Python vs Go: A Practical Comparison for Developers

Two things are true about Python: It’s the most popular programming language in the world, and it’s the most common companion language for Go developers. How do we know that? From the Developer Ecosystem 2026 survey by JetBrains.

But we also know a lot more about the developers who use these two languages – both from quantitative data from different surveys, as well as qualitative data from our interviews with professionals who switched from Python to Go.

In this article, we share findings from these sources, going well beyond the simple syntax comparisons you will usually find. We will look at how the two languages differ, what the surveys tell us about the people who use them, and what our interviews say about the trade-offs between Python and Go, so that if you’re considering making the leap, you don’t have to dive in blind.

TL;DR

• The article compares Python and Go across language design, performance, tooling, developer profiles, and migration experiences.
• Python favors expressive code and rapid iteration; Go favors explicit, standardized code that is easy to maintain across teams.
• Go excels in raw performance and concurrency, and is widely used in cloud infrastructure. Python remains stronger for machine learning, data science, and scientific computing.
• Go developers are typically more infrastructure-focused and more senior. Go is rarely someone’s first language, whereas Python is very beginner-friendly.
• Python leads in building AI models, yet Go developers create AI integrations at nearly the same rate: 46% vs 44%.
• Moving to Go rarely means abandoning Python. Most teams use both, with Go for services and infrastructure and Python for AI and data workloads.

A note about the data

Unless stated otherwise, all survey figures come from the JetBrains Developer Ecosystem Survey 2026 (28,945 responses, fielded May–July 2026). We compare two groups: developers who selected Go among their primary languages (1,836) and developers who selected Python (11,298). The groups overlap, with 619 respondents selecting both languages. Therefore, every gap below is a conservative estimate of the real difference. Some questions were shown to only a part of each group, so bases are provided for reference. The qualitative material comes from interviews with developers who moved production workloads from Python to Go. We conducted the interviews between April and May 2026 and included seven participants who began using Go within the past year.

Python vs Go – languages compared

Core philosophy

On paper, Python and Go sound like they’d get along. Python lays out the core principles in the Zen of Python: Simple is better than complex, readability counts, there should be one obvious way to do it, etc. Go, as per Effective Go, “focuses on simplicity, reliability, and efficiency”, and is “specifically designed to make it easy to build software at scale”.

In practice, Python often optimizes for the person writing the code, offering multiple ways to say the same thing. Go optimizes for the person (or, nowadays, possibly the agent) reading someone else’s code two years later, so it picks one way to say most things and makes you spell it out. The goal is for all Go code to look like other Go code.

We point this out right at the beginning, because however similar these two philosophies look, this difference in approach underlies almost all the other language design differences that follow.

Ease of use and learning curve

Python is arguably one of the easiest programming languages to learn among those widely used in production. The syntax resembles natural language, the core set of constructs is small, and beginners can focus on programming ideas instead of language mechanics, leading to less frustration and faster results. Surveys consistently show Python to be one of the most popular programming languages (if not the most popular). In 2026, 10% of Python developers have been coding for less than a year, 23% are students or working students, and 19% hold junior positions, showing the language has a strong early-stage user base.

Python’s popularity among early learners is almost self-perpetuating. Initially, the language was well-suited to introductory courses and learning materials largely because it lets beginners print “Hello, world” with little ceremony, bypassing much boring theory in favor of real outputs. It also has the added benefit of being a generalist language that can handle a variety of use cases. Later, the sheer number of introductory courses and learning materials made it a natural choice for anyone who wanted to pick up programming but didn’t know where to start.

It’s not the same case with Go. In fact, you would be hard-pressed to find courses that teach you programming through Go. There are two reasons for that. First, Go 1.0 was released in 2012, long after Python had already become firmly embedded in CS curricula. The other is that Go solves problems that beginners don’t have. Explicit error handling, static types, and interfaces, which we’ll discuss later, all pay off across a large codebase maintained by a team over years. Go also has a much narrower set of implementations than Python, which makes it even less appealing for beginners.

That said, Go was also designed to be easy to learn, but for a different reason: Any large team should be able to onboard a new engineer quickly. Many Go developers will say that if you already know general programming concepts, you can become productive in Go in a matter of days or weeks. Developers we talked to said that Go “feels as easy to write as Python” and that it “strikes a good balance between Python’s simplicity and Java’s explicit nature”.

