What Julia Actually Is

Julia was released in 2012 by a team originally based at MIT with one specific goal: keep the readability of a scripting language like Python while getting the execution speed of a compiled language like C or Fortran. Most languages force a trade. Write in Python or R and heavy numerical code runs slowly unless it drops into C extensions. Write in C++ and everyday development takes longer because of manual memory management and a stricter type system. Julia's compiler, built on LLVM, turns a function into machine code the first time it runs, so a loop written in Julia does not need to be rewritten in C to be fast.

The design choice AI teams notice first is multiple dispatch. Instead of tying a function to one object, the way typical object oriented languages do, Julia lets a function behave differently depending on the combination of argument types passed to it. For numerical and scientific code, this turns into a real advantage: the same differential equation solver can work on ordinary numbers, matrices, GPU arrays, or numbers with uncertainty attached, without rewriting the solver each time.

The ecosystem has grown steadily rather than explosively. Julia now has more than 12,000 registered packages and has passed 100 million cumulative downloads. According to JuliaHub's 2025 State of Julia report, downloads of MLJ.jl, a popular Julia machine learning package, more than doubled year over year, a sign that adoption for AI specific work is picking up even though Julia's overall market share remains small.

Why Python Still Runs the AI World

Python was not built for AI either. It became the default language for machine learning because of timing and libraries, not because it was the fastest option on paper. NumPy, pandas, scikit-learn, TensorFlow, and PyTorch all matured inside the Python ecosystem, and each new tool built on the last one instead of starting over in a different language. By the time deep learning took off in the 2010s, Python already had the widest set of building blocks, the largest number of trained developers, and the most tutorials and community answers.

That lead has not shrunk. Recent developer surveys put Python's share of AI and machine learning projects at around 70 percent, and it continues to top rankings such as the JetBrains State of Developer Ecosystem survey as the preferred language for data science work. The TIOBE Index, which tracks overall language popularity, placed Julia at around rank 32 worldwide in early 2026 with roughly a half percent share, still climbing but far behind Python's double digit share.

Python's own execution speed is still modest compared with compiled languages, which is why performance critical pieces of PyTorch and TensorFlow are written in C++ or CUDA under the hood, with Python acting as the interface layer on top. That approach works well for most AI products. A model training loop spends most of its time inside optimized C++ or GPU kernels, so the fact that the surrounding Python code runs slowly barely matters. Where it starts to matter is in code that cannot be handed off to a pre-built kernel: custom simulations, agent based models, or anything where the underlying mathematics, not just the neural network layers, needs to run fast.

Julia vs Python at a Glance

The table below summarizes how the two languages compare on the factors that matter most when scoping an AI project or a hiring plan.

Performance: Why Julia Is Fast, and Why That Matters for AI

Speed differences between languages sound academic until they show up as a cloud bill or a missed deadline. Python's typical execution model interprets code line by line, and its Global Interpreter Lock stops CPU bound Python code from truly running instructions in parallel across multiple cores, even on a multi core machine. Libraries like PyTorch work around this by offloading the actual number crunching to C++ and CUDA, but any part of an AI pipeline written in plain Python, such as data preprocessing loops, custom loss functions, or simulation code, keeps that speed limit.

Julia compiles each function to native machine code the first time it runs, using the same LLVM compiler backend used by Swift and parts of Rust's toolchain. Benchmarks on numerical workloads, including the widely cited Debian benchmarks game, consistently show Julia running within about two times the speed of raw C, while Python and R can run ten to a hundred times slower on the identical task. For a machine learning pipeline that spends most of its runtime in a custom simulation, a Monte Carlo loop, or a differential equation solver rather than inside a pre-built PyTorch layer, that gap decides whether a job finishes in minutes or hours.

The clearest public example comes from Pumas AI, a company building pharmacology simulation software. According to a published JuliaHub case study, a pharmacokinetic simulation that took about eight hours to run in NONMEM, an established modeling tool in the pharma industry, ran in around twenty minutes after being rewritten in Julia. That is not a marginal gain. It is the difference between running one simulation overnight and running dozens of scenarios in an afternoon. 

Where Python Still Wins

• Breadth of libraries. If a use case involves large language models, computer vision, or standard tabular machine learning, Python already has a mature, well documented library for it.

• Hiring speed. There are far more Python developers with AI experience than Julia developers, so building or scaling a team happens faster and usually at a lower average cost.

• Production tooling. Model serving platforms, MLOps tools, and cloud AI services are built around Python first, sometimes exclusively.

• Community support. Bugs get answered faster, tutorials stay current, and most AI research code released alongside academic papers ships in Python.

• Integration. Python glues easily into web backends, data pipelines, and existing enterprise software, which matters when AI is one part of a larger product.

Where Julia Pulls Ahead

• Differential equations and simulation. Julia's DifferentialEquations.jl package is regarded as one of the most complete solver suites in any language, used in physics, epidemiology, and engineering models.

• Scientific machine learning. Models that combine a physical equation with a learned component, sometimes called physics informed neural networks, run naturally in Julia because the equation solver and the learning layer live in the same language and can be optimized together.

