LiteLLM is an AI gateway that routes model, MCP, and agent traffic through one API. Willow is an AI control plane that governs how people and agents use AI across the org, with identity, approval, discovery, and audit, gateway or not.
LiteLLM is an AI gateway. It puts model providers behind one OpenAI-compatible API and also routes traffic to MCP servers and agents. It gives platform teams controls for routing, budgets, permissions, rate limits, logging, and guardrails.
Willow is an AI control plane. It manages how people and agents use AI across an organization. Its controls cover identity, tool access, approval, discovery, policy, and audit across AI clients and MCP servers.
Willow combines MCP gateway capabilities with organization-wide governance for people, agents, and AI clients. LiteLLM centers on model routing, with additional controls for MCP and agent traffic.
Both products can control MCP access. Both have guardrails and enterprise identity features. The main difference is at what point those controls are applied.
This guide breaks down how the two compare, where each one goes deeper, and how to decide which you need, or whether you need both.
LiteLLM is built around traffic passing through a gateway. Its model gateway gives applications one API for many model providers. It supports load balancing, fallbacks, cost-based routing, caching, budgets, and rate limits.
LiteLLM also has tools for reducing model context. Its beta compress() API keeps selected context while replacing lower-relevance content, and LiteLLM reports a 77.7% reduction in average prompt tokens in one SWE-bench Lite benchmark. Its MCP Tool Search can also reduce how many tools are placed into model context.
Willow does not provide the same model-routing layer. It applies model policy and budgets closer to the people, agents, and AI clients generating the traffic.
Willow's guard coverage includes Claude Web, Claude Desktop & Cowork, Claude Code, Cursor, Codex, Pi, Gemini Web, and ChatGPT Web. LiteLLM reaches clients through the gateway, and can restrict MCP access to approved applications.
Willow background agents have their own identity, scoped tools, skills, triggers, and sessions. Willow can deploy them to a managed runtime or to an agent harness running in the customer's Kubernetes cluster.
LiteLLM's Agent Gateway connects to agents through A2A. Its documented providers include A2A, Vertex AI Agent Engine, LangGraph, Azure AI Foundry, Bedrock AgentCore, and Pydantic AI, and it adds gateway controls including agent and session limits and iteration budgets. Its Managed Agents Platform is in alpha.
So LiteLLM can control how requests reach models, MCP servers, and agents. Willow can also provision and run agents while applying identity and policy to the people and agents using those systems.
LiteLLM has detailed access controls for MCP. Its MCP permission model can apply permissions through several identity levels, and permissions are checked when a client asks for the tool list and again when it calls a tool. LiteLLM can restrict individual tools and the parameters those tools are allowed to receive.
It also has an explicit deny primitive. A key configured with no-mcp-servers resolves to zero MCP servers even if its team or access groups would otherwise grant access.
Willow's MCP controls use a different structure. Resources are distributed through groups, and group membership is cumulative, so human users and machine users can receive access through those groups. Willow then applies controls further down the path: individual tools can be enabled or disabled, background agents receive scoped capabilities, and conditions can decide whether a particular invocation is allowed based on its arguments or data fetched from the target system.
For teams choosing an advanced MCP gateway, Willow combines access management with controls over individual tool calls: which tools an identity can use, whether an invocation meets policy, and whether execution requires human approval. LiteLLM also offers granular MCP permissions, including parameter restrictions and explicit deny, alongside its model-routing capabilities.
LiteLLM also supports service-account keys for production projects that should not belong to a specific user. Willow handles non-human access differently: a machine user can have a named human owner, group-based tool access, and rotatable credentials, and a background agent has its own identity, credentials, and scoped capabilities.
The difference is therefore not whether LiteLLM supports non-human access. It does. Willow adds named ownership and treats the machine user or agent as an identity that can be governed separately.
Human accounts can also be tied to an identity provider through SCIM. When a user is removed or deprovisioned in the IdP, Willow deactivates the account.
Willow's beta user risk score runs from 0 to 100 and can use identity-risk data from CrowdStrike Falcon Identity Protection.
Willow separates runtime and build-time guards. Runtime guards inspect live usage, and build-time guards can block non-compliant content before it reaches a user's AI client. Willow supports Built-in, Regex, JSONata, LLM, and Function checks.
LiteLLM also supports guardrails and tool inspection. Its open-source guardrail framework supports custom guardrails and Presidio for PII masking, and several additional guardrail integrations are part of its Enterprise product. A guardrail registered by a team stays in a pending state until an admin approves or rejects it in the LiteLLM UI.
Willow can apply rules to individual tool calls. A condition can inspect the call arguments, query another API, and use the response when deciding whether to block the call. Its Claude Code guard hook can mask PII and secrets in tool output before the model reads it.
LiteLLM can inspect tool results too. Its post_call guardrail runs after an LLM call on input and output, and tool-call results are included in that path by default.
