Python vs Go for AI Agents: Production Benchmarks
Python is the language of AI training. But for AI serving and orchestration layers? Go eats Python for lunch. Here are the benchmarks from migrating a high-throughput agent router.
We love Python. It has PyTorch, NumPy, and LangChain. It is the undisputed king of building models. But when you deploy those models behind an API handling 1,000 RPS, Python's GIL (Global Interpreter Lock) becomes a choke point.
At Shahriar Labs, we migrated our primary "Orchestrator Service"—the router that decides which LLM to call—from FastAPI (Python) to Gin (Go).
The Benchmark: 10k Requests
Scenario: Receive a JSON payload, validate schema, normalize text, call an external LLM (simulated 200ms latency), and return response.
| Metric | FastAPI (Python) | Gin (Go) | Winner |
|---|---|---|---|
| Throughput | 450 req/sec | 3,200 req/sec | Go (7x) |
| Idle memory | 120 MB | 8 MB | Go (15x) |
| CPU utilization | 1 core (GIL-bound) | All 8 cores | Go |
| Type safety | Runtime (Pydantic) | Compile-time (struct) | Go |
| Prototyping speed | Fast | Moderate | Python |
1. Throughput (Req/Sec)
- FastAPI (Uvicorn): 450 req/sec (maxed out 1 CPU core due to GIL)
- Go (Gin): 3,200 req/sec (automatically utilized all 8 cores)
Winner: Go (by 7x)
2. Memory Footprint (Idle)
- fastapi-container: 120MB
- go-binary-container: 8MB
Winner: Go (by 15x)
3. Developer Experience
This is where Python usually wins. But using robust typing in Go (struct) prevents a class of runtime errors that Pydantic only catches during execution.
The Hybrid Architecture
We didn't kill Python. We just moved it.
- Go handles: HTTP handling, Websockets, Auth, Rate Limiting, JSON validation, and Request Routing.
- Python handles: The core inference logic (PyTorch/TensorFlow).
The Go service calls the Python service (via gRPC) only when heavy math is needed. For 90% of requests (cache hits, auth checks, simple routing), Python is never touched.
Conclusion
If you are building an AI startup, use Python to prototype. But if you plan to scale, learn Go. Your AWS bill will thank you.
FAQ
Q: Is Go hard to learn for Python devs? A: The syntax is stricter, but the concurrency model (Goroutines) is much easier than Python's AsyncIO.
Q: Why not Rust? A: Rust is great, but Go's compilation speed and "batteries-included" standard library make it faster for iteration.
Q: Do you use LangChain in Go? A: We use LangChainGo, but mostly we write raw API wrappers for better control.
Q: Does this affect latency? A: The gRPC hop adds <1ms latency, which is negligible compared to the 200ms LLM inference time.
Q: What about Mojo? A: Mojo is promising but not yet mature enough for our production stack.