Inferbase vs LiteLLM
LiteLLM balances deployments of the model your config names. Inferbase routes each request to the best model, with an auditable decision.
Where the routing lives
Both put many models behind one OpenAI-compatible API. The difference is where the model decision is made.
Routing you configure
The gateway balances deployments and walks your fallback lists. Which model handles a request is decided in this file, before any request arrives.
Routing that decides
No model list to maintain. The decision is made per request, and recorded.
Gateway, or routing-first
Unified access you operate yourself, versus model selection as the product.
LiteLLM’s value is unification: one self-hosted, OpenAI-compatible gateway to 100+ providers, with keys, budgets, guardrails, and cost tracking in one place. Its core router works at the deployment level, balancing providers for the model you named and failing over down lists you define; a beta AutoRouter adds complexity-tier classification on top, with the tier-to-model mappings still living in your config.
- The model decision is still yours to encode. Even with the beta AutoRouter, which classifies prompts into complexity tiers, the tier-to-model mappings are config you write and maintain; nothing scores the candidates on per-task quality evidence.
- Config is a liability that grows. Every new model, price change, or deprecation is an edit to model_list and fallbacks that someone owns; a routing platform absorbs that churn for you.
- You run the gateway. The proxy, its upgrades, and Redis for some routing strategies are your infrastructure; the open-source license is free, the operating time is not.
If what you need is a self-hosted control plane over providers you already run, LiteLLM is the stronger fit. If what you need is the right model per request, chosen and served for you with a decision on the record, that is where Inferbase is built to win. Still weighing the categories themselves? Start with our breakdown of gateways versus routers.
Side by side
Where the two line up and where they diverge, without hiding what LiteLLM does well.
| Inferbase | LiteLLM | |
|---|---|---|
| Routing decision | The best model for each request | The best deployment of the model you named |
| Per-prompt model selection | First-party, benchmark-grounded, the core of the product | Beta auto-router maps prompts to complexity tiers; the tier-to-model mapping is yours to write |
| Routing strategies | Quality, cost, or latency as an objective | Shuffle, latency, cost, least-busy, usage, or custom, across a model group |
| Fallback on failure | Automatic, ranked within the routed pool | Yes, ordered lists you define, plus context-window and content-policy fallbacks |
| Per-request transparency | Audit record: task, candidates, scores, why the winner won | Extensive logs, cost tracking, and budgets, no decision rationale |
| OpenAI-compatible API | Yes | Yes |
| Provider breadth | Curated catalog: open models served across multiple providers, closed models (OpenAI, Anthropic, Google and others) on your own keys or cloud account | 100+ providers, including your own vLLM or Ollama |
| Provider accounts | None needed, one bill | Yours, bring your own keys per provider |
| Who operates it | Managed platform | You: self-hosted proxy, config, and Redis for some strategies |
| Pricing | Free to start | Open source is free to self-host; enterprise features are paid |
Reflects publicly documented behavior as of July 2026. LiteLLM ships fast, check their docs for the latest.
Where each one fits
They solve different problems, so pick by what you are optimizing for: a gateway you control end to end, or routing you never have to configure.
LiteLLM is the better fit when
- You need a gateway inside your own network or compliance boundary
- You already hold provider accounts and want one interface with keys, budgets, and guardrails across teams
- You need the widest provider reach, including local backends like vLLM and Ollama
- You want open source you can read, extend, and run at no license cost
Inferbase is the better fit when
- You want the platform to pick the best model per request, not a config file you maintain
- You do not want to operate a proxy, its config, or provider accounts
- You need a routing decision you can audit, per request
- You want routing and serving as one managed system with one bill
Frequently asked questions
Straight answers on how Inferbase and LiteLLM differ, and when each one is the better choice.
An LLM gateway is one API in front of many providers: every service calls the gateway with one set of credentials, and the gateway holds the provider keys, enforces limits and budgets, logs the traffic and falls back when a provider fails. Most gateways send each request to the model you named, or to whichever host is up for that model; the model decision stays in your config or in rules you write. Inferbase is a gateway that also makes that decision, on the prompt and on evidence.
