SaaS Metrics
Gemini Executive Synthesis
GoModel, an open-source AI gateway in Go.
Technical Positioning
A lightweight, open-source AI gateway (single Go binary, ~17MB Docker image) that provides usage tracking, cost management, model switching, debugging, and caching, positioned as an alternative to heavier solutions like LiteLLM, especially after security incidents.
SaaS Insight & Market Implications
GoModel addresses critical operational and cost management pain points for enterprises integrating multiple AI models. Its positioning as a lightweight, open-source AI gateway offering usage tracking, model switching, debugging, and caching directly impacts AI spend optimization and operational flexibility. The explicit comparison to LiteLLM and mention of its supply-chain attack highlights a market demand for secure, performant, and transparent alternatives in AI infrastructure. For B2B SaaS, GoModel represents a valuable control plane for AI consumption, enabling organizations to manage multi-provider strategies, enforce cost controls, and enhance observability, all crucial for scaling AI adoption responsibly.
Proprietary Technical Taxonomy
Raw Developer Origin & Technical Request
Hacker News
Apr 21, 2026
Show HN: GoModel – an open-source AI gateway in Go
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: gomodel.enterpilot.ioAny feedback is appreciated.
Developer Debate & Comments
Given this app seems to expose itself via REST calls, why would anyone care that it’s written in Go? I guess it matters to potential contributors but the majority of interest would be from users.
Hey, this looks super nice. I do like the 'compact' feel of this. Reminds me of Traefik. It seems very promising indeed!One problem I have is that yes, LiteLLM key creation is easier than creating it directly at the providers and managing it there for team members and test environments, but if I had a way of generating keys via vault, it would be perfect and such a relief in many ways.I see what I need on your roadmap, but miss integration with service where I can inspect and debug completion traffic, and I don't see if I would be able to track usage from individual end-users through a header.Thank you and godspeed!
This is awesome work, thanks for sharing!How do you plan on keeping up with upstream changes from the API providers? I have implemented something similar, and the biggest issue I have faced with go is that providers don’t usually have sdk’s (compared to javascript and python), and there is work involved in staying up to date at each release.
Looks nice, thanks for open sourcing and sharing.I'm all in on Go and integrating AI up and down our systems for https://housecat.com/ and am currently familiar and happy with:https://github.com/boldsoftware/shelley -- full Go-based coding agent with LLM gateway.https://github.com/maragudk/gai -- provides Go interfaces around Anthropic / OpenAI / Google.Adding this to the list as well as bifrost to look into.Any other Go-based AI / LLM tools folks are happy with?I'll second the request to add support for harnesses with subscriptions, specifically Claude Code, into the mix.
I have written and maintained AI proxies. They are not terribly complex except the inconsistent structure of input and output that changes on each model and provider release. I figure that if there is a not a < 24 hour turn around for new model integration the project is not properly maintained.Governance is the biggest concern at this point - with proper logging, and integration to 3rd party services that provide inspection and DLP type threat mitigation.
I wrote a similar golang gateway, with the understanding that having solid API gateway features is important.https://sbproxy.dev - engine is fully open source.Another reason golang is interesting for the gateway is having clear control of the supply chain at compile time. Tools like LiteLLM the supply chain attacks can have more impact at runtime, where the compiled binary helps.
Are these kinds of libraries a temporary phenomenon? It strikes me as weird that providers haven't settled on a single API by now. Of course they aren't interested in making it easier for customers to switch away from them, but if a proprietary API was a critical part of your business plan, you probably weren't going to make it anyway.(I'm asking only about the compatibility layer; the other tracking features would be useful even if there were only one cloud LLM API.)
Does this have a unified API? In playing around with some of these, including unified libraries to work with various providers, I've found you are, at some point, still forced to do provider-specific works for things such as setting temperatures, setting reasoning effort, setting tool choice modes, etc.What I'd like is for a proxy or library to provide a truly unified API where it will really let me integrate once and then never have to bother with provider quirks myself.Also, are you also planning on doing an open-source rug pull like so many projects out there, including litellm?
Expectable, given that LiteLLM seems to be implemented in Python.However kudos for the project, we need more alternatives in compiled languages.
Curious how the semantic caching layer works.. are you embedding requests on the gateway side and doing a vector similarity lookup before proxying? And if so, how do you handle cache invalidation when the underlying model changes or gets updated?
Engagement Signals
Cross-Market Term Frequency
Quantifies the cross-market adoption of foundational terms like Go and LiteLLM by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.