In brief: LiteLLM is a capable, widely adopted open-source AI gateway with over 58,000 GitHub stars. The reason people look for alternatives is usually not a missing feature: it is that self-hosting means you deploy, patch, scale, monitor and own the uptime of the gateway yourself. A managed gateway takes that responsibility back — at the cost of requests passing through a third party.
What LiteLLM does well
It is worth being clear about this, because the tradeoff only makes sense if the other side is genuinely strong.
| Strength | Why it matters |
|---|---|
| One interface for many providers | You call many LLM APIs through a single OpenAI-style surface. |
| Open source and inspectable | The code is visible and modifiable. |
| Self-hosted data path | Requests do not pass through a third-party gateway. |
| Large community | Over 58,000 GitHub stars means an active ecosystem and abundant examples. |
The self-hosted tradeoff
Running a gateway yourself is not just a deployment step. It is an ongoing responsibility.
| You gain | You take on |
|---|---|
| Full control of the data path | Deployment, upgrades and upstream change tracking |
| Fully custom routing and key policy | Capacity planning and availability |
| No third-party dependency | Key and credential management for upstream providers |
| Auditable source | Logging, usage accounting and retry logic |
What changes with a managed gateway
A hosted gateway moves those responsibilities to the service provider. You hold one API key instead of many, and you get unified usage and billing views.
The tradeoff is explicit: requests pass through FlowBarAI. If your compliance requirements forbid that, self-hosting is the correct choice — and it is worth saying so plainly.
How to decide
| Your situation | Leans toward |
|---|---|
| You have a platform or SRE team, and need to keep the data path in-house | Self-hosted |
| Small team that wants to ship and not operate a gateway | Managed |
| Usage fluctuates and you would rather not pre-pay for peaks | Managed, pay-as-you-go |
| You need deeply custom routing logic | Self-hosted (you can fork) |
Migrating to an OpenAI-compatible endpoint
FlowBarAI exposes an OpenAI-compatible endpoint, so migration is usually three steps: create an account and API key, point your client base URL at FlowBarAI, and keep the rest of your request format unchanged.
One caveat: model identifiers differ between gateways. Check the model catalog when you switch rather than assuming a one-to-one mapping.
Frequently asked questions
Is LiteLLM free?
LiteLLM is an open-source project with over 58,000 GitHub stars. Its repository reports a non-standard license identifier, so check the repository for current terms, including any commercial or enterprise arrangements.
Why do teams look for LiteLLM alternatives?
Usually not features. It is the operational responsibility of hosting, upgrading and scaling the proxy yourself, or a team preference for a managed service with a unified billing view.
Can I keep using the OpenAI SDK after switching?
Yes. FlowBarAI exposes an OpenAI-compatible endpoint, so you keep your SDK and request format and change only the base URL and API key.
Does traffic go through a third party with a managed gateway?
Yes — that is the core tradeoff. If your compliance requirements forbid it, self-hosting is the right choice.
How is FlowBarAI priced?
Pay-as-you-go. Current rates and top-up tiers are published on the pricing page, and every paid request is quoted from the live catalog before submission.
Try the managed path
An OpenAI-compatible endpoint, pay-as-you-go billing, and no gateway for you to operate.