Syntax and readability

When you read Python, you can often follow it even without programming experience, because it reads relatively close to English. Python is dynamically typed, so type declarations are optional, though in practice, many companies and large codebases now write Python with type hints throughout, checked by tools like mypy or Pyright. Most of the ecosystem follows the formatting conventions set out in PEP 8, but they are still optional, and so some teams diverge from them.

Go, by contrast, is explicit and deliberately limited, which contributes to the cross-team readability we mentioned. It is statically typed, which makes it noticeably more verbose than Python. That was the main syntactic complaint raised by the developers we interviewed. Formatting in Go is not a matter of preference: gofmt will shape your code into the canonical form, whether you like the conventions or not.

One syntactic difference that developers consistently pointed out as a big adjustment when moving from Python to Go was error handling. Python lets you push failure handling to the edges of a function with try / except. Experienced Go developers clutch their pearls at the sight of that.

# Python: handle failure where it is convenient
try:
    profile = get_user(user_id)
except UserNotFound:
    return render_404()

// Go: handle failure where it happens
profile, err := getUser(userID)
if err != nil {
    if errors.Is(err, ErrUserNotFound) {
        return render404(w)
    }
    return fmt.Errorf("get user %s: %w", userID, err)
}

The Go version is longer, and in a large handler, developers will find themselves writing the if err != nil boilerplate ad nauseam. In return, they get control flow with no invisible jumps: Every path a value can take is explicit. Both approaches come with trade-offs.

Type system

Python’s dynamic typing checks types at runtime. This gives developers flexibility in writing and can speed up iteration, but it also means some type errors only show up when the code runs. The flexibility extends to the object model, which relies on duck typing: The code cares less about what an object is than what it can do. The developers we interviewed said they sorely missed duck typing, as it makes testing and mocking more difficult in Go.

Go checks types before the program runs. This results in fewer type-related failures reaching production and makes refactoring safer, but also causes code verbosity, as we touched on earlier.

Object model and polymorphism

Python is object-oriented in the way that most developers first learn the term. Everything is an object, and classes inherit from other classes (several at once, if you insist). Polymorphism works in two ways: down the class hierarchy when a subclass overrides its parent’s method, and sideways through duck typing, where anything with a read() method can be handed to code that expects a file.

Go has none of that hierarchy. There are no classes, no inheritance, and no way to declare that one type is a subtype of another. Instead, structs have methods attached to them, as well as interfaces that types satisfy implicitly without ever naming them.

# Python: the implementation announces its role
class Store(ABC):
    @abstractmethod
    def get(self, key: str) -> bytes: ...

class RedisStore(Store):
    def get(self, key: str) -> bytes:
        ...

// Go: the consumer declares what it needs; RedisStore never mentions Getter
type Getter interface {
    Get(key string) ([]byte, error)
}

func handleRead(g Getter, key string) ([]byte, error) {
    return g.Get(key)
}

Notice how the dependency runs. In Python, RedisStore inherits from Store, so the abstraction sits with the implementation. In Go, handleRead declares the one method it actually calls, and RedisStore never learns that Getter exists. That’s why Go developers say to “accept interfaces, return structs”, and that’s why interfaces stay small – io.Reader and io.Writer have a single method each.

Performance and speed

Go follows a traditional compilation model, producing a native binary. Python, on the other hand, takes a hybrid approach: Source code is compiled to bytecode, which the interpreter then executes.

As most compiled languages do, Go wins in terms of raw speed. You can compare the patterns across latency, throughput, and startup time on The Computer Language Benchmarks Game. Microbenchmarks like these don’t really translate directly to real workloads, and we wouldn’t be surprised if you made nothing of them. Our interviewees told us what the difference meant in production for them: One team migrated to Go after cold starts on AWS Lambda in Python exceeded 10 seconds; another measured a tenfold improvement in request times after rewriting their Python service in Go.

But raw language speed matters only when your service is actually compute-bound or latency-sensitive, and plenty of services are neither. Equally importantly, the Python interpreter is not slow where it counts most for one of its largest user bases. In machine learning, data work, and numerical computing, libraries like NumPy and PyTorch offload expensive operations to optimized native code, making interpreter overhead much less relevant. When we asked migrating developers where they decided to keep Python code, they confidently listed all AI and ML workflows – LLMs, chatbots, RAGs, and model training.

Memory management

Memory management is another important part of the performance discussion. Both languages manage memory for you, but differently: Go uses a tracing garbage collector, while CPython uses reference counting alongside a cyclic garbage collector. This affects what developers can fine-tune and what they need to design around.

