Want to get started with AI, but confused about which AI programming languages to choose from? We understand it gets confusing to choose one among the pool of so many languages and platforms. Thus, we have compiled this blog to help you choose the best AI tech for your specific project needs.
You will get a quick decision matrix table at the end to simplify the complexity of AI programming languages and choose the right one for your project requirements.
Let’s dive right into it!
The Role of Programming Languages in AI
First thing, we need to understand what exactly AI models are, before understanding the role played by programming languages in AI.
Generally speaking, AI models or artificial intelligence models are programs that detect specific patterns using a collection of data sets, and in return, they send the best reasonable output as per our commands.
Here, to give commands in a way that the computer machines understand, programming languages come into play. When we are developing an AI model, we need to give the computer very clear instructions. AI Programming languages act as a medium allowing the model to communicate between the computer and the AI model.
Different AI programming languages in practice directly affect things like how quickly, how well, and how easily you can build an AI system.
For example, Python is loved by a lot of developers, but it’s not as powerful in terms of performance as compared to C++. In fact, libraries like TensorFlow or NumPy are made by the C++ programming language.
Not to mention, the correct choice of programming language for your project directly impacts model efficiency, development speed, maintainability, and scalability.
Overview of Top AI Programming Languages
Here are the current top AI programming languages that are being used by top tech companies globally in 2025, to build robust AI systems.
Note: The AI programming languages mentioned below are based on their popularity and our recommendation from top to bottom.
#1 – Python – AI Programming Languages
| Category | Details |
| Launched In | 1991 |
| History | Developed by Guido van Rossum as an easy-to-read, high-level scripting language |
| Famously Used By | Google, Facebook, OpenAI, Netflix, Dropbox, Microsoft, Spotify, Amazon |
| Features | – Interpreted, dynamically typed, and high-level– Simple syntax and large standard library– Extensive ecosystem for data science, AI, web, and automation– Great community and third-party library support |
| Why for AI | – Dominant programming language for fields like AI research and development– Offers huge collection of libraries: TensorFlow, PyTorch, scikit-learn, Keras, etc.- Easy to prototype deep learning models – Allows quick deploy of AI models |
| Best Frameworks/Libraries | – TensorFlow, PyTorch, Keras – Deep learning- scikit-learn – Traditional ML- NLTK, spaCy, Transformers – NLP- Pandas, NumPy, Matplotlib – Data processing |
| Pros | – Easiest entry point for AI- Strong ecosystem and support- Great for both research and production |
| Cons | – Slower than compiled languages- Not ideal for memory- or CPU-intensive real-time systems |
| Best Sources to Learn | – Python.org– Fast.ai– DeepLearning.ai (Coursera)– Kaggle Courses |
#2 – JavaScript – AI Programming Languages
| Category | Details |
| Launched In | 1995 |
| History | Created by Brendan Eich at Netscape; originally designed for interactive web content |
| Famously Used By | Google, Netflix, PayPal, Airbnb, Adobe (primarily for web and browser-based AI) |
| Features | – Lightweight and interpreted programming language with frist-class functions- Dynamically typed, runs in browsers and Node.js– Prototype-based, multi-paradigm, single-threaded, dynamic language- Event-driven and asynchronous by design- Powers both frontend and backend (with Node.js) |
| Why for AI | – Ideal for browser-based AI- Interactive ML models, – Fits suitable for AI in UI/UX programming- Supports real-time visualization, chatbots, and in-browser inference- Fast-growing ecosystem and rapid support available from community |
| Best Frameworks/Libraries | – TensorFlow.js – Deep learning in the browser- Brain.js – Neural networks- ml5.js – Easy-to-use high-level AI API built on TensorFlow.js |
| Pros | – Excellent for real-time AI apps on the web- Easy deployment via web browser- Good for AI-driven UI/UX |
| Cons | – Not suitable for large model training- Slower than native languages for heavy computation |
| Best Sources to Learn | – TensorFlow.js Docs- ml5.js Docs– JavaScript.info |
#3 – Java – AI Programming Languages
| Category | Details |
| Launched In | 1995 |