It also supports downstream credential handling. Its on-behalf-of authentication uses RFC 8693 token exchange so the gateway can pass a scoped token to an MCP server instead of forwarding the original bearer token.
Willow's rate limit is per user and can be narrowed to selected groups and MCP servers.
LiteLLM also limits per user, but that is one of several scopes. It can apply limits to keys, teams, models, agents, sessions, and MCP servers, so per-MCP-server limits are one scope among several rather than its only rate-limit model.
The products overlap around inspection and policy. Willow extends those controls into AI clients and user identity. LiteLLM applies them from the gateway.
Willow can require approval before a tool call runs.
A tool can be set to Require approval, which asks the user to approve the call before execution. Willow also includes human approval in Radar, where one KPI tracks destructive actions that run without human approval.
Approval and conditions are separate features.
Willow's conditions currently enforce Block. For a call that needs human review, Willow's documentation says to use Require approval on the tool instead.
Claude Guard can also pause outgoing HTTP requests and gate them behind human approval when Claude is used in Chrome.
This is useful when an agent is allowed to use a tool but some calls still need a person to approve them.
Willow applies policy inside several AI clients rather than relying only on traffic sent through a central gateway.
Its surfaces include Claude Web, Claude Desktop & Cowork, Claude Code, Cursor, Codex, Pi, Gemini Web, and ChatGPT Web.
Guard hooks are available for Claude Code, Cursor, and Codex.
These hooks can inspect user prompts and file-tool calls before the agent acts. On Claude Code, tool output can also be masked for PII and secrets before the model reads it.
This matters when employees already use several AI clients and the organization wants policy to follow that usage.
Willow can define, provision, and run background agents rather than only putting a gateway in front of agents that already exist.
A background agent has its own identity and permission boundary. Willow manages its tools, skills, triggers, sessions, and deployment to a runtime.
Teams can also run those agents in their own Kubernetes cluster. The agent definition remains in Willow while the harness creates the corresponding workloads in the customer's environment.
This is separate from using a machine user for a CI job, script, or backend that the customer already operates.
Willow can find AI tools that are already present on employee devices.
AI Discovery covers MCP servers, skills, and other AI tooling on developer machines. Its documentation includes SKILL.md, Cursor rules, CLAUDE.md, and AGENTS.md.
The scan agent can be deployed through GPO, Intune, IRU, Jamf, JumpCloud, and Mosyle.
Willow also has a browser extension for managed Chrome and Edge. It can monitor OAuth flows and web AI-agent access.
This gives security teams a way to find AI tools that were adopted outside a central platform.
Radar scores AI security posture across five categories:
Identity & Access, Shadow AI, Data Exposure, Agents & MCP, and Monitoring & Incidents.
Individual KPIs can be mapped to security and compliance frameworks. Audit-log retention targets 180 days.
Log export itself is not unique to Willow. LiteLLM also supports broad logging and observability integrations.
Willow's log integrations include Google SecOps and Panther alongside systems such as CrowdStrike, Grafana Loki, S3, Splunk, and webhooks.
Radar is the larger difference here. It gives security teams a separate view of posture and control coverage rather than only exporting the underlying activity.
Willow supports SaaS, Hybrid, and On-Prem deployments.
In the Hybrid model, the runtime runs inside the customer's infrastructure while Willow manages the control plane.
For On-Prem, the entire Willow platform can run inside the customer's Kubernetes cluster. The deployment can operate without calling back to Willow SaaS and is suitable for environments with strict egress controls, including air-gapped networks.
Willow can also produce an egress allowlist for hybrid and on-prem deployments. This gives network teams the upstream hosts that need to be permitted, while host discovery records the DNS lookups individual sandboxes actually make.
For teams using the hosted product, Willow also offers European hosting.
This means deployment location is not a basic difference between Willow and LiteLLM. Both can run inside the customer's infrastructure. The difference is what each product is being deployed to control.
Model routing is a core part of LiteLLM.
It provides one OpenAI-compatible API across many providers and supports load balancing, fallbacks, lowest-cost routing, semantic caching, and response caching.
LiteLLM can also route to internal and self-hosted models, including models served through systems such as vLLM, Ollama, TGI, and Triton.
For a platform team building a common model layer for many applications, this is work Willow is not designed to replace.
Both Willow and LiteLLM can run inside customer infrastructure. LiteLLM's deployment options are focused on operating the AI gateway itself.
Its deployment documentation covers Helm and Terraform modules for AWS and GCP, as well as componentized deployments.
LiteLLM also supports multi-region deployment. A single enterprise licence can cover multiple regions when those regions share one database; deployments with separate databases require separate licences.
Air-gapped deployment is available on Enterprise Standard and SCALE.
LiteLLM also publishes a version-support policy. From 29 June 2026, it supports the four most recent stable minor lines.
