Growing datasets and the hype surrounding AI have spurred competitiveness among businesses, resulting in a fierce battle for improved system performance. Thus, the choice of the backend programming language is the core decision in building a reliable IT infrastructure and achieving a project’s success. The rivalry Rust vs Go vs Python is currently in the spotlight.
The right choice of the programming language impacts the level of reliability, security, and maintainability of your application code, which, in turn, affects time to market and software performance. Jarrod Overson, CTO at Candle, has maintained 70,000 lines of Rust code with far fewer tests than he would have in any other language, saving time on the project. However, he also had to put up with trade-offs, such as a limited number of libraries, complex code refactoring, and performance issues with asynchronous code.
How do you know when and why use Rust, Go, or Python? Incorrect decisions can lead to technical debt, system latency, increased infrastructure costs, and ultimately, a decrease in service quality.
Backed by hands-on experience from our skilled programmers, this article provides a decision-making framework to help you evaluate each language’s capabilities in terms of performance, community support, use cases, infrastructure demands, and associated costs.
Go vs Rust vs Python: Why these languages shape the 2025 and beyond backend systems
Rust, Go, and Python have become the frontrunners because they reflect distinct backend priorities and cater to different needs. Python for AI productivity with a large follower base, numerous data science libraries, and ease of use. Rust for uncompromising performance and memory safety without garbage collection. Go for simplicity and scale in cloud-native environments.
Python: current market overview
In 2025, the adoption rate of Python among developers increased by seven percentage points compared to 2024, according to Stack Overflow. With 57.9% of developers using it due to its widespread use in AI/ML projects, and a wide selection of libraries and third-party tools.
Such a significant surge in popularity may indicate that hiring Python developers will be even easier in 2025, which is a distinct advantage if you’re looking to ramp up a project quickly.
Rust: current market overview
For eight consecutive years, Rust has been the most admired programming language. 72% of developers admit that this language is the most modern and promising, with potential for interesting and challenging projects. The most widespread Rust use cases are in embedded systems, IoT, and edge computing projects.
Major tech giants are seeing numerous advantages of Rust for their businesses and rewriting parts of their code in this language for enhanced performance and security. For instance, Microsoft internally refers to 2025 as “the year of Rust”.
Microsoft is rewriting parts of Hyper-V, the virtualization technology behind Azure, in Rust. This enhances the platform’s security by reducing memory-related bugs that are common in older languages, such as C and C++. They have also added 32-bit chip support to Tock OS (an operating system built with Rust) for Surface devices, improving performance and security even for low-level system components.
Go: current market overview
The philosophy of Go is based on simplicity, stability, and reliability. It was initially designed to address the issues and limitations of other languages, such as C++. Robert Pike, co-creator of Golang along with Robert Griesemer and Ken Thompson, said back in 2012:
Before the compilation was done, we’d roped Ken in and had decided to do something. We did not want to be writing in C++ forever, and we — me especially—wanted to have concurrency at my fingertips when writing Google code. We also wanted to address the problem of “programming in the large” head-on.
According to the TIOBE index, Go is ranked 8th among the most popular programming languages based on the number of developers, courses, and third-party vendors. Cloudflare reported that the Go language is the leading choice for building automated API calls, with 12% of all automated API requests made by Go clients.
Rust vs Python vs Go: Performance differences based on industry-standard benchmark analysis
Performance-wise, these three languages are different depending on the trade-offs you’re willing to make, which involve the developer’s time and skills, as well as potential errors that you’ll need to address at runtime.
Compilation speed compared
When and why compilation speed matters
Compilation speed directly affects the edit–build–run cycle: faster builds shorten feedback loops, save developer time, and keep projects moving efficiently. Companies often measure the impact of compilation by tracking build times, test execution speed, and overall developer throughput.
Faster code compilation directly translates into higher productivity and accelerated time-to-market.
Longer compilation times can increase cloud resource usage, CI/CD pipeline costs, and developer idle time, affecting budgets in large-scale projects.