| History | Currenlty Oracle owned, it was developed by James Gosling at Sun Microsystems |
| Used by Companies | IBM, Twitter, Amazon, LinkedIn, Google (for Android and enterprise systems), Salesforce |
| Features | – Compiled, statically typed, object-oriented- Runs on the JVM (Java Virtual Machine)- Known for portability (“write once, run anywhere”) and performance |
| Why for AI | – Suitable for enterprise-level AI solutions – Capable of handling big data ecosystems (e.g., Apache Hadoop, Apache Spark with Java APIs)- Strong support for multi-threading, scalability, and performance- Used in robotics, IoT, and financial AI systems |
| Best Frameworks/Libraries | – Deep Java Library (DJL) – Native deep learning library for Java- Weka – Popular for classic machine learning algorithms- Deeplearning4j (DL4J) – Distributed deep learning on JVM- Neuroph, MOA – Lightweight frameworks for neural networks and stream data analysis |
| Pros | – Good for large-scale enterprise AI applications- Strong typing and maintainability- Easy integration with big data tools and backend APIs |
| Cons | – Verbose syntax compared to Python- Fewer AI-specific libraries and community support than Python- Slower experimentation cycles |
| Best Sources to Learn | – Deep Java Library (DJL) Docs- Deeplearning4j Quickstart- Weka Official Site- Java AI with DL4J – YouTube (Amigoscode)– Oracle Java Tutorials |
#4 – C++
| Category | Details |
| Launched In | 1985 |
| History | Developed by Bjarne Stroustrup as an extension of C with object-oriented features |
| Famously Used By | NVIDIA, Intel, Tesla, Adobe, Microsoft, IBM |
| Features | – Compiled, statically typed- object-oriented- Supports low-level memory control– Multi-threading and real-time performance capabilities- Used to build AI libraries themselves |
| Why for AI | – Common in AI engines, robotics- Embedded AI, and performance-critical systems- Core of Python-based AI libraries like PyTorch and TensorFlow |
| Best Frameworks/Libraries | – Dlib, Shark ML, FANN – Traditional ML- OpenCV – Computer vision- TensorRT, ONNX Runtime – Model inference at scale |
| Pros | – High speed and efficiency- Suitable for real-time AI and edge deployments |
| Cons | – Verbose syntax- Slower development speed compared to Python |
| Best Sources to Learn | – Cplusplus.com- OpenCV Docs- freeCodeCamp C++ for AI (YouTube) |
#5 – R – AI Programming Languages
| Category | Details |
| Launched In | 1993 |
| History | Developed by Ross Ihaka and Robert Gentleman in New Zealand; derived from the S language, aimed at statistical computing and graphics |
| Famously Used By | Facebook, Google, Microsoft, Genentech, Bank of America (especially in biostatistics, finance, academia, and data science) |
| Features | – Interpreted, dynamically typed, designed for statistical analysis– Excellent data visualization (ggplot2, lattice)- Functional programming support- Rich ecosystem for data cleaning, exploratory data analysis, and model building |
| Why for AI | – Powerful for statistical machine learning, predictive modeling, and NLP– Frequently used in bioinformatics, finance, and social sciences AI research– Strong in data preprocessing and model explainability |
| Best Frameworks/Libraries | – caret, mlr3 – Unified interfaces for ML- randomForest, xgboost, e1071 (SVM) – Classical ML- keras, tensorflow – Deep learning (via R interfaces)- tm, quanteda – NLP- shiny – AI-driven web apps |
| Pros | – Best suited for statistical AI and model diagnostics– Strong visualization and reporting tools- Popular in academic research and healthcare AI |
| Cons | – Slower than compiled languages- Less preferred for deep learning or production AI pipelines compared to Python |
| Best Sources to Learn | – R for Data Science (Book)- CRAN Machine Learning View- DataCamp ML with R Track- KDnuggets R Tutorials |
#6 – Go (Golang) – AI Programming Languages
| Category | Details |
| Launched In | 2009 |
| History | Developed at Google by Robert Griesemer, Rob Pike, and Ken Thompson to simplify system-level programming with built-in support for concurrency, performance, and simplicity |
| Famously Used By | Google, Uber, Dropbox, Twitch, SendGrid, Dailymotion (infrastructure, backend AI services, real-time systems) |
| Features | – Statically typed, compiled, and concurrent programming language- Fast compilation and execution, close to C/C++ performance- Built-in support for goroutines and channels for concurrency- Simple syntax and easy maintainability- Strong standard library and excellent tooling (e.g., go fmt, go test, go run)- Native support for networked and distributed applications- Easily deployable as a single binary with no external dependencies |