A router decides which model should answer; a gateway executes the request and governs it with keys, limits, budgets, logs and failover. Some routers only recommend, leaving you to call the provider; some gateways only proxy to the model you named. Inferbase runs the router inside the gateway, so the decision is served rather than recommended and its record sits beside the usage receipt.
That the same call which chooses the model also runs it. You send model="auto" to one OpenAI-compatible endpoint; the router classifies the request, picks the model that clears the bar for the task on your objective, the gateway serves it with two fallbacks ready, and the response comes back with its decision on the record. There is no second system to operate for the inference and no second bill for it.
When a workload mixes tasks and difficulties, which most production traffic does. We publish no savings percentage: one was withdrawn in September 2026 because the baseline price it was measured against is no longer one we can substantiate, and a ratio is only as good as its denominator. What routing does is send each request to the cheapest model that clears the quality bar for it, so the gain is the gap between what you pay now and what the right-sized model costs. A workload that is all one hard task gains less; the routing page shows the run and its caveats, and Routing Preview measures it on your own traffic.
It is, at two layers. Its core router balances deployments of the model you named, picking a deployment by strategies like latency, cost, or least-busy, and fails over down fallback lists you write. A beta AutoRouter added in July 2026 also classifies prompts into complexity tiers, using heuristics, keyword rules, or an optional small-model call. What stays with you is the mapping: which model serves each tier is config you write and maintain, and nothing scores the candidates on per-task quality evidence. Inferbase ranks the eligible models per request from benchmark-grounded, task-conditioned evidence, with a decision record you can audit.
The open-source gateway is free to self-host, and that is a genuine strength. The full cost is the infrastructure it runs on and the operating work: deploying and upgrading the proxy, maintaining the model list and fallback config as models change, and running Redis for some routing strategies. Enterprise features such as SSO and audit logs are a paid tier. Inferbase is a managed platform with no gateway to operate, free to start.
Yes. LiteLLM supports ordered cross-model fallback lists, plus specialized fallbacks for context-window overflows and content-policy rejections. The difference is how the list is made: in LiteLLM you write and maintain it by hand; in Inferbase the candidate pool is ranked from benchmark evidence per request, so the fallback order tracks the task rather than a static config.
Usually not, but they can coexist. If your team runs LiteLLM as an internal gateway for key management and budgets, Inferbase can sit behind it as one more OpenAI-compatible endpoint, with model="auto" turning on routing for the traffic you send it. If what you want from the gateway is model selection, failover, and one API, Inferbase covers that without the self-hosted layer.
Both are OpenAI-compatible, so it is a base URL and key change with the SDK you already use. What you leave behind is the config file: no model_list to maintain, no fallback lists to hand-order, and no provider keys to distribute, since Inferbase serves the models it routes to. Set model="auto" to route, or name a model to pin it.
On evidence, then on your objective. Each request is classified for its task family and complexity; the catalog is narrowed to the models that clear a quality bar for that task on benchmark evidence and that fit the request; the survivors are ranked on the objective you set (cost, balanced, quality or latency). A model with no measurement for the task is treated as unknown and does not qualify on guesswork.
Yes. Every routed request writes a decision record: the task the classifier saw, its complexity, the objective, how many models qualified, which won and how clearly, and each fallback attempt if any. When streaming it is the first event on the stream; afterwards it is retrievable by request id from the API and the dashboard.
One line. Point an existing OpenAI SDK at the Inferbase base URL and send model="auto"; chat completions, streaming, tool calls and structured output pass through unchanged, and Anthropic-shaped clients are served on the Messages endpoint. Name a model instead of auto and it is served as named, so an application can pin where it must and route where it can.
A free plan with no time limit and no card: 5,000 requests a month on any model and any key, with Inferbase's own models free for chat, embeddings and rerank within daily limits. Pro is $19 a month with 50,000 requests and no daily limits. Managed inference on Pro is billed at the provider's token price plus a flat 6%; on your own provider keys or cloud account the tokens are billed by your provider, with nothing added. Routed or pinned, a request counts the same.
Put intelligence in the middle.
One gateway in front of every model, with your policies applied and every decision on record. Start on the free plan with 5,000 requests a month, no card required.