In rapid prototyping, agile environments, and CI-driven teams, long compile times can be a major blocker. By contrast, in large-scale systems or safety-critical applications where runtime efficiency and reliability matter more than iteration speed, slightly longer compile times are often an acceptable trade-off.
Rust: Runtime power vs compile time
Rust is a systems programming, statically typed, compiled language with a zero-cost abstraction feature, allowing abstractions without runtime performance penalties. Additionally, Rust enables frequent compiler checks to prevent errors at runtime.
Thus, with Rust, you can build high-performance, error-free systems without much reliance on your hardware capacity, but at the expense of increased compilation time. However, with the right toolset, developers can improve compilation speed. As in this example, a developer managed to decrease it from 175 seconds to 9.1 seconds.
Go: Fast compilation by design
When it comes to Golang vs Rust compilation, fast compilation of Go was an initial design goal. The language has only 25 keywords (Rust has 53 and Python has 38), which increases the speed. Go is a statically typed, compiled language which provides optimal runtime performance with low memory overhead compared to Python. Additionally, in terms of a Python vs Go vs Rust concurrency comparison, Go can process a single input file (even with a million lines) in parallel via goroutines, as noted by this Reddit user.
Python: Ease of use requires performance workarounds
Python is a dynamically typed interpreted language, which makes it relatively slow but easy to use. However, developers can mitigate performance bottlenecks by compiling Python code to C using tools such as PyPy and Cython. This way, they bridge performance capabilities with Python’s innate ease of use, but need to devote time to additional workarounds.
Rust often sacrifices compile-time speed for runtime efficiency, while Go prioritizes compile speed without sacrificing too much runtime performance.
Python, meanwhile, offers rapid iteration but weaker runtime speed unless extended with tools. That’s why a Rust vs Python speed comparison is practically irrelevant.
Seasoned programmers can maximize the potential of any language and help you strike a balance between compilation speed and runtime performance, depending on your project priorities and team capacity.
Memory usage patterns
Runtime memory usage impacts overall system performance, especially during energy-intensive tasks.
Where Rust memory rules shine
Rust ensures memory safety by avoiding garbage collection and instead relying on ownership, borrowing, and lifetimes. Ownership rules imply that each data object (value) in Rust code has only one owner at a time, whether that’s a variable, struct, or function. When the function goes out of scope, the memory is returned to the memory allocator.
This unique approach enables developers to avoid common system vulnerabilities that are typical of C++, such as buffer overflows, null pointer deferencing, and use-after-free errors. Thus, runtime efficiency improves as code has fewer errors and is easily maintainable.
Rust’s memory management can have a steep learning curve, as it’s a new concept that requires developers to have some prior experience, as well as time to get accustomed to it.
Python approach to memory management
Apart from common type and value attributes, each object in Python code also has a reference count (the number of references pointing to each object). Once the reference count drops to zero, Python automatically deallocates the memory, allowing for immediate cleanup. However, reference counting has its limitations, such as performance overhead due to circular references where objects reference each other in a loop, preventing automatic cleanup
Python leverages automatic garbage collection to solve these issues. But this process can involve frequent cycles, which can again slow down performance. Therefore, for optimal memory management, developers may need to resort to manual memory optimization and usage monitoring.
Go and its unique garbage collector
Go employs an automatic garbage collector (GC) based on a concurrent, tri-color mark-and-sweep algorithm, which means it tracks which objects are still in use (“mark”) and then frees up those that are not (“sweep”). At the same time, the program continues to run with minimal interference. Unlike Python, Go defaults to value semantics rather than reference semantics, which reduces the likelihood of memory leaks and simplifies memory management.
However, Go’s GC can still introduce latency in long-running processes and pause the program (e.g, for 2.7 seconds in this example). However, engineers can reduce GC overhead through optimization techniques in such environments as GOCC (which controls GC frequency) and GOMEMLIMIT (which caps memory usage).
For business decision-makers, these memory management differences matter most in high-throughput systems, real-time applications, or resource-constrained environments.
Rust’s approach eliminates garbage collection pauses but requires more development time.