| Why for AI | – Gaining popularity in production AI microservices (e.g., model serving, data APIs, AI monitoring tools)- Used to build high-performance concurrent AI services (e.g., inference APIs, logging pipelines)- Great for edge AI and lightweight IoT AI apps where speed, size, and concurrency matter- Used with external AI models via gRPC, REST APIs, or bindings with C/C++ libraries- Growing ecosystem for machine learning in production, though not a core language for model training |
| Best Frameworks/Libraries | – Gorgonia – For building and training neural networks in Go- GoLearn – Traditional machine learning algorithms (classification, clustering, regression)- Fuego, Evoli, HeatonML – Lightweight or experimental AI tools- TensorFlow Go – Go bindings for TensorFlow inference (limited training support) |
| Pros | – Fast, compiled, and highly concurrent- Excellent for building scalable AI services and APIs– Low memory footprint—suitable for edge computing– Easy deployment and maintenance |
| Cons | – Limited high-level AI libraries compared to Python- Not well-suited for deep learning model development or GPU-heavy training– Less community support for AI-specific use cases |
| Best Sources to Learn | – Gorgonia Documentation– GoLearn GitHub Repo– Official Go Tour– Go for AI – Practical AI Microservices (YouTube)– Go Programming Language Book (Alan A. A. Donovan, Brian Kernighan) |
#7 – Julia – AI Programming Languages
| Category | Details |
| Launched In | 2012 |
| History | Developed by Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B. Shah at MIT with the goal of combining the speed of C with the usability of Python, R, and MATLAB |
| Famously Used By | NASA, Aviva, BlackRock, Moderna, IBM, Capital One, and research labs in scientific computing, finance, and pharmaceuticals |
| Features | – Compiled, high-performance, dynamically typed– Just-In-Time (JIT) compilation via LLVM for C-like speed– Multiple dispatch and strong numerical computing support- Native support for parallel and distributed computing– Built for scientific, numerical, and mathematical computing– Syntax similar to MATLAB/Python, but optimized for performance-heavy tasks |
| Why for AI | – Ideal for AI in scientific computing, finance, and physics– Great for large-scale numerical simulations, differentiable programming, and mathematical modeling– Faster than Python in many numeric AI workloads due to JIT and static type inference- Used in machine learning research, physics-informed neural networks, and AI for scientific modeling– Enables model training and optimization natively, without switching between dev and deployment languages |
| Best Frameworks/Libraries | – Flux.jl – The most popular deep learning framework in Julia- MLJ.jl – Interface to many traditional ML models (similar to Scikit-learn)- Knet.jl – High-performance deep learning- DifferentialEquations.jl + SciML – For scientific machine learning- Zygote.jl – Automatic differentiation- Metalhead.jl – Pretrained vision models |
| Pros | – Performance near C, with high-level syntax- Native GPU support (e.g., with CUDA.jl)- Great for researchers, scientists, and academic AI– Smooth integration with Python, C, R, and even MATLAB- Strong support for custom AI models, especially where differential equations are involved |
| Cons | – Smaller ecosystem than Python or R- Less mature in production environments- Fewer resources, libraries, and community support for web/backend AI use cases |
| Best Sources to Learn | – JuliaAcademy – Free Courses by Julia Computing– Flux.jl Documentation– MLJ.jl Docs- Official Julia Documentation- Deep Learning with Flux – GitHub |
#8 – Swift – AI Programming Languages
| Category | Details |
| Launched In | 2014 |
| History | Developed by Apple Inc. as a modern, safer, and faster replacement for Objective-C |
| Famously Used By | Apple, IBM, Lyft, Airbnb (primarily for iOS/macOS app development, including AI-powered apps) |
| Features | – Compiled, type-safe, and memory-safe– Modern syntax, fast execution, and strong integration with Apple ecosystem- Supports protocol-oriented and functional programming– Native support for Swift Playgrounds and Xcode tools- Excellent for mobile AI development |
| Why for AI | – First-class support for on-device AI with Core ML and Create ML– Ideal for building AI-powered iOS/macOS apps (e.g., face recognition, NLP, personalization)- Supports model conversion from TensorFlow/PyTorch via Core ML Tools– Great for real-time, privacy-focused AI experiences |
| Best Frameworks/Libraries | – Core ML – Apple’s native machine learning framework- Create ML – No-code/low-code model training for Swift apps- TensorFlow Swift (experimental) – Swift bindings for deep learning- BNNS, MPS, Turi Create (via Python integration) |
