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InferAll vs LiteLLM

The short version: LiteLLM is the open-source gateway you run yourself. InferAll is the managed alternative for teams that want the same multi-provider abstraction without operating a proxy. If you need on-prem, strict data residency, or full control of the gateway process, run LiteLLM. If you want one base URL, a free OSS inference allowance, and billing handled for you, use InferAll. These are complementary tools that often get framed as competitors.

At a glance

FeatureLiteLLMInferAll
Deployment modelSelf-hosted OSS proxy (you run it)Managed SaaS at api.inferall.ai
License / sourceMIT, with a separate enterprise/ directory under its own licenseProprietary hosted service
Free OSS inference tierNone — bring your own model hosting100k tokens/month on 186 NVIDIA-hosted OSS
Anthropic-format endpointYes — configurable on the proxyYes — /v1/messages, no proxy to run
OpenAI-format endpointYes — primary surfaceYes — /v1
Catalog size100+ LLM providers via proxy adapters255+ models across 6 providers
Failover / fallbackConfigurable in router yaml; you own the policyServer-side cross-provider retry on 429/529/5xx/timeout
Billing / spend trackingSelf-managed (DB-backed budgets in the proxy)Built-in: free tier, Pro, Team, Enterprise
On-prem / VPCYes — that is the pointNo — hosted only
Operational burdenYou run a serviceTwo environment variables
VS Code extensionNo first-party branded extensionYes — InferAll for VS Code (Cline-based, sign-in to use)

License and catalog rows are verified against LiteLLM's current LICENSE file and README; they can shift quarter to quarter. Have a correction? Email contact@kindly.fyi.

When LiteLLM is the right choice

The strongest case for LiteLLM is that you need the gateway to live where you control it. On-prem, inside a regulated VPC, in a region your hosted-SaaS vendor doesn't operate in, or behind a firewall that doesn't allow outbound connections to a third-party endpoint — these are real constraints, and they're exactly what a self-hosted OSS proxy is designed for. If a procurement reviewer says “all model traffic must transit a service we operate,” LiteLLM answers that and InferAll cannot.

The second case is control over the gateway behavior itself. LiteLLM's router config is yours: you write the fallback policy, the cost guardrails, the rate-limit policies, and the upstream model whitelist. If your routing has to encode company-specific rules (“production traffic only routes to a vetted upstream subset; experiments can use the wider pool”), that's a config file you own. InferAll has an opinion about cross-provider failover and a curated upstream set — that opinion is fine for most teams, but it's opinionated.

The third case is integration depth. LiteLLM exposes hooks for logging, custom callbacks, request transformation, and an admin UI for budgets and keys. If a platform team wants to build a gateway product on top of an OSS core, LiteLLM is the foundation that's designed for that. InferAll is the opposite shape — a finished product you consume, not a kit you extend.

And of course: if you have a strong open-source preference, if you want the code reading available, if you want to contribute back upstream — those preferences are valid and LiteLLM is on the right side of them.

When InferAll is the right choice

The clearest fit is “we want what LiteLLM does but we don't want to run it.” If your team would spend a week standing up a LiteLLM proxy and another week wiring it into the rest of your platform — billing, alerting, capacity, on-call — and the result is a routing layer that isn't differentiating for your product, InferAll is the shortcut. Set two environment variables, your SDK code keeps working, and the gateway is somebody else's operational problem.

The free open-source inference tier is the other big one, and it's not something LiteLLM can give you on its own. LiteLLM routes; it doesn't host models. To get free OSS inference behind a LiteLLM proxy you'd need to bring your own NVIDIA NIM contract, your own self-hosted vLLM cluster, or a deal with a community host. InferAll bundles 100,000 tokens per month against 186 NVIDIA-hosted models — Llama 3.1 405B, Mixtral, Nemotron, CodeLlama — into the service. For chatty coding-agent traffic that burns through tokens on cheap turns, that allowance is real money.

The third reason is Claude Code in particular. Both LiteLLM and InferAll expose Anthropic-format endpoints, but with LiteLLM you're also operating the proxy that sits between Claude Code and the upstream Anthropic API. With InferAll, the Anthropic-compatible /v1/messages endpoint is hosted and the free tier means Claude Code can run against free OSS models without setting up billing first. That's the workflow we built the gateway around.

Finally, the billing surface comes with the service. Free, Pro, Team, and Enterprise plans live at /#pricing — no separate Postgres for spend tracking, no separate auth layer, no separate admin UI to keep up with the OSS release cadence.

Migrating from LiteLLM to InferAll

Because both surfaces are OpenAI-compatible (and both expose an Anthropic-compatible route), the migration is the same two-variable swap that moves you between any two OpenAI-format providers. The wire format is unchanged; only the base URL and the key are different.

The harder part of the migration is the LiteLLM-specific configuration you've accumulated — the router yaml, the budget rules, the custom callbacks. Some of that maps onto InferAll's server-side routing and per-key controls; some of it has no equivalent and you'd need to give it up to move. Worth a config diff before you commit.

Environment swap

# Before: self-hosted LiteLLM proxy
export OPENAI_API_KEY=sk-...
export OPENAI_BASE_URL=https://litellm.your-company.internal/v1

# After: InferAll managed gateway (no proxy to run)
export OPENAI_API_KEY=ifa_...
export OPENAI_BASE_URL=https://api.inferall.ai/v1

# Or, if you want Claude Code / Cline (Anthropic-format):
export ANTHROPIC_API_KEY=ifa_...
export ANTHROPIC_BASE_URL=https://api.inferall.ai
claude

On model IDs: InferAll uses the upstream provider's native naming on its OpenAI- and Anthropic-compatible surfaces — gpt-4o, claude-sonnet-4-20250514, gemini-2.5-pro. If your LiteLLM config rewrote model names to internal aliases, you'll either keep that rewrite in your application layer or update the call sites.

