of AI models. There are several areas where Python is still a game-changer:

Data preparation and management

  • Data compression. Before the creation of AI pipelines, it is worth optimizing data storage and processing. With Python’s extensive list of libraries, such as fast parquet and h5py, you can be equipped with effective tools to cope with this task. 

  • Data cleaning. Such Python libraries as Pandas and Spark ensure high data quality by removing empty and unnecessary fields and filtering out fake data.  

  • Data anonymization. It is crucial to protect user privacy during model training. Thanks to PySyft, Python enables data anonymization which prevents private information leakage.

AI prototyping

  • Developing AI models from scratch. Python remains the top choice for AI prototyping because of its rich ecosystem of libraries and frameworks like PyTorch, TensorFlow, and Scikit-learn. It enables rapid development and iteration, making it ideal for testing and validating AI concepts before scaling them.

  • Finetuning & integration of the existing model into your pipeline. Python simplifies the fine-tuning of AI models to enhance their quality with specific datasets and integrates these models into larger systems without the need to rewrite them in other languages. For standard deployment, tools like ONNX are used for model serialization, or the models are wrapped as microservices.

ETL processes

Python's versatility and extensive range of data processing libraries make it highly effective for handling large datasets, especially in the exploratory phase of AI projects: 

  • Batch processing. Tools like Pandas for smaller datasets and Dask or PySpark for larger, distributed datasets facilitate efficient batch processing. Apache Airflow can be used to orchestrate these workflows.

  • Streaming. For real-time data processing, Python supports tools such as Confluent's Kafka Python and Twisted for efficient streaming capabilities. Additionally, for more intensive big data operations, libraries like PySpark can be used for distributed data processing.

Unified Python infrastructure

If your entire infrastructure is built on Python, it makes sense to move forward with Python for AI development. And here is the answer to your “why”?

So the first advantage relates to a single-language infrastructure that reduces integration issues and facilitates development cycles while minimizing costs. Moreover, using Python across backend development and AI projects simplifies the technology stack. 

Why Python is a good choice for Spark development?

There are assumptions that Scala and Java are the best languages for Spark development since Spark was built on these languages and benefits from their native integration. However, using Python with PySpark allows developers to quickly adapt and integrate Spark into projects, offering benefits such as simplicity, cost efficiency, and enhanced capabilities.

With Python’s diverse libraries and ease of use, Spark development is often more efficient with Python. Below are concise reasons why: 


Important!  Due to its inherent performance limitations, Python might have a slower speed compared to Scala or Java. That’s why PySpark is valuable here since it can mitigate Python’s shortcomings.  It allows Spark to seamlessly operate with Python while leveraging Spark’s computing capabilities within Python's ecosystem.

Python vs. Langchain4j

So ultimately, what to choose? What’s better for you business?

We don’t see these two options as being in competition with each other; their effectiveness depends on your business requirements and existing systems.

Let’s summarize.

If your product or platform infrastructure is built in Java, Langchain4j is the most suitable solution. It maintains high performance, offers scalability, and can easily support enterprise-level applications.

As for Python, it remains the king for its versatile data processing libraries, making it highly efficient for rapid prototyping and ETL processes.

If you need AI consultation to develop an effective, secure, and easy-to-maintain tech stack for your AI project, contact our AI experts. Our AI services are designed to drive your business growth and accelerate success.