| Pros | – High performance and optimized for Apple silicon- Easy integration with iOS apps- Excellent tools for on-device inference and ML personalization |
| Cons | – Limited outside the Apple ecosystem- Smaller AI community and fewer libraries compared to Python |
| Best Sources to Learn | – Apple Core ML Official Docs– Swift.org– Hacking with Swift – Core ML- Apple Create ML Docs |
#9 – Rust – AI Programming Languages
| Category | Details |
| Launched In | 2010 |
| History | Developed by Graydon Hoare at Mozilla to provide a safe, concurrent, and fast systems programming language |
| Used by Companies | Dropbox, Amazon, Mozilla, Microsoft, OpenAI (for safety-critical and high-performance systems) |
| Features | – Compiled, statically typed, memory-safe (no garbage collector)- Focus on performance, safety, and concurrency– Zero-cost abstractions- Strong support for multithreading– Modern syntax with tooling like cargo, clippy, and rust-analyzer |
| Why for AI | – Ideal for safe and efficient AI infrastructure, e.g., inference engines, embedded AI, and deployment- Used in low-latency AI systems, edge computing, and WASM-based ML– Often used to build backends for AI tools and integrate with C++/Python AI models– Growing interest due to performance and predictable memory usage |
| Best Frameworks/Libraries | – tch-rs – Rust bindings for PyTorch- burn – Modern, modular deep learning framework- ndarray, linfa – For numerical computing and traditional ML- autodiff, rustlearn, leaf (archived) |
| Pros | – Extremely fast and memory efficient- Guarantees thread safety at compile time- Suitable for embedded, real-time, and AI in production environments |
| Cons | – Steep learning curve- Smaller ecosystem for AI/ML- Limited high-level libraries for deep learning and data preprocessing |
| Best Sources to Learn | – The Rust Programming Language Book- Burn Docs– tch-rs GitHub– Linfa Docs– Rust by Example |
#10 – Scala – AI Programming Languages
| Category | Details |
| Launched In | 2004 |
| History | Developed by Martin Odersky to combine functional and object-oriented programming on the Java Virtual Machine (JVM) |
| Famously Used By | Twitter, LinkedIn, Netflix, Airbnb, Coursera (especially in big data and distributed AI processing environments) |
| Features | – Compiled, statically typed, runs on the JVM– Supports both object-oriented and functional paradigms- High compatibility with Java libraries- Strong support for concurrency and immutability– Expressive syntax for math-heavy logic |
| Why for AI | – Ideal for big data + AI projects using Apache Spark MLlib, which is natively written in Scala- Frequently used for streaming AI, recommendation engines, and ETL pipelines– Well-suited for batch training and parallel data processing at scale- Combines functional purity with strong typing, making it good for complex AI pipelines |
| Best Frameworks/Libraries | – Apache Spark MLlib – Scalable machine learning on big data- Breeze – Numerical processing and linear algebra- DeepLearning.scala – Type-safe deep learning DSL- ND4S – N-Dimensional arrays for Scala (uses ND4J backend) |
| Pros | – Powerful for scalable AI on distributed systems– Seamless integration with Java ecosystem– Functional + OOP makes code expressive and clean |
| Cons | – Steeper learning curve than Java- Less suitable for experimental or rapid AI prototyping– Smaller AI community than Python |
| Best Sources to Learn | – Apache Spark MLlib Guide– Breeze Docs– DeepLearning.scala GitHub– Scala Documentation- Scala Exercises |
#11 – Haskell – AI Programming Languages
| Category | Details |
| Launched In | 1990 |
| History | Designed by a committee of academics as a pure functional programming language, named after logician Haskell Curry |
| Famously Used By | Facebook, Standard Chartered, AT&T, GitHub (mostly for research, compilers, and high-assurance systems; less common in mainstream AI production) |
| Features | – Purely functional, statically typed, with lazy evaluation– Powerful type system and type inference– Supports monads, immutability, and higher-order functions– Emphasizes mathematical correctness and referential transparency– Concise, expressive syntax |
| Why for AI | – Excellent for symbolic AI, logical inference, and DSLs for AI modeling– Ideal for AI research, especially in areas like probabilistic programming and knowledge representation– Useful in theoretical AI, genetic programming, and formal verification– Promotes clean and modular code, which helps with AI model correctness |
