A Sovereign LiteLLM Alternative
LiteLLM is a good proxy in front of your keys. The Scrydon AI Gateway is the system that holds them: every call runs as a person, and the same policy and audit chain cover the tools your agents call and the network they reach.
LiteLLM answers the question most teams ask first: one OpenAI-compatible address for a hundred providers, virtual keys, budgets and a spend dashboard, self-hosted. The Scrydon AI Gateway answers the question that arrives a quarter later: who exactly made this call, what else did their agent do, and can we prove it to an auditor. It stands alone and can be deployed on its own.
Read this if you're a platform team that has run a proxy for a while and is now being asked for attribution, audit and a security boundary rather than a dashboard.
Deploy the AI Gateway Starts at €500 / month on a yearly commitment, deployed on its own in a day.
The Scrydon AI Gateway is a sovereign alternative to LiteLLM: a single governed endpoint that speaks the standard model APIs — Anthropic Messages, OpenAI Chat Completions, OpenAI Responses and Gemini — so existing tools reach any model unchanged, but where every call runs under the caller's own federated identity rather than a virtual key, against a clearance-gated model allowlist, with data loss prevention, a spend cap and an immutable audit trail. It shares one policy and one audit chain with governed tool calls and sandbox egress, and runs air-gapped, on-premises or on a European sovereign cloud.
LiteLLM is the most widely used open-source LLM proxy, and deservedly so: a Python proxy and SDK that puts a hundred providers behind one OpenAI-compatible API, with virtual keys, team budgets, spend tracking and routing, self-hostable in an afternoon. Its limits are the limits of the category. A virtual key stands for a team at best, so attribution stops at the key; governance ends where the model call ends, while an agent's afternoon is mostly tool calls against systems that hold your data; and the proxy sits in front of your credentials rather than being the system that holds them. The Scrydon AI Gateway keeps what LiteLLM gets right — the standard APIs, any model, developers who change nothing — and moves the control point: a gateway key is minted against a named person in your own identity provider, the model allowlist follows that person's clearance, data loss prevention screens what leaves, and the same identity governs the tools the agent calls next and the network its sandbox may reach.
Runs as a Person, Not a Key
Every call resolves through your identity provider to the human who made it, with their delegated grants — which is what makes per-developer cost, clearance-gated models and instant revocation possible.
Governance Past the Model Call
One policy and one audit chain across the model call, the tools the agent calls over MCP, and the egress of the sandbox those tools run in.
Sovereign by Deployment
Air-gapped, on-premises or a European sovereign cloud, with open-weight models on your own hardware behind the same endpoint.
LiteLLM Alternative in the Scrydon platform
One integrated, sovereign architecture. Here is where LiteLLM Alternative sits — highlighted against the full stack it works with.
The AI OS for Humans & AI Agents
Ontology & Semantic Layer, one connected model for your data, knowledge & processes
Combining the best of data lakes, data warehouses and search
Governed access to every model, and the agents & workflows that execute across your systems
Integrate across A2A, MCP, legacy systems and data sources
Secure domain federation, trusted data sharing, and cross-boundary intelligence
Sovereign Foundations
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What LiteLLM does, and what the AI Gateway does with the same request
LiteLLM's strength is breadth and speed of adoption: a proxy you can run in an afternoon, a hundred providers behind one OpenAI-compatible API, virtual keys with budgets, and a dashboard that turns an undifferentiated invoice into something a team can read. The Scrydon AI Gateway takes the same request and changes what happens behind the address: the call runs as a named person from your identity provider, the model allowlist follows that person's clearance, data loss prevention screens what leaves, and the audit record settles before the stream finishes. The vendor key is never issued to anyone.
The standard APIs, unchanged tools — Both speak the APIs your tools already speak, so Claude Code, Codex, Cursor and any application on a standard completions API run through either without modification.
Identity from your provider, not a virtual key — LiteLLM attributes a call to the virtual key that made it. The AI Gateway mints keys against a person in your identity provider and refuses a request that tries to name a tenant.
Policy at dispatch, on every call — A clearance-gated allowlist decides which models this person may reach; data loss prevention and moderation run inline on streaming and non-streaming calls; the turn is metered against the organisation's cap before the answer lands.
The same chain for tools and egress — When the agent goes on to call a system over MCP or run code in a sandbox, the same credential model, the same policy snapshot and the same audit trail apply — a proxy is not in that path.
When the question changes from what did we spend to who did what
LiteLLM is a fair, capable choice for the first question — one address, any model, a dashboard — and many teams should start there. The gap opens when the second question arrives, usually from a CISO or an auditor: not what did we spend but who did what, on which system, with whose permission. A virtual key cannot answer that; it stands for a team at best. And a proxy cannot see the larger part of an agent's day, which is spent calling your mail, your tickets and your CRM rather than a model. The Scrydon AI Gateway is built for the second question: every call attributable to a person, one policy and one audit chain across the model call, the tools the agent calls and the sandbox it runs in, deployed where your production runs — and deployable on its own before anything else is.
Scrydon AI Gateway vs LiteLLM
Both put every model behind one address and both meter spend. The difference is who the call runs as, and how far the governance reaches once the model has answered.
| Capability | Scrydon | LiteLLM |
|---|---|---|
| Who the call runs as | The person, resolved through your identity provider, with their own delegated grants | A virtual key, standing for a team or a user the administrator set up |
| Model calls governed | Clearance-gated allowlist, DLP, moderation, cap and immutable audit on every call | Model access per key, budgets and rate limits, guardrail integrations, logging |
| Tool calls governed | Same identity, policy and audit chain as the model call | Partly, where it also fronts a tool protocol; not the caller's delegated grants |
| Network egress from the sandbox | Enforced outside the workload, which cannot reconfigure it | Out of scope |
| Where the vendor key lives | On the platform; never issued to a developer | In the proxy's configuration or database |
| Cost attribution | Per person, per team or unit, and per turn, by model, capability and workflow, with an on-pace projection and a cap | Per key, team and model, with budgets |
| Deployment | Sovereign — air-gapped, on-premises or European cloud | Self-hosted container, or the vendor's hosted service |
| Pricing model | Fixed monthly fee for the cluster that carries your people, excluding hosting | Open source, with a paid enterprise tier for some features |
A category comparison, written to orient: LiteLLM is a capable, widely used proxy and we would not pretend otherwise. LiteLLM is a trademark of its owners; capabilities evolve — verify current details with the vendor.
Frequently asked questions
What is the best alternative to LiteLLM for a regulated organisation?+
How is the Scrydon AI Gateway different from LiteLLM?+
Does the AI Gateway support the same APIs as LiteLLM?+
Is LiteLLM not already self-hostable and open source?+
Can we move from LiteLLM to the AI Gateway without disrupting developers?+
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