Or: keep both, route by workload

The decision doesn't have to be exclusive. A common shape is to run LiteLLM inside your VPC for traffic that has residency requirements, and to use InferAll for everything else — developer-laptop coding-agent traffic, prototype workloads, anything where the free OSS allowance is the difference between “turn it on this afternoon” and “file an infra ticket.” Two base URLs, two keys, and the workload picks the gateway that fits.

Both, by workload

# It is reasonable to run LiteLLM in your VPC for sensitive workloads
# and use InferAll for everything else. Route by workload, not by gateway.

# Example: critical batch jobs go through internal LiteLLM (full control).
LITELLM_INTERNAL_URL=https://litellm.your-company.internal/v1

# Coding-agent traffic from developers' laptops goes to InferAll
# (free OSS allowance, no proxy on the developer path).
export OPENAI_BASE_URL=https://api.inferall.ai/v1

Why we're writing this

This page is on inferall.ai and it's about something adjacent to InferAll, so the bias goes one way by default. We built InferAll because we wanted a managed multi-provider gateway with a free OSS allowance and a single bill, and the existing options either required us to run our own proxy or didn't include a free inference pool. LiteLLM is a serious piece of open-source software, and for teams that should be running their own gateway it's the right call. If we wrote a comparison that pretended otherwise, you'd be right not to trust the rest of the page.

Frequently asked questions

Is InferAll a managed LiteLLM?

Approximately, yes — for the routing-and-translation slice of what LiteLLM does. Both put one OpenAI-compatible endpoint in front of many upstream providers. The differences are operational: InferAll is a hosted SaaS that you point at and bill against; LiteLLM is an open-source proxy you deploy and operate yourself. InferAll also bundles a free open-source inference allowance on NVIDIA NIM, which LiteLLM does not provide because LiteLLM is a router, not a model host.

Does LiteLLM speak the Anthropic API format like InferAll does?

Yes. LiteLLM supports an Anthropic-compatible passthrough so Claude Code and Anthropic-SDK consumers can target a LiteLLM proxy. InferAll exposes the same surface at /v1/messages. The functional difference is hosting: with InferAll you set ANTHROPIC_BASE_URL=https://api.inferall.ai and you're done; with LiteLLM you set it to your self-hosted proxy and you also own the uptime of that proxy.

What does LiteLLM cost compared to InferAll?

LiteLLM's open-source proxy is free — you pay for the infrastructure that runs it (a container, a VPC, an autoscaler, an on-call engineer) plus the upstream provider tokens it forwards. InferAll is a hosted gateway: the free tier covers 100,000 tokens per month against 186 NVIDIA-hosted OSS models with no infrastructure to operate, and premium providers bill at the upstream's published per-token price with zero markup. Whether LiteLLM-the-OSS or InferAll-the-SaaS is cheaper depends on your traffic and your infra costs.

Can I run InferAll on my own infrastructure?

No, not in the same sense that LiteLLM lets you. InferAll is a hosted gateway at api.inferall.ai. If on-prem deployment, strict data residency, or running the gateway inside your VPC is a hard requirement, LiteLLM is the right tool for that problem. We don't think we should compete with self-hosted OSS for that use case — we point people there.

Why would I use InferAll instead of running LiteLLM myself?

Three honest reasons. First, you don't want to operate another service — no proxy container, no Postgres for spend tracking, no on-call rotation for the gateway. Second, you want a free OSS inference allowance you didn't have to procure tokens for. Third, you want billing, key management, and usage tracking to come with the gateway instead of being something you assemble. If none of those apply, LiteLLM is great and the control of running your own gateway is real.

Can I use Claude Code with LiteLLM?

Yes, if you stand up a LiteLLM proxy and configure the Anthropic-compatible route, you can point Claude Code at it via ANTHROPIC_BASE_URL. The first-time setup involves running the proxy, wiring upstream credentials, and choosing where to host. With InferAll the same Claude Code integration is two environment variables against api.inferall.ai with no proxy to operate.

Does InferAll's free tier route through OSS models inside LiteLLM-style routing?

InferAll's free tier is 100,000 tokens per month against 186 open-source models hosted on NVIDIA NIM. You hit the gateway, the gateway forwards to NIM, and the tokens come off your free allowance until it resets. Claude Code, Cline, and any OpenAI-SDK or Anthropic-SDK consumer can use that allowance just by selecting one of the free models. This is something LiteLLM-as-a-pure-router cannot give you on its own — you'd need to bring your own NIM contract.

Related

InferAll home — the gateway, the free tier, the failover story.

InferAll for VS Code — Cline-based agent with the gateway pre-wired, free first run.

Pricing — free tier, Pro, Team, Enterprise.

AI inference API — endpoint surface, supported providers, code examples.

Unified AI API — one key, one bill, every provider.

Last updated: 2026-05-14.

LiteLLM facts on this page are drawn from the LiteLLM project README, documentation, and public packaging. InferAll facts are drawn from this site and the gateway running at api.inferall.ai. Specifics change. Have a correction? Email contact@kindly.fyi.