Go’s GC is optimized for low latency but may not suit microsecond-sensitive applications.
Python’s memory management works well for most business applications, but can become a bottleneck in memory-intensive workloads.
Real-world performance context
Below is a table comparing Rust, Go, and Python based on benchmark data from the Computer Language Benchmarks Game, a widely referenced resource for performance comparisons.
The above benchmarks are simulated algorithmic problems that may differ from real-world scenarios. However, they’re necessary to give a general perspective on how Rust, Go, and Python perform in different environments.
These Python vs Go vs Rust performance benchmarks demonstrate that Rust consistently excels in CPU-intensive workloads, Go offers balanced performance across various scenarios, and Python achieves competitive results in specific use cases, such as memory allocation tasks.
Depending on the task at hand and your core priority for defining performance, you should decide on one or several most critical benchmarks and run custom performance tests. For instance, a developer compared the performance of the three languages using a single benchmark by running a simple program that counts primes among the same 1 million values, and the results were surprising. In this Python vs Rust vs Go performance benchmark, Go appeared to be the fastest of all.
Rust vs Go vs Python: Comparison from Xenoss engineers
So far, we’ve covered the market overview for each language and compared their performance specifics. To help you keep in mind the core differences between Python, Go, and Rust, we’ve compiled a table highlighting their prominent differences. Just like cars are built for different purposes (some for speed, some for comfort, others for endurance), programming languages are optimized for various types of workloads.
Moving on with the language comparison, we’ll discuss which project requirements should impact your choice and how each language stands from the business perspective.
Project requirements
Here are the project criteria you should consider:
Project focus
For instance, you need to develop an AI system with a robust infrastructure that can efficiently handle peak loads while utilizing only a portion of the GPU capacity. If that’s your focus, then developing with Rust can prove effective. If, for the same AI system, your priority is developer productivity and access to advanced data science tooling, then choose Python. And if your system will handle thousands of parallel requests in the cloud, then Go can be a viable option.
Functional and non-functional requirements
With performance and speed as the main requirements, you can choose Rust. However, this doesn’t mean that your entire application should be written entirely in Rust; only some of the most critical, low-level, and performance-intensive components (e.g., IoT and embedded devices) should be written in Rust. For horizontally scalable, concurrent workloads (such as APIs, streaming services, and microservices), Go is more effective. For compute-light applications or rapid prototyping, Python suffices.
Industry regulations
For industries with strict regulations, such as healthcare and financial services, consider the maturity and compliance track record of the language ecosystem. Python and Go use cases have longer histories in regulated environments, while Rust’s ecosystem is newer but growing rapidly in adoption.
Listing the scope of priorities for each project is the first step to selecting the best-fit backend programming languages.
Business considerations
The choice Rust vs Go vs Python is also driven by your business considerations, including costs and in-house engineering capacity.
Talent availability and hiring costs.
Rust has the steepest learning curve, which makes it harder to ramp up teams and explains why Rust developers are less common and more expensive to hire. In the US, the average salary is around $150,000 per year, reflecting the scarcity of talent. By contrast, Python and Go developers typically earn closer to $120,000 annually.
The talent pool also differs in experience. Python’s low entry barrier attracts many beginners. About half of Python developers have less than two years of experience. Rust, on the other hand, tends to attract seasoned programmers, often switching from C++ or Go, which makes them more experienced but harder to source.
Go developers sit in the middle. The language is easier to learn than Rust but more structured than Python, meaning Go developers often have stronger prior coding experience than Python developers, while being more readily available (and less costly) than Rust specialists.
Time-to-market
Python enables the fastest prototyping, making it ideal for AI/ML projects and MVPs where iteration speed is most crucial. Go offers a balance of simplicity, concise syntax, quick ramp-up, and strong concurrency support for backend systems. Rust requires more time upfront due to its steep learning curve, but pays off in the long term with enhanced reliability, security, and performance.
Long-term maintenance costs
Rust reduces technical debt through strong compiler checks and memory safety guarantees, minimizing costly production bugs. Go offers straightforward codebases that are easy to maintain, especially in distributed teams. Python, while productive initially, may incur higher maintenance costs in performance-intensive systems unless optimized with third-party tools.
