LiteLLM Alternative: Hosted, Zero-Markup LLM Gateways Compared

If you have outgrown wiring each provider's SDK by hand, you have probably landed on LiteLLM — and now you are weighing a LiteLLM alternative because running the proxy yourself is starting to feel like a project of its own. That instinct is reasonable. LiteLLM is a popular, well-respected open-source gateway, and for teams that want to self-host and own every layer it is an excellent choice. But not everyone wants to deploy, scale, secure, and monitor a proxy that now sits in the critical path to every model call. This guide lays out why teams go looking for an alternative to LiteLLM, what to evaluate, and how a hosted, zero-markup gateway compares — fairly, with the trade-offs in both directions.

If you want the background first, our explainer on what is LiteLLM covers the SDK and proxy in detail; this piece assumes you already know roughly what it does and are deciding what to run.

Why teams look for a LiteLLM alternative

The reasons rarely come down to the software being bad — LiteLLM is mature and capable. They come down to where the work lands. Once your LLM proxy is load-bearing, the open-source version means you are on the hook for everything around it:

• You deploy and scale it. Containers, autoscaling, capacity planning, and a high-availability setup so the proxy does not become a single point of failure in front of every model.
• You secure it. Provider key storage, network policy, auth, secret rotation, and timely upgrades are all your responsibility.
• You monitor it. Uptime, latency, and alerting have to be wired into your stack. If the proxy goes down at 2am, your LLM features go with it.
• You maintain it. Tracking releases, applying updates, and keeping pace with provider API changes is ongoing engineering time, not a one-off.
• You build the dashboard. Logs and cost data exist, but turning them into a usable view of spend per model, per app, and per user is on you.

For a team that simply wants reliable, cheap model access with smart routing and a clear bill, that is a lot of operational surface to babysit. The search for a hosted LiteLLM-style experience is really a search for the same unified interface and routing without owning the infrastructure underneath it. That is the gap a managed LLM gateway fills.

What to evaluate in an alternative to LiteLLM

Before comparing names, get clear on what you are actually optimizing for. These are the dimensions that separate a good LiteLLM replacement from a lateral move:

Hosted vs self-host

This is the first fork. Do you need the gateway running inside your own network for data-residency or control reasons, or would you rather not operate one more service at all? Self-hosting gives you maximum control and no third party in the request path. A hosted gateway gives you faster time-to-first-call and no infrastructure to keep alive. Be honest about your team's capacity — the right answer is about preference and headcount, not prestige.

Provider coverage

A gateway is only useful if it reaches the models you actually use. Check the provider list and how easy it is to add a new one. LiteLLM's breadth (100+ providers) is a genuine strength; an alternative should cover the providers that matter to you without forcing you back into per-vendor SDKs.

Routing, fallback, and racing

The whole point of a gateway is behavior you would otherwise hand-code. Look for routing by cost or latency, automatic fallback when a provider errors or rate-limits, and optional racing — firing the same prompt at several models and taking the fastest response. Our deep dive on what is an LLM gateway walks through why these matter day to day.

Caching and cost analytics

Opt-in response caching can cut both latency and spend on repeated prompts — confirm it is controllable, not forced. On the analytics side, the bar is true per-call cost accounting: real dollars per request, per model, per user, reconcilable against your provider invoices, not just aggregate token counts.

OpenAI and Anthropic compatibility

Adoption cost hinges on this. If the gateway is drop-in compatible with the OpenAI SDK — and ideally the Anthropic SDK too — switching is a base-URL and key change, not a rewrite. Both surfaces matter if some of your code is written against Claude's native API.

Pricing model: markup vs BYOK

The biggest hidden cost question. Does the alternative resell tokens with a margin, charge a subscription, or let you bring your own provider keys (BYOK) and pay providers directly at list price? LiteLLM itself adds no token markup — you pay providers directly because it is your proxy. A hosted alternative is only a true like-for-like on economics if it preserves that property instead of quietly inserting a margin.

LiteLLM vs a hosted gateway: a fair comparison

Here is the balanced version. This compares the self-hosted open-source approach against a managed gateway like flo2 — not "good vs bad," but two answers to "who runs the proxy."

Read this table as a preference map, not a scoreboard. If running a proxy is no burden for you — or you specifically want everything inside your own environment — the self-host column is genuinely the better fit, and LiteLLM is a strong, mature pick. If you would rather not operate one more service, the hosted column gets you the same unified interface and routing without the overhead.

Where flo2 fits as a hosted, zero-markup option

This is where a managed option earns its keep without giving up the economics that make self-hosting attractive. flo2 is a developer-first, hosted LLM gateway built around a specific stance: zero token markup with bring-your-own-keys. You connect your own provider accounts — OpenAI, Anthropic, Gemini, Groq, Cerebras, DeepInfra, Mistral, xAI, OpenRouter — and you pay each provider directly at their real price. flo2 never sits in the money path, so there is no per-token margin on top.

In exchange for one key that is drop-in compatible with both the OpenAI and Anthropic APIs, you get the routing layer most teams otherwise assemble and host themselves:

• Smart routing sends each request to the cheapest or fastest model that meets your bar, so trivial calls do not hit a flagship model at flagship prices.
• Fallback chains fail over across providers and models when one errors or rate-limits, instead of dropping the request.
• AI racing fires the same prompt at several models in parallel and returns the fastest response when latency matters.
• A/B testing with a judge scores "model–task fit" on your real traffic, so you promote the winner on evidence rather than vibes.
• Opt-in response caching stops you paying for the same answer twice.
• True per-call cost accounting in a managed dashboard — real dollars per request, reconcilable against your provider bills.

The honest framing, the same one that applies to LiteLLM in reverse: BYOK means you still own your provider keys, quotas, and rate limits, and you need accounts with the providers you want to route to. flo2 takes the deployment, scaling, security, and monitoring of the gateway off your plate; it does not take over your provider relationships, which is exactly the point — you keep them, along with any enterprise terms or discounts you have negotiated. flo2 is free during Beta.

The bottom line

If LiteLLM answers "how do I unify my model calls," the reason to look for an alternative is usually narrower than dissatisfaction: you want that same unified interface, routing, and direct-to-provider pricing without running the proxy yourself. Self-hosting LiteLLM is the right call when you want full control of the data path and have the ops capacity to back it. A hosted, zero-markup gateway is the right call when you would rather skip the infrastructure and get a dashboard, fallback, racing, and honest cost accounting on day one. Many teams try both before deciding.

Want to weigh the wider field? See our best LLM gateway comparison for a side-by-side across categories. And if a hosted, BYOK, zero-markup setup matches your priorities, point your existing OpenAI or Anthropic SDK at flo2 and compare the real costs against your own invoices — it is free to try during Beta.