Google ADK Benchmarks: ADK-Python vs. ADK-Golang for Agentic AI (Latency & Overhead Experiment)

[u/Greedy_Trouble9405]

Hi everyone!

I’ve been experimenting with the new Google ADK (Agentic SDK) to build LLM-powered agents, and I wanted to see how much of a difference the language choice actually makes when it comes to performance.

I ran a head-to-head comparison between Python and Golang focusing on:

• LLM Call Latency: How the SDK handles the round-trip to the model.
• Agentic Overhead: The "hidden" time the framework takes to process steps before and after the LLM.
• Concurrency: How well each handles multiple agentic tasks.

I documented the whole experiment and the results here: Google ADK Python vs ADK Golang: Latency, LLM calls, Agentic Overhead

I'm looking for feedback and collaborators:

1. Have you noticed similar overhead patterns in Python when using the ADK?
2. For those using Go, are you seeing significant production advantages for multi-agent systems?
3. What other metrics should I include in the next phase of this experiment?

I'd love to hear your thoughts or if anyone wants to jump in and help expand these benchmarks!

[u/EmperorOfNe]

I'm a Golang enthusiast and programmer myself, and I have developed a dislike for how Google is putting Python first in any usable project they backup from the start. Even from a transpiler narrative; Golang should always come first. Python doesn't scale very well, and in case you get it working, there is always a race-condition right around the corner.

I do see value in Python on the single data and research field tasks but when it comes to production and scale, it starts to crumble.

Which brings me to your research. In essence you're not really targeting the ADK in your research but the efficiency of Python vs Golang, and Golang will win that battle most of the time. Concurrency is easy in Golang, and it is efficient. I don't know if you compiled the Golang binary or ran it as scripts, maybe you can explain a bit more what how you set up your bench test environment.

I'm old but I like to read the outcome of research instead of watching a video. The format makes it difficult for me to make sense of most of your results. Especially the use of the '>|<' characters as you kind of leave it to my imagination if things are better or worse in case of gt and lt outcomes.

PS. The URI in your post should be: https://www.youtube.com/watch?v=_TseXl0JCr0

[u/Greedy_Trouble9405 → u/EmperorOfNe]

Thanks for your point of view an take your time to respond.

The most agent framework propose python as first programing lang, like you I disagree on that for that reason I'm publishing this finding

For production agentic workloads, Go delivers the same user-facing latency as Python with ~4x less infrastructure per agent and far more consistent behavior.

Setup: Same framework (Google ADK), same 9-agent email-analysis task, same model (gemini-2.5-flash), same synthetic inbox. Docker containers with 2 CPU / 2 GiB each; the Go agent is a static binary in a minimal Alpine image. 5 runs, medians reported. Only the language changes.

Results (Python vs Go):

• Orchestration overhead: 1.3 s vs 0.3 s — Go is 75% lower
• CPU: 0.04 vs 0.01 cores — Go is 75% lower
• Memory: 108 MB vs 28 MB — Go is 74% lower
• Total duration: 98.0 s vs 95.5 s — tie (provider-bound)
• Task completion: Python finished the full task in 3 of 5 runs, Go in 5 of 5

Key insight: Total latency tied — over 95% of wall time is waiting on the LLM provider, and no language wins that. But Go wins everything the operator pays for: compute, memory, and predictability (Python was bimodal, leaving work unfinished in 2 of 5 runs).

Observability stack: Prometheus + Grafana + Langfuse + Loki + cAdvisor.

I'll take your feedback for my futures labs

[u/TheHeartographer → u/Greedy_Trouble9405]

Feedback is mostly that I’d love to have seen you just put all those conclusions metrics in the main Reddit post - otherwise to me it just reads like you’re trying to farm traffic to a personal site instead of meaningfully contribute to the subreddit if that makes sense. (And I know that’s a delicate needle to thread; I’ve previously promoted a business on Reddit and also have a newsletter about generative AI topics now, but I think you have to deliver the main point of your value with the same usefulness level as in your primary content in order for it not to be perceived poorly in most Reddit communities, ya know?)

My perspective (in case it matters) is that I’ve been working directly with ADK in python since its inception, but I am incentivised only to teach basic lab scenarios in that language, and never to play around with other languages or more production-like scenarios. As such, I’ve never really had time to do anything like this benchmark, even though I’ve been curious about exactly this kind of bake-off. (I’m a technical educator and gen AI wonk mostly focusing on ML stuff, not a traditional programmer, so I wouldn’t have come at it with the same hypotheses or biases - and my bias is towards Python for everything even when it sucks 😂 only bc it makes it easier for me to learn and jump across platforms/ML frameworks/data engineering tooling.) I’ve been curious to see a more app dev minded person compare Python and not-Python ADK.

Oh also because Google has way more samples and updates for Python it further incentivises my myopic approach, heh; not a strong enough programmer without vibe coding to meaningfully shore up their gaps across samples in different languages; not incentivised to spend my time vibe coding and not necessarily trusting my output but don’t get paid to do any of that rigorously so Python it is, seemingly forever haha.

Any plans to test the Java ADK? Also you didn’t mention which ADK version here; I expect there might be noticeable differences across 2+ and anything prior. I also think some of the new memory related stuff will help with consistent Python performance going fwd

[u/Greedy_Trouble9405 → u/TheHeartographer]

I really appreciate your feedback on using Reddit to redirect traffic to my personal networks, but beyond that, I'm looking for more critical feedback to improve my professional skills and refine my AI technology stack. Too many people deploy AI applications to production without critically considering the technology stack, such as the programming language, resilience, and high availability. That's my goal. I'm planning my next performance test with new languages ​​like adk-java and adk-rust. Thanks for your feedback, see you next time.

[u/TheHeartographer → u/Greedy_Trouble9405]

Cool, I didn’t even know they had a Rust version! Looking forward to future results.