Go, Python, and Rust use cases and success stories
Python vs Rust vs Golang isn’t an apparent rivalry, as all three are strong languages with growing communities, fans, supporters, and even haters. Deciding on Python vs Rust, Golang vs Python, and Go vs Rust for your particular project is one of the most crucial decisions that will define your application performance, stability, and security.
When to use Python: Default choice for AI/ML pipelines
Key Python workloads include natural language processing, deep learning, machine learning, data analysis, scientific computing, automation, backend web services, scripting, rapid prototyping, and workflow orchestration. Annual Python survey reveals that the top Python use cases include:
Python is a versatile language with strong community support and a wide range of data science libraries, making it ideal for experimenting with AI and ML projects. Such a large ecosystem spurs developers’ creativity. Rust and Go, in this respect, are more practical languages focused on providing developers with an environment that prioritizes speed over creativity.
Data processing, engineering, and analysis (especially in a scientific setting) are also typical use cases where Python shines. For every stage of the data pipeline, there is a Python library that simplifies development.
With Python, you focus on quick and creative development, but can reach bottlenecks at runtime and in production or when scaling your codebase.
When NOT to use Python
Python’s Global Interpreter Lock (GIL) limits true multithreading for CPU-bound tasks, though it handles I/O-bound concurrency well through asyncio and multiprocessing. Developers use C or C++ for computationally intensive tasks, and Python serves as the “glue” that holds AI and ML applications together.
When intense performance matters, Python can fall short. With this language, businesses can conduct prolonged, data-intensive research rather than achieve real-time performance or low latency.
NASA cuts development costs with Python
NASA’s United Space Alliance (USA) developed a workflow automation system (WAS) using Python that achieved “immediate functioning code so much faster in Python than in any other language [Java and C++] that it’s staggering,” according to Senior Project Engineer Robin Friedrich. He managed to set up the development environment and start coding in Python within 20 minutes.
Unlike Java and C++, Python doesn’t require extensive documentation to maintain and read code. A shallow learning curve was also a decisive factor, as no matter the team turnover at NASA, new developers didn’t require much time or training to onboard to the project.
The system was completed in under a year and met and even exceeded NASA’s specifications.
When to use Go: Cloud-scale concurrency workhorse
Key Go workloads include APIs, microservices, web applications, command-line interface applications, DevOps, large data processing systems, and cloud applications.
Go’s simplicity and fast compilation times make it an ideal choice for quick deployment. This language suits teams and projects of any size, as it’s adaptable to different environments. Goroutines and channel-based communication between them enable the efficient execution of concurrent workloads without additional overhead or complexity. Developers can work simultaneously on various parts of a project and maintain productivity.
Apart from Python being an obvious choice for AI/ML workloads, Go can also be a suitable option, as it enables parallel processing for data pipelines with large datasets, simplifying model training and inference.
Migration to Go is a common practice among companies seeking simplicity, high performance, code maintainability, and scalability. Stream, a service that provides APIs and SDKs for building in-app chats and videos, switched from Python to Go, as the latter offers higher performance (40 times faster than Python) and a 5-second compilation time for their largest microservice. For their team of 20, Go is an optimal choice, as the ecosystem and tooling are well-established compared to newer languages like Rust.
When NOT to use Go
If garbage collection is a significant overhead for your application and you need uninterrupted runtime and enhanced performance, then Rust may be a better choice than Go. Additionally, embedded systems, microcontrollers, and operating systems are not typically compatible with Go due to its garbage collector and runtime requirements.
If your team heavily relies on AI/ML libraries and advanced scientific tooling, Python would offer a stronger ecosystem support.
PayPal sees 10% CPU savings after migrating to Go
PayPal’s NoSQL database was written in C++, but over time, the code complexity increased, making it harder to maintain. Go seemed a viable option due to its simple code layouts and goroutines. PayPal’s development team spent six months migrating the C++ database to Go.