| Best Frameworks/Libraries | – HLearn – Machine learning in Haskell- AI-haskell – Neural networks and NLP experiments- Hoogλearn – Experimental deep learning- LambdaNet, BayesStack, hBayes – Probabilistic models and inference tools |
| Pros | – Ideal for academic AI, theoretical models, and symbolic reasoning– Promotes safe, bug-free code- High expressiveness and mathematical clarity |
| Cons | – Steep learning curve- Small AI ecosystem and minimal deep learning support- Not suitable for mainstream or production-ready AI solutions |
| Best Sources to Learn | – Learn You a Haskell for Great Good!– Haskell.org Documentation- Awesome Haskell– Haskell for AI GitHub Repos– School of Haskell |
#12 – Lisp – AI Programming Languages
| Category | Details |
| Launched In | 1958 |
| History | Created by John McCarthy at MIT; it is the second-oldest high-level programming language, designed specifically for symbolic computation and AI research |
| Famously Used By | Historically used by MIT AI Lab, NASA, and early AI companies (e.g., Symbolics); now mostly in academic, research, and niche AI fields |
| Features | – Dynamically typed, interpreted, with homoiconicity (code as data)- Powerful macro system– Ideal for recursive, symbolic, and rule-based logic– High support for rapid prototyping and exploratory programming– Minimal syntax and extensibility |
| Why for AI | – One of the first languages designed for AI– Excellent for symbolic AI, expert systems, theorem proving, rule engines, and natural language processing– Allows modifying or generating code at runtime, making it extremely flexible for AI researchers- Still used in AI education, logic-based AI, and concept modeling |
| Best Frameworks/Libraries | – CLIPS (inspired by Lisp) – Expert system shell- AIMA Lisp Code – From “Artificial Intelligence: A Modern Approach”- ACL2, CLOS (Common Lisp Object System)– OpenCog, BioBIKE, Screamer – Symbolic/logic AI |
| Pros | – Extremely flexible and highly expressive– Great for symbolic reasoning and custom rule-based AI systems– Supports metaprogramming and AI domain-specific languages |
| Cons | – Not suited for modern deep learning or GPU-heavy AI- Smaller community and ecosystem today- Limited industry adoption in current AI projects |
| Best Sources to Learn | – Practical Common Lisp (Book)- Common Lisp HyperSpec- AIMA Lisp Codebase– Land of Lisp (Book)– Awesome Lisp |
#13 – Prolog – AI Programming Languages
| Category | Details |
| Launched In | 1972 |
| History | Created by Alain Colmerauer and Robert Kowalski in France and the UK; designed for logic programming and automated reasoning |
| Famously Used By | Early AI labs, academic institutions, IBM, NASA; still used in rule engines, expert systems, and natural language understanding |
| Features | – Declarative logic programming language- Based on facts, rules, and queries– Built-in support for pattern matching and backtracking– High abstraction for symbolic reasoning |
| Why for AI | – Ideal for symbolic AI, knowledge representation, and automated inference– Used in expert systems, chatbots, theorem provers, and rule-based reasoning |
| Best Frameworks/Libraries | – SWI-Prolog – Most popular open-source Prolog implementation- GNU Prolog, SICStus Prolog, TuProlog– ProbLog, Logtalk – Probabilistic and object-oriented extensions |
| Pros | – Excellent for rule-based AI– Compact and human-readable knowledge bases– Automatic goal-directed search via inference |
| Cons | – Not suitable for deep learning or numeric-heavy AI- Difficult to integrate with modern systems- Steep learning curve for non-logical thinkers |
| Best Sources to Learn | – SWI-Prolog Official Docs– Learn Prolog Now!– The Art of Prolog (Book) |
#14 – MATLAB – AI Programming Languages
| Category | Details |
| Launched In | 1984 |
| History | Developed by MathWorks for matrix-based engineering, numerical computing, and simulations |
| Famously Used By | NASA, Boeing, Siemens, Intel, Qualcomm, financial institutions, automotive & aerospace industries |
| Features | – High-level interpreted language focused on matrix and numerical computation– Rich visualization tools- Powerful toolboxes (e.g., Signal, Image, Control Systems)- GUI development & simulations |
| Why for AI | – Excellent for prototype AI systems, mathematical modeling, and control systems– Used in signal processing AI, image processing, and academic AI simulations– Toolboxes for machine learning, reinforcement learning, and deep learning available |
| Best Frameworks/Libraries | – Statistics and Machine Learning Toolbox– Deep Learning Toolbox– Reinforcement Learning Toolbox– Computer Vision Toolbox, Text Analytics Toolbox |
