JetBrains’ Developer Ecosystem Survey 2026 presents a nuanced view of Python and Go. Python remains the most popular programming language, and it is commonly used alongside Go among developers.
WHAT THE DATA REVEALS ABOUT DEVELOPERS
The 2026 survey gathered 28,945 responses. Among respondents, 11,298 selected Python as a primary language, 1,836 selected Go as a primary language, and 619 respondents selected both languages, indicating significant cross-use between the two ecosystems. TechStaged has also covered CI/CD in 2026: JetBrains Finds AI Use Growing Even as Self-Hosting Persists.
DESIGN AND RUNTIME DIFFERENCES THAT MATTER IN PRODUCTION
Python favors expressive code and rapid iteration, while Go emphasizes explicit, standardized code that is easier to maintain across teams.
Go is statically typed, making refactoring safer but more verbose, and its formatting is standardized by gofmt. Python is dynamically typed, with optional type hints increasingly used in larger codebases.
- Expressive, beginner-friendly Python vs explicit, uniform Go
PERFORMANCE AND CONCURRENCY: WHERE EACH LANGUAGE SHINES
Go is noted for raw performance and concurrency, commonly used in cloud infrastructure. Python remains strong in machine learning, data science, and scientific computing because optimized libraries offload heavy work to native code.
- Go: concurrency with goroutines and channels; Python: interpreter overhead offset by native libraries
MIGRATION EXPERIENCES AND PRACTICAL PATTERNS
Interviews with developers who moved production workloads from Python to Go describe improved latency in some cases. Some teams reported a tenfold improvement in request times after rewriting in Go, while others noted that cold starts in Python-based serverless environments could exceed 10 seconds.
- Migration benefits are workload-dependent
- Many teams maintain both languages: Go for services/infrastructure, Python for AI/ML workloads
CONTEXT, CAVEATS, AND WHAT COMES NEXT
Surveys provide directional insight but are not universal benchmarks. Microbenchmarks don’t always translate to real workloads, and memory-management differences (Go’s tracing garbage collector vs CPython’s reference counting and cyclic GC) shape performance in production.
- Be mindful of workload types and ecosystems when choosing between Python and Go
RELATED COVERAGE
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- Developer Tools articles
SOURCES
- The JetBrains Blog: Python vs Go: A Practical Comparison for Developers Published · Primary source