Go’s GC runs concurrently with the program and is designed to minimize stop-the-world pauses. Developers can tune this behavior: GOGC and GOMEMLIMIT set the pacing, and the memory profile (heap and allocs) tells you when a service allocates too hard.

In CPython, an object is typically freed the moment its last reference disappears. Memory use is predictable, and pauses are rarely worth measuring, but the cost is spread across every operation, and there’s little to room tune it.   

Concurrency and parallelism

Even though both languages support concurrency, developers we talked to cited better concurrency as the reason why they switched from Python to Go.

Goroutines are central to Go’s design. They are lightweight threads managed by the runtime. They are cheap to create, carry little overhead, and scale to thousands of concurrent operations. The runtime handles the scheduling, which makes the model simpler than in Python – there is no separate async/await syntax, so a function is just a function. However, if you start to spawn goroutines without controlling their lifecycle, you’d better be ready to deal with race conditions, leaks, and deadlocks before long.

The other half of the concurrency model in Go are channels – typed conduits that pass values between goroutines and synchronize them. Instead of guarding shared state with a lock, Go can pass the value to whichever needs it next. A select statement waits on several channels at once, which is how Go expresses timeouts, cancellation, and fan-in using built-in language constructs. These very same primitives also produce common Go bugs: goroutine leaks and more deadlocks. 

Meanwhile, Python relies on standard libraries, single-threaded event loops, and sub-processes to work around interpreter constraints. It offers threading, but in CPython, the global interpreter lock (GIL) has historically prevented threads from running Python bytecode simultaneously, making it ineffective for CPU-bound parallelism. For I/O-bound concurrency, there is asyncio, and for real parallelism there is multiprocessing, but that adds memory overhead and requires inter-process communication.

It’s worth noting that the GIL itself is no longer a necessity – starting with Python 3.13, PEP 703 introduced an officially supported free-threaded build, and 3.14 continues that work. Most production code still runs with the GIL in place, but with each new release, the claim that “Python can’t do real threading” is becoming less true.

# Python: async/await, and the coloring that comes with it
async def fetch_all(urls):
    return await asyncio.gather(*(fetch(u) for u in urls))

// Go: ordinary functions run concurrently
var wg sync.WaitGroup
for _, u := range urls {
    wg.Add(1)
    go func() {
        defer wg.Done()
        fetch(u)
    }()
}
wg.Wait()

The approach to concurrency gives Go much of its practical advantage in high-throughput services, and having experienced both, our interviewees praised Go for being “async by default” and better suited for high-concurrency components, and they described it as having a superior concurrency model that’s highly performant and appealing for real-world use cases.

Libraries, frameworks, and IDEs

Go’s self-contained ecosystem

Go was never built as just a programming language, but as an ecosystem. As the Go team puts it, the goal was “an end-to-end platform with tooling all around the software development lifecycle” (Google for Developers), with formatters, test frameworks, dependency management, and security tools included in the toolchain, alongside a standard library that’s comprehensive enough to make external frameworks optional, if not irrelevant.

The Developer Ecosystem 2026 survey shows how successful the Go team has been in achieving that goal. For example, among Gophers, the standard html/template and text/template library packages are used by 31% and 22% of developers respectively, whereas the only other significant alternative – templ – is used by 11%.

Almost half of Gophers either don’t write unit tests or don’t use testing frameworks: 14% don’t write unit tests at all, and 35% don’t use frameworks for testing – the testing package provides everything they need in that regard. Others may also reach for tools such as Testify (31%) or GoMock (25%). The remaining libraries are far less popular.

For web development, 46% of developers use the standard net/http library, compared to 48% who use Gin. This proves that where a third-party framework exists, it doesn’t compete with the many other frameworks, but only with the standard library. Oftentimes, it loses.

Python’s broad ecosystem

Python’s ecosystem works the other way around. The Python Package Index (PyPI) is vast and mature, and where the standard library stops, several strong alternatives tend to compete.

Take web frameworks, for example. Half of Python developers use FastAPI, and Django (31%) competes closely with Flask (30%). The data science landscape is more fragmented, and developers use those libraries simultaneously: 69% use NumPy, 68% use pandas, 50% use PyTorch, 49% use Matplotlib, etc. With Python, there’s often no clear leader, and choosing the right framework or library is part of the job.

IDEs & editors

When it comes to editors, we see a similar pattern unfold. Go has an excellent language server, which makes general-purpose editors a viable alternative to a Go-specific IDE. When asked which IDEs or editors they use for Go development, 49% reported using GoLand, while 48% reported using VS Code. 