That gives teams a defined support window when planning gateway upgrades.
LiteLLM rewrote its gateway hot path in Rust and publishes benchmark results.
Its own pages publish three different overhead figures: about 0.05 ms in its Rust migration post, about 0.7 ms at p99 in a later benchmark, and 0.66 ms at p99 on its current AI Gateway page.
These come from different tests, so they should not be treated as one benchmark result. The useful point is that LiteLLM publishes the measurements and the test details rather than only making a general performance claim.
LiteLLM also publishes production incident reports. One Pfizer report describes a bug involving an ssl:false presence check that reduced gateway throughput by about 48 percent.
That report gives teams information about the gateway under production load as well as in benchmarks.
Willow pricing lists three levels: $0 per month, $15 per seat, and a contact-sales tier.
LiteLLM's open-source gateway has no licence fee. Its Enterprise pricing is based on gateway scale rather than a single published monthly price.
There is also a distinction inside the repository licence. LiteLLM describes the open-source gateway as MIT-licensed, while the repository uses MIT outside the enterprise/ directory and a separate BerriAI licence inside enterprise/.
Some features require an Enterprise licence. Audit logs, SCIM, organization and team roles, and secret-manager integrations are listed as Enterprise features. SSO is free for up to five users and requires an enterprise licence above that. Air-gapped deployment also requires Enterprise Standard or SCALE.
Running LiteLLM also has infrastructure requirements.
Its production setup uses Postgres, and Redis becomes part of the architecture as the deployment grows. LiteLLM recommends shared Redis when running more than one proxy instance so state such as rate-limit counters can be shared. At higher request volumes, Redis is also used to buffer spend writes before they reach the database.
The version policy adds another operating requirement. LiteLLM supports the four most recent stable minor lines, so teams need to keep the gateway within that support window.
Willow's infrastructure requirements depend on the deployment model. SaaS, Hybrid, and On-Prem place different parts of the runtime and platform in Willow's or the customer's environment.
The published prices therefore do not describe the full cost of every deployment. Infrastructure and operational ownership depend on which deployment model a team chooses.
Yes, but running both should solve two separate problems.
LiteLLM can handle model routing, budgets, caching, MCP permissions, and other controls in the request path.
Willow can handle identity, human approval, AI discovery, controls inside AI clients, background agents, and security posture.
There is overlap between those layers. Both can apply policy to MCP activity, for example. Using both means deciding which system owns each rule instead of configuring the same control twice. It also means operating two policy and logging layers when one may already cover the problem.
If all AI traffic already goes through LiteLLM and a team does not need endpoint discovery, client-level controls, managed background agents, or human approval, adding another control plane may not add much.
The reverse is also true. A team that needs identity and client governance but does not need a shared model-routing gateway may not need LiteLLM.
Both make sense when the two problems are separate: LiteLLM manages the request path, while Willow manages the identities and clients generating those requests.
There is no verified product integration assumed here. The fit is architectural.
Choose Willow when your priority is advanced MCP governance, including identity-based access, tool-call conditions, and human approval. Choose LiteLLM when your primary requirement is a shared model-routing layer with load balancing, fallbacks, and caching.
No. Willow is an AI control plane.
It governs how people and agents use AI across an organization, including identity, tool access, approval, policy, discovery, and audit.
An AI gateway such as LiteLLM focuses on traffic moving between applications and models, tools, or agents.
Willow is a fit when the problem is broader than routing AI requests.
That includes controlling which people and agents can use particular tools, requiring approval for sensitive actions, applying policy inside AI clients, discovering AI tooling already present on employee devices, and giving security teams a view of AI security posture.
Yes.
Willow can apply controls across supported AI clients including Claude, Cursor, Codex, ChatGPT, Gemini, and Pi.
Its AI Discovery features can also find MCP servers, skills, and AI configuration present on developer machines, rather than requiring every tool to first be registered behind a central gateway.
Willow gives machine users and background agents their own governed identities.
Machine users can have named human owners, scoped access, and rotatable credentials. Background agents can have their own tools, skills, triggers, sessions, and permission boundaries, and Willow can provision and run those agents.
Yes.
Individual tools can be configured to require approval before execution. Willow can also gate certain outgoing requests in supported clients such as Claude.
This lets teams allow an agent to use a tool without automatically allowing every action that tool can perform.
Yes.
Willow supports SaaS, Hybrid, and On-Prem deployment.
Hybrid keeps the runtime inside the customer's infrastructure while Willow manages the control plane. On-Prem runs the full Willow platform inside the customer's Kubernetes environment and can operate without a call-home requirement, including in air-gapped networks.
Yes.
A team can use LiteLLM for model routing and gateway controls while using Willow for identity, approval, discovery, client-side policy, and broader AI governance.
There is some overlap around MCP controls, so teams using both should decide which system owns each overlapping policy.
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