This project proved successful as this migration helped PayPal’s team improve code maintainability, scalability, and increase developers’ productivity. Bala Natarajan, Sr. Director of Engineering at PayPal said:
In our tightly managed environments where we run Go code, we have seen a CPU reduction of approximately ten percent with cleaner and maintainable code
Such results prompted the PayPal team to invest even more in Go, adopting it across their numerous distributed solutions to leverage concurrent workloads while achieving high performance and security.
When to use Rust: Systems powerhouse
Key Rust workloads include IoT, processing engines, security-sensitive applications, developer tools, and embedded system components.
Rust shines in the environments where its performance has the most application, such as operating systems and embedded systems. Writing an entire application in Rust should be well-justified, as it may require significant engineering effort.
The same stands for migration to Rust. You can either migrate your application entirely to Rust or partially (e.g., migration of data processing pipelines, machine learning systems, and CLI tools).
According to the example of migrating Java code to Rust, the Rust community suggests planning for a migration of around 4-6 months due to Rust’s notorious steep learning curve. They also recommend starting small and selecting some non-essential software components for migration to Rust, and once tested, proceed to migrate the most CPU- or memory-hungry components.
When NOT to use Rust
Carefully consider and justify the use of Rust for your particular application and business. Startups that need a quick rollout and lack in-house engineering capacity, as well as budget for hiring Rustaceans, may hit an overhead wall when developing in this language.
You should thoroughly evaluate the available tooling, infrastructure fit, and dependencies your Rust code will have. And even if you’re sure about experimenting with Rust, proceed with caution and in small increments.
Plus, simple web applications or software prototypes aren’t the right fit for Rust, as you’ll sacrifice lots of development time for a system that doesn’t need Rust-level performance. For less performance-critical solutions, Python and Go are more suitable choices.
Rust is a long-term decision. It’s not the language for testing and playing around. Once you decide to proceed, Rust will require a significant amount of time, effort, and investment. Just like a sports car, Rust is for those who are willing to spend money on it and don’t expect it to fulfill every need, such as driving to the grocery store or picking up kids from school right after racing.
Amazon chooses Rust to power AWS Lambda and Fargate
Amazon is the most prominent Rust client. They build lots of solutions in this programming language, such as Amazon Firecracker, a virtualization technology. AWS Lambda and AWS Fargate run on Firecracker, which enables these systems to launch in less than 125 ms. This allows Lambda to process trillions of executions for hundreds of thousands of active users per month.
Carl Lerche, an AWS engineer, stated that using Rust and tokio (the Rust runtime) allows them to write responsive and reliable services, which in turn help them offer a better customer experience. It’s no surprise that AWS is the top cloud service provider, with a 29% market share in 2025. They make strategic decisions and set their priorities right, particularly in selecting programming languages.
Hybrid use of Rust, Golang, and Python
The good news is that you can select a combination of these languages to cover your different needs. For instance, Rust and Python complement each other well in AI pipelines, where Python is responsible for prototyping and tooling, and Rust handles the heavy lifting, enabling high performance.
Cheuk Ting Ho, PSF Board Member, Developer Advocate at JetBrains, supports the concept of hybrid use of Rust and Python:
Python and Rust complement each other well. Python is a high-level language and fast to code, while Rust is compiled and fast to execute. PyO3 brings the two together so developers can have the best of both worlds.
It’s also a common practice to combine Go and Python in cloud workloads. In such an environment, Go handles APIs and concurrency, while Python powers analytics and ML integrations.
Combining Rust with Go is less common but is also gaining traction. With Rust offering safety in security-critical environments and doubling Go’s performance capabilities, while Go remains in charge of concurrency and orchestration.
Bottom line
Analyzing every feature, value, and trade-off of Rust, Python, and Go requires thorough evaluation.. Still, it’s worthwhile, as the programming language choice often defines how fast, secure, and reliable your final product will behave in the real world.
Approach language selection as a strategic decision that weighs technical benchmarks along with business considerations and unique project requirements. Consult with Xenoss engineers to save time and streamline this process.