| Pros | – Fast prototyping for engineering AI– Powerful built-in algorithms– Excellent for education, research, and simulation-heavy AI |
| Cons | – Commercial license (expensive)- Less suitable for large-scale production AI systems- Smaller AI developer community vs. Python |
| Best Sources to Learn | – MATLAB AI Docs- MathWorks Deep Learning Tutorials- Coursera: AI with MATLAB |
#15 – Shell/Bash – AI Programming Languages
| Category | Details |
| Launched In | 1989 (GNU Bash) |
| History | GNU Bash was developed by Brian Fox for the GNU Project; used as a command-line interface and scripting language for Unix/Linux systems |
| Famously Used By | All Linux-using organizations (Google, AWS, NASA, Red Hat, etc.) for automation, pipeline scripting, and deployment of AI/ML tools |
| Features | – Command-line scripting for file operations, automation, data pipelines- Supports pipes, filters, text processing, and batch processing– Integrates with tools like Python, R, Docker, SLURM, etc. |
| Why for AI | – Commonly used to automate AI workflows: data preprocessing, model training orchestration, job scheduling on clusters- Powers ML DevOps, data ingestion, log processing– Supports shell-based interaction with AI model APIs, command-line tools, and Dockerized AI environments |
| Best Frameworks/Libraries | – Not applicable (uses system tools and commands)- Frequently integrates with Python scripts, CLI AI tools, Docker, Git, SSH, SLURM, and cronjobs |
| Pros | – Lightweight and portable- Critical for automation and DevOps in AI projects- Helps glue together AI tools and processes |
| Cons | – Not used for actual model development- Limited programming constructs (no native ML/AI libraries)- Prone to silent errors if not written carefully |
| Best Sources to Learn | – GNU Bash Manual- LinuxCommand.org– Shell Scripting Tutorial (GeeksforGeeks) |
Comparing AI Programming Languages: Decision Matrix
TO help you get started quickly, here is a concise decision matrix table helping you find the right AI programming language for your next project.
| Language | AI Focus Area | Strengths | Limitations | Best For |
| Python | General AI, ML, DL, NLP | Rich libraries, easy syntax, huge community | Slower runtime | Research, production AI, prototyping |
| JavaScript | In-browser AI, UI/UX AI | Web-native, real-time interaction | Not for heavy model training | Web-based AI apps, demos |
| Java | Enterprise AI, ML pipelines | Scalable, portable, stable | Verbose, slower prototyping | Production-grade AI, backend systems |
| C++ | Embedded AI, real-time systems | Fast, memory-efficient | Complex syntax, slower development | Edge AI, robotics, performance-critical AI |
| R | Statistical AI, analytics | Excellent for stats, data viz | Less suited for DL, smaller ecosystem | Academia, healthcare, finance AI |
| Go | AI microservices, backend infra | Fast, simple, good concurrency | Limited ML libraries | Production APIs, real-time AI infrastructure |
| Julia | Scientific AI, numerical modeling | Near-C speed, math-first design | Small community | Scientific research, simulation AI |
| Swift | On-device/mobile AI (iOS/macOS) | Core ML integration, fast execution | Apple ecosystem only | iOS AI apps, privacy-preserving AI |
| Rust | Safe systems AI, embedded inference | Memory-safe, fast, thread-safe | Steep learning curve | AI at the edge, backend model inference |
| Scala | Distributed AI, big data ML | Functional + OOP, Spark ML support | Verbose, smaller AI community | Scalable ML pipelines (Spark, Kafka) |
| Haskell | Symbolic AI, theorem proving | Pure functional, clean abstractions | Not for DL, small ecosystem | Research, logic-based AI |
| Lisp | Symbolic/logic AI, DSLs | Code-as-data, meta-programming | Dated syntax, niche usage | Expert systems, AI language modeling |
| Prolog | Rule-based/symbolic AI | Built-in inference & logic handling | Not suitable for ML/DL | Knowledge-based AI, chatbots, theorem provers |
| MATLAB | Academic/engineering AI | Toolboxes, visualization, simulations | Paid, not for scale | AI in engineering, simulations, control systems |
| Shell/Bash | AI automation & DevOps | Lightweight, great for scripting | Not for AI logic or modeling | Orchestrating AI workflows, data prep pipelines |
Selecting the Right AI Programming Languages for Your AI Project
Selecting the right AI programming language varies based on your project needs. If the chosen AI programming language is right, it will significantly help you avoid complexity, development delays, and scalability scope issues in the future. Thus, here is a quick guide for what AI programming language suits best for different types of AI projects.