• Optimization heavy workloads. Portfolio optimization, energy grid balancing, and supply chain scheduling all involve iterative numerical solvers where Julia's speed removes the need to rewrite hot loops in C.

• Real time and control systems. Robotics and control applications that need a fast, predictable response time benefit from Julia's compiled performance without the added complexity of a separate C++ codebase.

• Research to production continuity. Julia code that runs fast in a research notebook does not need to be rewritten in a second language to run fast in production, so teams avoid the common handoff where a Python prototype gets rebuilt in C++ before shipping.

Real AI and Machine Learning Work Where Julia Fits Best

The clearest way to judge when Julia AI development actually pays off is to look at industries already using it in production, not just in research papers.

• Pharmaceutical modeling and drug discovery. Pharmacokinetic and pharmacodynamic simulations, the kind used to predict how a drug behaves in the body, involve solving large systems of differential equations repeatedly. Pumas AI and several pharma research groups now run this work in Julia.

• Climate and weather modeling. Research groups working on large scale climate and weather simulation have adopted Julia where Python alone could not keep pace with the required grid resolution and simulation frequency.

• Energy grid and power systems. Utilities and energy researchers use Julia for optimization problems such as balancing electricity supply and demand across a grid in near real time, a workload that mixes simulation with fast iterative solvers.

• Financial risk modeling. Julia's speed on matrix heavy calculations fits Monte Carlo risk simulations, algorithmic trading research, and portfolio optimization, areas where a few milliseconds per calculation add up across millions of iterations.

• Robotics and autonomous systems. Control algorithms that plan and react in real time benefit from Julia's compiled speed, particularly in research robotics labs building on packages such as RobotDynamics.jl.

• Scientific machine learning and physics informed models. Any AI project that must respect a known physical law, not just learn patterns from data, tends to move faster in Julia because the solver and the neural network share one codebase.

Outside of these areas, standard AI work such as building a customer support chatbot, a recommendation system, a document classifier, or most computer vision applications still has no strong reason to move away from Python. The libraries are more mature, the developers are easier to find, and the performance gap rarely matters when the heavy lifting already happens inside an optimized C++ or CUDA layer.

A Decision Checklist: When to Hire Julia Developers for AI

Rather than treating this as a philosophical debate, run a project through a short checklist. This is a practical way to judge when to use Julia instead of Python for AI, based on the workload rather than opinion. Julia AI development tends to make sense when most of the points below are true, and Python remains the safer default when most of them are false.

• The core computation involves differential equations, large matrix operations, or iterative numerical solvers, not just training a neural network on labeled data.

• The team has already hit a performance ceiling in Python and is patching it with C extensions or similar workarounds, adding complexity without solving the underlying problem.

• The project needs to move directly from research code to production without a separate rewrite phase, because a second implementation in C++ would be too slow to build and maintain.

• Simulation speed, not model accuracy alone, decides whether the product is viable, such as real time control, live risk modeling, or high frequency optimization.

• The domain already has strong Julia libraries, such as SciML, JuMP.jl for optimization, or Flux.jl, that would otherwise need to be built from scratch in another language.

• The organization can support a smaller, more specialized hiring pool, either through in house recruiting or by working with an AI development company that already maintains Julia talent on staff.

If none of these apply, hiring Julia developers for AI adds a hiring and maintenance cost without a matching benefit. If two or more apply, especially the first and second, it is worth a serious technical review.

What It Costs to Hire Julia Developers vs Python Developers

Cost is usually the deciding factor once the technical case is settled, and it works against Julia in two ways: a smaller pool of available developers, and a market that has not standardized rates the way Python has.

Two things stand out. First, Julia developer pay does not follow the same offshore discount curve that Python development does, because the pool is too small and too specialized for large scale offshore staffing. Second, hiring a generalist AI developer and asking them to pick up Julia rarely works well for production code. Multiple dispatch and type stability, the features that make Julia fast, also make it easy to write Julia code that looks correct but runs slowly if the developer does not understand them. Experience with the language matters more here than it does with Python, where competent generalists can be productive quickly. 

Learning Curve: How Fast Can a Python Team Pick Up Julia

The syntax itself is not the hard part. A Python developer can read basic Julia code within a day or two because both languages favor short, readable lines and avoid the heavy boilerplate of Java or C++. The real learning curve sits in two concepts that do not exist in Python: multiple dispatch and type stability. Multiple dispatch changes how a developer thinks about structuring code, since behavior is chosen by argument types rather than by which object a method belongs to. It takes a few real projects, not a tutorial, for that habit to feel natural.

Type stability is the concept that trips up most newcomers. Julia code runs fast only when the compiler can determine a variable's type ahead of time. Code that lets a variable silently change type during a loop still runs, but it quietly falls back to a much slower execution path, and nothing in the syntax warns a beginner that this happened. This is why experience matters more for Julia hires than for Python hires. A developer who understands type stability will write code that is fast by default. One who does not will write code that looks identical but runs many times slower.

