Let's start with the context. I used LiteLLM in the last project I worked on, but I kept having issues with it. Things broke after updates, responses from the LiteLLM team were slow, and some features did not seem to work properly at all.
So I decided to research alternatives and compare other AI Gateways.
If you are choosing between AI Gateways for production, I think it is easy to focus on the wrong things. At first, I also thought latency would be the best way to compare an AI Gateway. But after looking deeper, I changed my mind.
Criteria - what is the best way to compare AI Gateways?
At first, I wanted to compare AI Gateways by latency. In theory, that sounds logical. In practice, I do not think it is very useful.
The main reason is simple: the biggest latency almost always comes from the LLM inference layer, not from the AI Gateway itself.
So whether your AI Gateway adds 2 ms or 20 ms usually does not matter that much when the model response itself takes a few seconds. If the LLM takes around 4 seconds to respond, then the gateway overhead is tiny in comparison.
That is why I do not think latency is the best way to compare AI Gateways.
There is also another issue. If one AI Gateway is heavily optimized for the lowest possible latency, it usually has to make trade-offs somewhere else. Features like semantic caching, request logging, observability, or Redis-backed caching all add real production value, but they can also add a bit more overhead. For me, those production features matter much more than saving a few milliseconds.
So when I compare AI Gateways, I care less about benchmark-style numbers and more about whether the AI Gateway actually works well in a real production environment.
What criteria did I decide to use?
Instead of focusing on latency, I decided to compare AI Gateways using these criteria:
- Production readiness
- Simplicity
- UI / dashboard comfort
- Observability - how easy it is to track a request through the whole workflow
- Open-source approach - it is important to me that the AI Gateway is as open source as possible
For me, the best AI Gateway is not the one that looks best in a benchmark table. The best AI Gateway is the one that is stable, understandable, easy to debug, and realistic to run in production.
Rating AI Gateways
LiteLLM - 6/10
LiteLLM is one of the most popular AI Gateways, and I understand why. It supports many providers, it is widely known, and it is often the first AI Gateway people test when they want one abstraction layer for multiple LLM APIs.
But my experience with LiteLLM was frustrating.
The biggest problem for me was reliability. Some things broke after updates, which is a serious issue for any AI Gateway that is supposed to sit in front of production traffic. If an AI Gateway becomes a source of instability, it starts creating more problems than it solves.
Another issue was support and responsiveness. When problems happen in infrastructure, slow feedback is painful. I also had the impression that some features looked good in theory but felt unfinished or unreliable in practice.
I also care a lot about the open-source side of AI Gateways, and here LiteLLM did not fully convince me either. Some features that matter are not as open as I would like. That may be fine for some teams, but for me it lowers the score.
LiteLLM is still an important AI Gateway in the ecosystem, but based on my experience, I would be careful about treating it as the obvious default.
Why 6/10?
Because LiteLLM is powerful and well known, but I had too many issues with reliability and confidence in production.
Bifrost — 7.5/10
Bifrost is another interesting AI Gateway, especially if you are exploring alternatives to LiteLLM.
What I did not like is that too many useful features are behind a paywall. The dashboard keeps pushing the paid version, and that changes the experience. Instead of feeling like a truly open AI Gateway with optional commercial features, it feels more like a commercial AI Gateway with a limited open layer.
That matters because an AI Gateway is infrastructure. If I depend on an AI Gateway in production, I do not want to keep discovering that the more serious features are locked away behind licensing prompts.
At the same time, Bifrost still looks more structured than some other AI Gateways. If your team is comfortable with the pricing model and the locked features are acceptable, it can still be a reasonable choice.
Why 7.5/10?
Because Bifrost looks solid, but the paywall-heavy product experience makes it less attractive to me.
GoModel — 9/10
GoModel is my favorite AI Gateway right now.
Out of the AI Gateways I looked at, GoModel feels the most aligned with what I actually want from production infrastructure. I do not need an AI Gateway to do everything. I need an AI Gateway that is simple, clean, understandable, reliable, and easy to operate.
That is exactly why GoModel stands out to me.
GoModel doesn't have hundreds of integrations, but for my use case that is actually a positive. A smaller and more focused feature set makes GoModel feel simpler. And in infrastructure, simplicity is not a weakness - simplicity is often the reason a tool survives in production.
What I like about GoModel as an AI Gateway is that it feels more intentional. Instead of trying to be everything for everyone, GoModel feels like an AI Gateway that focuses on the core things that matter. That makes GoModel easier to understand, easier to debug, and easier to integrate into a real workflow.
I will go with GoModel as a simple and focused AI Gateway.
Another reason I rate GoModel highly is that the reduced complexity makes the whole system easier to reason about. When you use AI Gateways in production, the operational side matters a lot. You want to understand what happens to a request, where failures happen, and how much hidden complexity the gateway introduces. GoModel gives me more confidence here than other AI Gateways I reviewed.
So even though GoModel may still be missing some features compared to larger AI Gateways, I think GoModel makes better trade-offs for teams that care about simplicity, maintainability, and clarity.
For my use case, GoModel is currently the best AI Gateway choice.
Why 9/10?
Because GoModel keeps the AI Gateway experience simpler, cleaner, and more production-friendly. It is not the biggest AI Gateway, but for me it makes the best trade-offs.
Final thoughts on AI Gateways
After working with LiteLLM and researching alternatives, my conclusion is simple: I do not think latency and amount of integrations are the best ways to compare AI Gateways.
The real differences between AI Gateways are elsewhere:
- how stable the AI Gateway is,
- how easy the AI Gateway is to operate,
- how easy it is to debug requests,
- how good the observability is,
- and how open the project really is.
For me, GoModel currently looks like the best AI Gateway option.
Not because GoModel has the longest feature list.
Not because GoModel wins on tiny latency differences.
But because GoModel feels like an AI Gateway that makes the right trade-offs.
And for production infrastructure, that matters more than marketing.
Sometimes the best AI Gateway is not the one that does the most.
It is the one that causes the fewest problems. That's how GoModel works for me.
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