1. Define the AI Problem Type
Different types of AI problems require different capabilities, such as:
| AI Category | Language Fit | Reason |
| Machine Learning (ML) | Python, R, Java, Julia | Python has rich ML libraries; R is strong in statistical modeling; Java/Julia for performance |
| Deep Learning (DL) | Python, Julia, C++, Swift | Python (TensorFlow, PyTorch); Julia for numeric performance; C++/Swift for deployment |
| Natural Language Processing (NLP) | Python, Java, Lisp | Python for HuggingFace/Transformers; Java for scalable NLP; Lisp for symbolic NLP |
| Computer Vision | Python, C++, MATLAB | Python (OpenCV, DL); C++ for embedded vision; MATLAB for academic/engineering use |
| Symbolic/Logic AI | Prolog, Lisp, Haskell | Designed for rule-based inference, symbolic reasoning |
| AI for Edge Devices | C++, Rust, Go, Swift | Performance, low memory footprint, and native compilation are key |
| AI on the Web | JavaScript, Python (via backend) | TensorFlow.js, Brain.js for web AI; Python for backend APIs |
| AI in Production Systems | Java, Scala, Go, Rust | Reliability, concurrency, and scalability |
2. Factors to Consider: Project Scale, Performance Needs, and Team Expertise
| Criterion | Considerations | Best-Fit Languages |
| Performance | Need for speed, low-latency inference, hardware acceleration (e.g., C++, Rust, Julia) | C++, Rust, Julia |
| Prototyping Speed | Rapid development, experimentation, ease of syntax | Python, R, Lisp |
| Ecosystem Support | Availability of AI frameworks, libraries, community, and tooling | Python, Java |
| Ease of Learning | Developer productivity, onboarding new team members | Python, JavaScript, Swift |
| Scalability | Suitability for large systems, distributed AI workflows | Java, Scala, Go |
| Cross-Platform Deployment | Ability to run on mobile, cloud, edge, and different OS environments | C++, Swift, Rust, Go |
| Academic Research | Numerical fidelity, reproducibility, and publishing | Julia, MATLAB, Haskell |
| Enterprise Integration | Integrating AI into enterprise-grade applications and APIs | Java, Scala, Python |
| On-device AI | For mobile apps and local inference | Swift, Java (Android), C++ |
Future Trends in AI Programming Languages
Based on the Developer Survey 2025, here are our 5 hot future trends that will shape the upcoming AI programming language sector in the near future.
#Trend 1: Python will keep on dominating the AI ecosystem
There have been many discussions about the future of Python. One of the biggest arguments is “If Python is to be kept as the de facto AI programming language for backend, we would require 500x more computing power and memory to get the same result as compared to other languages.”
While this is true in terms of Python’s lack of performance compared to C++ or Rust, when it comes to performance boosting, internal DSA teams at companies can always rewrite the code into Scala later.
Python will continue ruling in coming future because it’s simple, readable, and supports an extensive ecosystem of libraries such as TensorFlow, PyTorch, Scikit-learn, and Keras.
#Trend 2: C++ will still rule when it comes to Performance
While many developers have already shifted to Python-based AI programming languages, many companies still prefer C++ due to its high performance and low-level control possibilities.
As per Reddit forums, C++ rules the niche in AI for robotics, game development, electronics, and IoT-based programming. C++ will continue to be embraced in the future because of its capability to commercialize AI, which won’t be possible with famous AI programming languages such as Python.
Not to mention, in reality, most Python libraries are written in C++ or C, like TensorFlow or NumPy; otherwise, ML programs would be quite slow. They just provide a Python interface, showcasing the everlasting supremacy of C++ as an AI programming language.