One reason is how much of Go’s surrounding ecosystem developers can access without having to assemble it themselves. GoLand installs with a test runner, debugger, coverage, and profilers wired into the toolchain. The analysis runs across the whole project, rather than only the open file. Interprocedural nil checks and unhandled-error inspections can catch the mistakes a Python developer might otherwise only see at runtime. Suffice it to say, Alan Donovan of the Go team has also described traditional IDEs like ours as setting “the gold standard” for refactoring because of their tight integration between the analysis logic and the user interface.

The Python side is more fragmented and has more Python-specific options, such as Jupyter Notebook and, of course, PyCharm. The latter is used by 30% of Python developers (PyCharm – 22%, PyCharm Pro – 8%), with VS Code at 41%. Notebooks explain much of the fragmentation, since data and service work don’t necessarily require the same toolset. Whatever you build in Python, PyCharm has first-class support for your stack, your frameworks, and your workflow – for web, data, and ML workflows – with notebooks running alongside a debugger and type checker. It parses your code and resolves usages, types, and dependencies throughout your entire project. So you always know exactly what your code does and what will break if it changes, for you and your agents alike.

If you use both Go and Python – as a quarter of Gophers do – it’s worth noting that both GoLand and PyCharm are built on the same platform, so keymaps, shortcuts, and settings sync between them. GoLand also supports Python functionalities through the Python Community Edition plugin.

Python vs Go – broader landscapes

The language itself is one thing, but many other factors go into deciding which language you choose to work with. Developers consider what the languages are used for, who uses them, and how they’re maintained. In this section, we’ll provide insights on the factors that shape the broader landscapes of Python and Go.

Go developers are more senior

Across all questions about age, experience, and seniority in the Developer Ecosystem 2026 survey, Gophers tend to be further along in their career than Python developers.

On overall professional coding experience (not just experience in their main programming language), 20% of Go devs report 16 years or more, compared to 12% among Python developers. At the other end, 9% of Gophers have 1–2 years of experience, compared to 20% of Python developers.

Most Go professionals have senior roles, at 60%, with only 2% being trainees and 10% juniors. Meanwhile, 42% of Python professionals are seniors; trainees (6%) and juniors (19%) make up a quarter of the cohort.

72% of Go developers are employed full-time, compared to 51% of Python developers. Students and working students make up 8% of Gophers, compared to 23% of the Python population.

Finally, by age, the largest group of Go developers is aged 30–34 (23%), compared to Python’s largest group, aged 21–24 (22%).

These differences align with the point we made earlier: Python is a very popular first language for learners, while Go is almost never chosen as a first language and instead is picked up by more experienced developers later in their careers.

It’s important to keep this in mind as you read the following sections on developer demographics, because a lot of these numbers may look like differences between two languages, where in reality, they’re just differences between a senior population vs a general one that you would see regardless of what ecosystems you compare.

Salary follows seniority

Salary bands clearly show how experience affects other demographic data. Go developers are better represented in the top bracket: 9% report earning more than $180,000 a year, compared to 6% of Python programmers. Meanwhile, Python users cluster more in the lowest brackets.

However, if we look only at full-time developers, the differences are no longer statistically significant. The premium is attached to the seniority and employment status, not the primary language. In other words, learning Go itself will not guarantee you a higher salary, but the roles that require Go generally pay better.

What they build

The Developer Ecosystem survey also asks people what functionality their code implements.

Go developers more frequently reported:

• 69% – providing APIs and services (Python – 41%)
• 63% – integrating with APIs and services (Python – 47%)
• 51% – application logic and workflows (Python – 37%)
• 47% – user interface (Python – 41%)
• 35% – libraries and frameworks (Python – 26%)

Meanwhile, Python developers reported higher rates for:

• 38% – data processing and analytics (Go – 30%)
• 36% – artificial intelligence and machine learning (Go – 20%)
• 32% – data science and data analytics (Go – 15%)

The question of where their code is executed adds more context: A vast majority of Go developers target server, infrastructure, or cloud environments (74% compared to 57% of Python developers. On the other hand, 66% of Python developers target the desktop (Go – 47%).

Python is clearly a general-purpose language with a stronghold in AI, ML, and data work that favors local execution, while Go is heavily oriented towards networked services and infrastructure.

There is, however, a particularly interesting line item in the set: integration with AI. While AI development per se is a Python domain, integrations are not. 46% of Go developers report implementing AI integrations, against Python’s 44%, almost equal numbers. Asked about the types of development they do at work, 51% of Python developers reported working with AI and AI tools, compared to Go’s 48%. Go engineers may not train the models, but they are building AI-powered products at roughly the same rate as Python developers.

