Go vs Python: A Network Engineer’s Perspective

After years of championing Go for network automation and infrastructure projects, I’ve recently dipped my toes into Python. While Python’s ubiquity in the networking world made this exploration inevitable, my experience has only reinforced my conviction that Go remains the superior choice for building robust, production-grade network applications.

The Allure of Python

Python’s appeal is immediately obvious. The gentle learning curve, extensive libraries, and readable syntax make it incredibly accessible. Within days, I was able to prototype scripts that would have taken more initial setup time in Go. For quick automation tasks and data analysis, Python feels effortless—there’s a reason it’s become the de facto language for network automation starter projects.

The extensive ecosystem surrounding networking is impressive as well. Libraries like Netmiko, NAPALM, and Nornir have created rich abstractions for device interaction. When you need to quickly extract data from a Juniper router or reconfigure an access list across multiple Cisco switches, Python certainly gets you moving fast.

Why Go Still Wins for Mission-Critical Networking

Despite Python’s conveniences, my time with it has only strengthened my appreciation for Go’s strengths, particularly for production network applications:

Type Safety Prevents Operational Incidents

Python’s dynamic typing offers flexibility and speeds up development, but it can sometimes postpone error detection to runtime. This represents a different philosophy about type checking than Go’s approach of catching issues at compile time.

Go’s static typing catches these issues at compile time, preventing countless potential incidents. When pushing changes to thousands of network devices, this safety net isn’t merely convenient—it’s essential.

Concurrency Model Matches Network Operations

Go’s goroutines and channels provide an elegant solution to the inherently parallel nature of network operations. Need to collect data from hundreds of devices simultaneously? Go’s concurrency primitives make this straightforward with a model that many developers find more intuitive than alternative approaches.

For large-scale configuration operations, Go’s concurrency model provides a clean separation of concerns that helps manage the complexity of parallel device interactions.

Deployment Simplicity Reduces Operational Risk

Python’s dependency management remains a persistent headache. Virtual environments, pip, conda, poetry—the ecosystem is fragmented and complex. Deploying Python applications across different environments often leads to “it works on my machine” scenarios.

Go compiles to a single binary with all dependencies included. This simplifies deployment across our network management systems and eliminates an entire category of operational problems. One binary, no dependencies, no surprises.

Performance Matters at Scale

For certain network operations that process large volumes of data, Go’s performance characteristics can be advantageous. The language was designed with concurrency and efficiency in mind, which can be beneficial when developing systems that need to handle high throughput.

When working with telemetry systems that process data from many sources simultaneously, these design considerations may become more relevant.

Python’s Undeniable Strength in AI

One area where Python has genuinely impressed me is in the generative AI space. Having recently worked with frameworks like LangChain, I’ve seen firsthand how Python’s ecosystem has become the dominant force in AI development. The speed at which you can prototype complex LLM applications using Python is remarkable.

LangChain and similar frameworks provide elegant abstractions for working with large language models, with Python’s dynamic nature allowing for rapid iteration as this technology evolves. The tight integration with popular AI models and vector databases has created a development experience that’s currently unmatched in Go.

While there are efforts to build similar tooling in Go, the Python ecosystem’s head start in this domain is substantial. For teams looking to integrate generative AI capabilities into their network operations—perhaps for automated documentation, configuration analysis, or even natural language interfaces to network commands—Python currently offers the path of least resistance.

Finding the Right Balance

Despite my clear preference for Go for core networking infrastructure, I’ve found value in using both languages strategically:

Go excels for:

• Long-running services (controllers, collectors, agents)
• High-performance data processing pipelines
• Critical configuration management tools
• Applications requiring parallel execution
• Tools that need distribution across diverse environments

Python works well for:

• Generative AI and LLM applications
• AI/ML model development and deployment
• Ad-hoc troubleshooting scripts
• Data analysis and visualisation
• Quick prototyping
• Integrating with existing Python-based tools

The Path Forward

For networking professionals considering which language to invest in, I recommend starting with Python for its accessibility but ultimately transitioning to Go for production systems. The learning curve is steeper, but the long-term benefits for reliability, performance, and operational simplicity are substantial.

Go’s compilation step may seem like an inconvenience compared to Python’s interpret-and-run approach, but in production environments, this “inconvenience” catches countless errors before they reach your network infrastructure.

As networks grow more complex and automated, the guarantees that Go provides—type safety, memory safety, concurrency, and performance—become increasingly valuable. Python may let you move quickly at first, but Go helps you move confidently when it matters most.

For anyone building systems that operate critical network infrastructure, that confidence is invaluable.