#Trend 3: R for Data Analytics and Statistical AI
R offers packages with specific functions for statistical modeling, such as mixed effects models, which saves a lot of time while also giving accurate data stats. People always compare Python and R for data analytics to explain why to choose R over Python. The simple answer is, the simple functions are already inbuilt in R, while more complex functions can be integrated with the help of community packages, which ultimately saves a bulk of time.
There are more options in the R statistical and ML packages, which are hard-coded in the Python versions. Compared to R, Python requires more time to code the same function with the R programming language.
# Trend 4: Growing Influence of Go and Rust in AI Programming Languages
Rust and Go are very different approaches to AI programming languages compared to others. For example, Rust highly focuses on web safety and performance, whereas GO is more oriented towards principles of simplicity, and rapid development and deployment of applications
Although both Rust and Go lack the extensive support as we see in the case of Python, there is surely a scope of ongoing support, as developers really like the simplicity and performance-oriented approach of these languages compared to Python.
Essential Libraries and Frameworks for AI Development
| Category | Library/Framework | Language | Purpose |
| Deep Learning | TensorFlow | Python, C++ | Training & deploying DL models such as ChatGPT or DeepSeek |
| PyTorch | Python, C++ | Research-oriented deep learning | |
| Keras | Python | High-level API for TensorFlow | |
| MXNet | Python, Scala | Scalable DL (used by AWS) | |
| Machine Learning | Scikit-learn | Python | Classical ML algorithms & preprocessing |
| XGBoost | Python, R, C++ | Gradient boosting for tabular data | |
| LightGBM | Python, R | Fast gradient boosting | |
| CatBoost | Python, R | Boosting with categorical support | |
| NLP | HuggingFace Transformers | Python | Pre-trained NLP models (BERT, GPT, etc.) for Genertaive AI |
| spaCy | Python | Industrial NLP pipelines | |
| NLTK | Python | NLP for academia and learning | |
| Computer Vision | OpenCV | C++, Python | Image & video processing |
| Detectron2 | Python | Object detection (by Facebook) | |
| Reinforcement Learning | RLlib | Python | Scalable RL (from Ray) |
| Stable Baselines3 | Python | Standard RL algorithms | |
| Data Handling | Pandas | Python | Data manipulation and analysis |
| NumPy | Python | Numerical computing | |
| Dask | Python | Scalable dataframes for large datasets | |
| Visualization | Matplotlib | Python | 2D plotting |
| Seaborn | Python | Statistical data visualization | |
| Plotly | Python, JS | Interactive visualizations | |
| Model Deployment | ONNX | Python, C++ | Cross-platform model exchange format |
| TensorFlow Serving | Python, C++ | Model serving system | |
| TorchScript | Python, C++ | Exporting PyTorch models |
Conclusion and Final Recommendations
Hope you liked our extensive blog on the top AI programming languages in 2025.
Resources for Learning AI Programming Languages
There are a lot of resources to learn AI Programming Languages, both paid and free. Here are some sources we suggest you can try to get started with AI programming.
- AI For Everyone | Coursera
- The Easiest Explanation of Neural Networks You’ll Ever Watch!
- 🧠🔓AI Unlocked: Building and Mastering Large Language Models, Step-by-Step 📚 | by Yusuf Sevinir | Medium
- [NEW] AI Mastery Bootcamp: Complete Guide with 1000 Projects | Udemy
To get more technical, these other courses will introduce the basics of machine learning:
- Supervised Machine Learning: Regression and Classification | Coursera
- Unsupervised Learning, Recommenders, Reinforcement Learning | Coursera
- Neural Networks and Deep Learning by DeepLearning.AI | Coursera
FAQs
What programming languages are best for AI?
There are many AI programming languages, but Python, JavaScript, and C++ are considered the best languages for building scalable AI solutions.
Is AI coded in Python?
Yes, AI can be coded in Python. In fact Python is a very popular language for building AI systems. It’s supported by many AI-powered libraries such as TensorFlow, Pytorch, and more to help in rapid development.
Is C++ or Python better for AI?
C++ is best for tasks requiring performance, whereas Python is best suited for rapid development due to its extensive support. C++ is popular for building native like AI systems in IoT, gaming, electronics, etc, while Python is used for web-based AI-powered systems. Also, C++ is more powerful in terms of performance than Python for AI-based systems.
Is AI better with Python or Java?
Both are good choices. Choose Python if you want quick development. While Java takes time, it is also more capable in terms of performance and scalability than Python.
