Technical
2026-03-158 min read

Why We Chose Go for Our Agent Infrastructure

Architecture decisions, performance benchmarks, and why Go outperformed alternatives for our agent platform.

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The Decision

When we started building Trefur, we faced a fundamental question: what language should our backend be in?

Options we considered:

  • Node.js — Fast, familiar, great ecosystem
  • Python — ML/AI native, but slow
  • Go — Fast, reliable, great for concurrent operations
  • Rust — Fastest, but high learning curve
  • We chose Go. Here's why.

    The Requirements

    Our infrastructure needs to:

  • Handle high throughput — Many concurrent agent traces
  • Process in real-time — Sub-second trace processing
  • Scale horizontally — Add capacity by adding machines
  • Be reliable — 99.9% uptime is table stakes
  • Why Go Beat the Alternatives

    Go vs Node.js

    Node.js is great for I/O-bound workloads. But trace processing is CPU-intensive. Go's compiled nature shines here.

    Go vs Python

    Python is non-negotiable for AI/ML. But for the infrastructure layer?

  • 10-50x slower than Go
  • GIL limits concurrency
  • Memory usage is high
  • We use Python where it makes sense. Go for the core platform.

    Go vs Rust

    We love Rust. But:

  • Steeper learning curve for the team
  • Slower development velocity
  • Compile times are painful
  • For a startup moving fast, Go's balance of performance and productivity won.

    Real Benchmarks

    Here's what we saw in production:

  • Throughput: Go handled significantly more traces per second than our Node.js prototype on the same hardware
  • Latency (p99): Single-digit milliseconds in Go vs ~80ms+ in Node.js
  • Memory: Go's footprint was a fraction of Node.js — important at scale
  • The numbers spoke for themselves. Go gave us the headroom we needed.

    The Trade-offs

    Go isn't perfect:

  • No generics before Go 1.18 (we use them now)
  • Error handling is verbose
  • Dependency management (go.mod) improved but not perfect
  • But for our use case, the benefits outweigh the drawbacks.

    Lessons Learned

  • The right tool for the right job — Use the best language for each layer
  • Benchmark with realistic data — Synthetic benchmarks lie
  • Think about scale from day one — Rewrites are expensive
  • Conclusion

    Go gave us the performance we needed without sacrificing development speed. For agent infrastructure — where throughput and latency matter — it's been the right choice.

    Would we use it for everything? No. But for the core infrastructure, Go is unbeatable.

    Ready to put this into practice?

    Start tracing your AI agents in 5 minutes with Trefur Observe.