GoModel – an open-source AI gateway in Go

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Hi, I’m Jakub, a solo founder based in Warsaw. I’ve been building GoModel since December with a couple of contributors. It's an open-source AI gateway that sits between your app and model providers like OpenAI, Anthropic or others. I built it for my startup to solve a few problems: - track AI usage and cost per client or team - switch models without changing app code - debug request flows more easily - reduce AI spendings with exact and semantic caching How is it different? - ~17MB docker image - LiteLLM's image is more than 44x bigger ("docker.litellm.ai/berriai/litellm:latest" ~ 746 MB on amd64) - request workflow is visible and easy to inspect - config is environment-variable-first by default I'm posting now partly because of the recent LiteLLM supply-chain attack. Their team handled it impressively well, but some people are looking at alternatives anyway, and GoModel is one. Website: https://gomodel.enterpilot.io Any feedback is appreciated.

Visit product View on Hacker NewsApr 21, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, dock, models · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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