DevOps: Same job, different toolbox

One other point of convergence between Python and Go developers that deserves its own section is DevOps. Both groups are involved in that area, though Python developers (31%) somewhat less than Gophers (45%). The two groups also tend to use different tools.

• For orchestration, Go developers are more likely to use tooling overall, and especially Kubernetes. Only 8% of Go developers don’t use container orchestration tools (compared to 17% for Python); 58% use Kubernetes (compared to 39%). The engineers we talked to cited Go’s deep integration with Kubernetes as one of the reasons they migrate entire services from Python to Go.
• For configuration management, Terraform leads both groups. However, Python developers lean more toward cloud-native IaC tools (29% for Python, 23% for Go) or use nothing (21% for Python, 15% for Go), while Go developers choose GitOps more often (27% for Go, 21% for Python).
• When it comes to DevOps practices, Go developers report using them more often overall, with one exception: AI-driven operations (12% of Go developers and 18% of Python developers). Python developers are also more likely to use no DevOps practices at all (6% for Python, 2% for Go).

So even if you move from Python to Go in the same type of role, the toolset change might be as big an adjustment as the language change itself.

Operating systems

A curious difference surfaced when we asked developers about the operating system they use for their development environment. Go developers work on macOS (70%) and Linux (57%) significantly more than on Windows (44%). Meanwhile, Python developers use Windows first (66%), then split almost evenly between macOS (54%) and Linux (52%).

For developers planning to migrate to Go, sticking with Windows doesn’t have to complicate deployment, even when the target is a Linux server. For pure Go, building a Linux binary from any of the three operating systems takes no more than two environment variables, while a Python deployment still has to find a matching interpreter and native packages, wherever it ends up.

Two different approaches to AI

We’ve already discussed using Python and Go for AI development, but what about using AI for Go and Python development? Both groups use AI heavily at work, but there’s a difference in how and what they use it for.

Go developers report using AI daily for tasks such as editing code across multiple files (60% vs 52%), agentic development of features or programs (51% vs 41%),  generating commit messages (51% vs 36%) and tests (47% vs 34%), or performing code reviews (39% vs 34%).

Meanwhile, Python developers lead on one task: learning new things about coding and software development (38% of Python developers vs 31% of Go developers), which is consistent with earlier data showing a larger early-career population among Python developers.

There is, however, a long list of tasks where the two groups are indistinguishable: refactoring, debugging, checking code for potential issues, asking general questions, or generating boilerplate – even though you’d expect Go developers to jump on the last one much more, considering the verbosity of the language.

So AI-assisted coding is common in both ecosystems. Go developers have simply wired it into the production chores of a maintenance-heavy codebase: commits, tests, reviews, and multi-file edits.

The groups also tend to use different tools. Go developers are more likely to use terminal and agentic tools like Claude Code (45% vs 40%), OpenAI Codex (24% vs 17%), Cursor (17% vs 12%), and OpenCode (12% vs 6%). Python developers, in turn, reach more for general-purpose assistants and Google or Microsoft surfaces like ChatGPT (20% vs 16%), Google Antigravity (9% vs 5%), and Microsoft 365 Copilot (4% vs 1%).

One number that has to change

Software development remains a heavily male-dominated field, and the gap is particularly pronounced in Go, which – as it’s typically people’s second language – inherits whatever the pipeline hands it and exacerbates it further. In our sample, only 1.8% of Go developers identify as female, compared to 5.8% of Python developers.

Transitioning from Python to Go

One-way path well traveled

You may ask why we’re focusing on transitioning from Python to Go, not the other way around. The data leaves little doubt about the path developers are taking. Python is already the most common second language among Go developers – 35% of Gophers declare it as their other primary language. But most Go developers (70%) say they have no intention of adopting another language in the next 12 months, and if they do, they name Rust (16%), rather than Python (2%). On the other hand, 10% of Python developers (a much larger cohort in terms of absolute numbers) plan to pick up Go, roughly 5.5 times the rate at which Go developers plan to pick up Python.

Why make the switch from Python to Go?

Despite working in different roles and on different products, the developers we interviewed gave consistent reasons for transitioning from Python to Go. These were:

• Better performance and speed, including cold starts, latency, and operating cost.
• Easier and more effective concurrency model.
• Type safety that catches failures that would otherwise reach production at compile time.
• Proximity to the platform, since Kubernetes and much of the cloud-native toolchain are written in Go, so teams working on that infrastructure already interact with Go daily.

