ProxyLLM vs LiteLLM: Hosted Flat-Rate vs Self-Hosted Router

LiteLLM and ProxyLLM sit at different layers, so the honest comparison starts there. LiteLLM is software: a free, open-source proxy you host that routes one OpenAI-compatible API across 100+ providers with virtual keys and budgets. ProxyLLM is a service: we host a lane that changes what your OpenAI traffic costs, by running Codex against your own flat ChatGPT subscription. A router organizes your spend; it cannot change the price of a token. We change the price of the OpenAI share of it, and the two compose cleanly.

What LiteLLM does well

LiteLLM is the standard answer for multi-provider plumbing, and it earned that. One config file maps model names to providers; the proxy speaks OpenAI’s API shape to your apps regardless of what sits behind it; virtual keys carry per-team budgets and rate limits; routing rules handle fallbacks and load balancing. It is free to run, audited by thousands of deployments, and as private as your own infrastructure, because it is your own infrastructure.

The costs are the self-hosting costs. You operate the gateway, secure the key store, monitor the process, and apply the updates, and at scale the proxy becomes one more production service with your name on the pager. How that burden compares across the DIY landscape is mapped in self-hosting a Codex proxy vs ProxyLLM, and LiteLLM against the big hosted marketplace in LiteLLM vs OpenRouter.

What we do instead

We do not try to out-route LiteLLM. Codex Hosted does one structural thing: it runs OpenAI’s official, unmodified Codex CLI on managed servers, signed into your own ChatGPT account through OpenAI’s device-code flow, and exposes it as an OpenAI-compatible endpoint. OpenAI-bound work bills to the flat subscription instead of the meter. As planning estimates, Plus absorbs roughly $700 of API-equivalent work a month, Pro 5x roughly $3,500, Pro 20x roughly $14,000; estimates, never guarantees. The fee is $129 a month, no inference markup, with your own keys (OpenAI, OpenRouter) passing through at provider rates and per-request logs naming the lane that served each call.

Side by side

Axis	LiteLLM	ProxyLLM
What it is	OSS proxy/router you host	Hosted service
Price	Free core, your infra and hours	$129/mo flat; $0 Starter tier (BYO keys, logs)
Provider surface	100+ providers, one API	OpenAI flat lane; your own keys as passthrough
Changes per-token price?	No; budgets and routing around the meter	Yes, for OpenAI traffic: plan windows replace the meter
Keys and budgets	Virtual keys, self-operated	Scoped sub-keys with budgets, hosted
Streaming	Streams whatever the provider streams	Key lanes stream; the Codex lane returns complete responses
Ops	Yours	Ours

The rows that decide it: if you need many providers behind one self-operated gateway, LiteLLM. If your problem is the size of the OpenAI bill itself, a router will not fix that and we will.

Composing them: LiteLLM upstream of the flat lane

Because our endpoint is OpenAI-compatible, it is just another route in a LiteLLM config. Keep LiteLLM as your front door and point the OpenAI bulk route at the flat lane:

model_list:
  - model_name: gpt-5-flat
    litellm_params:
      model: openai/gpt-5
      api_base: https://api.proxyllm.ai/v1
      api_key: os.environ/PROXYLLM_API_KEY

  - model_name: gpt-5-direct
    litellm_params:
      model: openai/gpt-5
      api_key: os.environ/OPENAI_API_KEY

router_settings:
  fallbacks:
    - gpt-5-flat: ["gpt-5-direct"]

Apps call gpt-5-flat; bulk work bills to the subscription; if the flat route ever errors, LiteLLM retries direct. Two notes on that config. The flat lane already carries its own internal fallback (a second connected account, then your API key), so the LiteLLM-level fallback is a belt on top of suspenders. And the Codex lane returns complete responses, so route token-streaming surfaces to gpt-5-direct or a key lane instead.

Which to choose

Choose LiteLLM when the problem is organizational: many providers, many teams, budgets and routing logic you want under your own control, with engineers who are happy operating it. Choose ProxyLLM when the problem is economic: an OpenAI-shaped bill above roughly $150 a month that scales with every call. Choose both when both are true, which describes most platform teams we talk to; the broader field of options is ranked honestly in ProxyLLM alternatives.

If the economic problem is yours, the calculator prices your current OpenAI spend against the flat lane in about thirty seconds, and the LiteLLM config above is all the integration there is.