For teams building a hybrid setup, a realistic ramp up plan is two to four weeks for an experienced numerical or scientific computing developer to become productive in Julia, and considerably longer, often several months, for a general software engineer with no numerical computing background. That gap is one more reason many organizations hire a small, specialized Julia team rather than retraining their existing Python developers wholesale.

Common Misconceptions About Julia

• "Julia is only for academics." This was closer to true a decade ago. Today, Julia runs in production at pharmaceutical companies, energy utilities, and financial firms, not just university research labs. JuliaHub's commercial backing and enterprise contracts are evidence that production use has grown well beyond academic research.

• "Julia will eventually replace Python for AI." Nothing in current adoption data supports this. Python's share of AI and machine learning projects remains far larger than Julia's, and the two languages are increasingly used together rather than in competition, with Julia handling a narrow, performance critical role inside a broader Python driven system.

• "Julia code is automatically fast just by using the language." Speed depends on writing type stable code correctly. A poorly written Julia function can run slower than well optimized Python using NumPy, which is why hiring an experienced Julia developer matters more than simply switching languages.

• "There is no point learning Julia if the library ecosystem is smaller." For general purpose AI work this is a fair concern. For the specific numerical and scientific workloads Julia targets, its libraries, such as DifferentialEquations.jl and JuMP.jl, are often more complete and better maintained than comparable Python alternatives.

The Hybrid Approach: Julia and Python Together

Very few teams need to choose one language for an entire AI system. The more common pattern, and often the more practical one, is to keep Python as the interface layer for APIs, data pipelines, and user facing tools, while routing the computationally heavy piece, the simulation engine, the solver, or the optimization routine, through Julia.

Tools such as PyCall.jl and PythonCall.jl let Julia code call Python libraries directly, and JuliaCall lets Python call into Julia in the other direction, so a team does not have to rebuild an entire data pipeline just to get access to a faster solver. In practice, this means a data scientist can keep working in a familiar Python notebook, calling out to a Julia function for the one part of the pipeline that actually needs the speed.

This hybrid setup also lowers the hiring bar. Instead of needing a full team fluent in Julia, a company can hire one or two Julia specialists to own the performance critical core, while the rest of the team continues building in Python. This is often the most realistic way to add Julia AI development to an existing Python heavy organization without a disruptive rewrite.

Pro Tips for Teams Considering Julia

• Benchmark before you commit. Write the actual bottleneck function in both languages using realistic data sizes before deciding. A synthetic benchmark can overstate or understate the real gap.

• Watch for type instability. Julia code where a variable's type can change during execution loses most of its speed advantage. This is the single most common reason a Julia rewrite disappoints.

• Budget for compile time. Julia compiles functions the first time they run, which can make short scripts feel slower to start than Python. This matters less for long running services and more for command line tools.

• Start with one workload, not a full migration. Move the specific bottleneck, a solver, a simulation, or a scoring function, to Julia first, and measure the actual gain before expanding further.

• Hire for the domain, not just the language. A Julia developer with a scientific computing or numerical methods background will usually produce better results than a general software engineer who picked up Julia recently.

Key Takeaways

• Python remains the right choice for most AI and machine learning projects because of its library maturity, hiring pool, and tooling.

• Julia earns consideration when a project's bottleneck is raw numerical computation, not model training itself.

• Julia's performance, often within about two times the speed of C, comes from its compiled execution model and does not require a separate C layer the way Python does.

• The Julia hiring market is small and does not offer the same offshore rate discounts as Python, so plan hiring timelines accordingly.

• A hybrid team, Python for the interface and Julia for the computational core, is often the most practical way to get the benefit without a full rewrite.

How an AI Development Company Helps You Make This Call

Most in house teams do not have a strong opinion on Julia because they have never needed one. That is normal. An experienced AI development company has usually already run this comparison on a real project and can tell within a short technical review whether a workload actually needs Julia's performance or whether a well optimized Python solution, using libraries such as NumPy, JAX, or Cython for the hot paths, would get the same result without the hiring overhead.

If a project does call for Julia, working with an AI development company that already has vetted Julia developers on staff removes the slowest part of the process: finding and evaluating specialized talent in a small market. It also means the hybrid architecture, Julia for the core computation and Python for everything around it, gets designed correctly the first time, instead of being patched together after a slow prototype reveals the bottleneck.

Whether the right move is to hire AI developers who specialize in Julia, build a hybrid team, or simply optimize an existing Python codebase, the decision is easier with a partner who has already made it more than once.

Conclusion

Choosing between Julia and Python for AI is not really a question of which language is better. It is a question of what the project actually needs to compute, how fast that computation has to run, and how much specialized talent the organization is prepared to hire and retain. For most AI products, Python's ecosystem and hiring pool make it the sensible default. For the smaller set of projects built around simulation, optimization, or differential equations, Julia AI development can cut both computation time and total cost of ownership, provided the team hiring for it understands where that advantage actually comes from. Getting that decision right the first time, rather than discovering it after a slow prototype, is usually the difference between a project that ships on schedule and one that gets rebuilt from scratch.