What they gained and what they lost

Perceived potential gains are one thing, but did Go actually deliver? According to the developers we interviewed, it did in the areas described above. But they also found benefits they weren’t expecting: predictable formatting and linting that contributed to “clean code”, out-of-the-box tooling, and simplicity comparable to Python’s.

But Go also has flaws that are obvious to developers coming from Python. Just like the reasons for adopting Go were consistent, so were the complaints:

• Explicit error handling that’s fundamentally different from Python, less intuitive, and more repetitive.
• Overall verbosity and opinionatedness, with more lines needed to convey the same behavior and fewer ways to express it.
• An object model that makes testing and mocking noticeably more difficult.
• Library gaps, especially for testing.

Did they really leave?

“Migrating”, “transitioning”, and “switching over” may all sound like final decisions to leave Python behind, but that’s not really the case. Not one of the developers we interviewed removed Python from their stack completely. They kept it for artificial intelligence and machine learning, data science, and pipelines on platforms that only support Python, as well as simply because they work on larger teams that already know Python and are productive in it.

The realistic outcome of “moving to Go” is not becoming a monoglot, but having a polyglot codebase where each language is used for what it’s best suited for.

Conclusions

So, which language should you choose? Is it worth dropping Python in favor of Go? We hope that by now the answer is clear – it’s not a matter of which language is better, but which one is a better fit. Based on our data and developer testimony, here’s what we found.

Go may be a better fit if:

• Performance is crucial for your app: Latency, throughput, or cold starts are problems you actually have and can measure.
• The workload is concurrent, especially if it is I/O-heavy.
• The service is long-lived and maintained by a team, not one person.
• The system already depends heavily on Kubernetes and the rest of the cloud-native toolchain.

Python may be a better fit if:

• The work is machine learning, data science, or numerical computing.
• Your platform only accepts Python, as managed ETL and notebook environments frequently do.
• Iteration speed matters more than runtime speed, which is often the case for internal tooling and prototypes.
• The library you depend on has no Go equivalent; for a lot of the scientific stack, there isn’t one.

Language features alone rarely determine the best choice. When deciding what to build with, the teams weigh in the toolchain they already run, the languages their colleagues already know or are willing to learn, and how much of the existing system they are willing to change. Those things carry more weight than any benchmark, and they are why developers we talked to ended up using both languages, instead of choosing one.

Frequently asked questions

Can Go replace Python completely?

For a lot of backend work, yes, in principle. In practice, it doesn’t. None of the developers we interviewed removed Python from their stack completely. They kept it for machine learning and data science, for pipelines on platforms that only accept Python, and because the teams around them already know it. In the Developer Ecosystem 2026 survey, about a quarter of Go developers report using Python in the same project.

Is Go good for data engineering?

It depends on which end of the pipeline you mean. Go works well for moving data around, including ingestion services, stream processing, API integrations, and CLI tooling, and 30% of Go developers report working on data processing and analytics, compared with Python’s 38%. The analytical end is a different story: Only 15% of Gophers do data science and data analytics, against 32% of Python developers, and the library ecosystem reflects that.

How long does it take a Python developer to learn Go?

Days to weeks for the language itself. The syntax is small, gofmt settles the formatting arguments, and developers we talked to said Go “feels as easy to write as Python”. What takes longer is everything around it: system concepts such as pointers, interfaces, goroutines, and channels, as well as the habits Go asks you to change, from explicit error handling to designing test seams instead of patching at runtime.

The bigger adjustment may not be the language after all. Go work tends to sit closer to Kubernetes, Terraform, and GitOps, so if you’re moving into a Go team from a Python one, budget as much time for learning the toolchain as for the syntax.

Is Go good for machine learning?

For training models, probably not. 20% of Go developers work on artificial intelligence and machine learning, against 36% of Python developers, and the developers we interviewed were blunt about why: Python has the libraries, while Go lacks equivalents.

When it comes to building products around the models, it’s a different story. 46% of Go developers implement AI integrations, against Python’s 44% – a negligible difference. Gophers generally aren’t training the models, but they sure are serving them.

Can Go and Python be used in the same project?

Not only can they be used together, they often are. 27% of Go developers report using Python alongside Go in the same project, behind TypeScript (43%) and just ahead of JavaScript (24%). We know from interviews that the usual division of labor follows each language’s strengths: Go for the networked services and infrastructure, Python for data work, machine learning, and anything that depends on the scientific library stack.