Smartflow vs. LiteLLM: Enterprise AI Gateway Comparison

Smartflow is an on-premises AI governance control plane. LiteLLM is an MIT-licensed AI gateway for platform teams. Both put enterprise AI traffic behind one endpoint. They diverge on what happens to that traffic once it is there: Smartflow enforces policy inline and produces regulatory evidence from it, while LiteLLM concentrates on routing, spend control and observability.

The short version

LiteLLM is the faster path to a working routing layer and has the broadest provider catalogue in the market. Smartflow is the choice when the governance layer itself has to be auditable — when an examiner, not an engineer, is the eventual reader of the logs. Many Smartflow deployments begin as LiteLLM deployments and move when a regulator enters the room.

Feature Comparison

Four rows in this table changed in September 2026. LiteLLM shipped semantic caching, an MCP gateway, an A2A agent gateway and air-gapped enterprise deployment, and published SOC 2 Type 2 and ISO 27001. Earlier versions of this page said otherwise and were wrong. Sources are listed at the foot of the page.

What each one is built to do

LiteLLM describes itself as the AI gateway for platform teams, and the product follows that description honestly: put the full AI stack behind one key, see who is driving spend, cap it before it runs, and route each request to the model that should handle it. The center of gravity is the platform team's problem — fragmentation, cost, and visibility across a growing model estate.

Smartflow starts from a different question. Not who spent what, but what authority was exercised, against which policy version, and whether the record of it survives an examination three years later. That reframing is what produces the architectural differences below. It is not a claim that one problem matters more than the other; most enterprises have both, and a good many run something like LiteLLM underneath a governance obligation it was never designed to discharge.

Deployment and the data path

Both products self-host, and both reach air-gapped environments — LiteLLM lists air-gapped deployment among its enterprise options, and documents that no telemetry is stored on its servers when self-hosted. On this axis the two are closer than they were a year ago, and a comparison written before 2026 would mislead you.

The remaining difference is what the control plane is for. Smartflow's control plane is the policy decision point: every request is evaluated against a policy version, and that version identifier is written into the record alongside the decision. The question a regulated buyer asks is not whether data left the perimeter but whether the enforcement decision is reconstructible after the fact. That is an evidence design problem, and it is the one Smartflow is organized around.

Agent and MCP governance

This row used to be the clearest line between the two products. It is not any more. LiteLLM ships a full MCP gateway with three transports, OAuth 2.0 including PKCE flows, permissions scoped by key, team or organization, semantic tool search, and cost tracking per tool invocation. It also ships an A2A agent gateway with agent cards, per-team access control and iteration budgets. Anyone claiming LiteLLM has no agent story has not read the documentation.

Where Smartflow still differs is identity. LiteLLM governs agent access through keys, teams and organizations — the same primitives that govern human access. AIDA issues a cryptographic credential to the agent itself, scoped and revocable independently of whoever launched it, so the audit record names the agent as a principal rather than inheriting a human's key. In a supervision conversation under FINRA Rule 3110 that distinction is the conversation. Elsewhere it may be an abstraction you do not need.

Evidence for examiners

LiteLLM's audit logging records create, update and delete operations against teams and virtual keys, capturing the acting user, the action, previous values and updated values. That is a competent administrative audit trail, and it is enterprise-licensed. Its compliance posture is expressed the way most infrastructure vendors express it — through SOC 2 Type 2 and ISO 27001 attestations of the vendor's own controls.

Smartflow's Regulatory Examination Suite answers a different question. Not "is the vendor well run" but "can this institution produce, on demand, the inventory of models in use, the policy in force on a given date, and the record of what each agent was permitted to do." That artifact is what a bank examiner asks for, and assembling it by hand after the fact is where most of the cost of an examination sits.

Where LiteLLM excels

LiteLLM has the broadest provider support in the market, the largest open-source community in this category, and the fastest path from zero to a working routing layer. The free tier is genuinely useful rather than a trial in disguise: virtual keys, spend tracking, budgets, rate limits, teams, load balancing, fallbacks and guardrails are all in the MIT-licensed proxy. Its 2026 engineering record is strong — a Rust rewrite, an auto-router, a managed agents preview — and it responded to a March 2026 supply-chain incident with a published post-mortem, engaged forensics and a rebuilt CI pipeline, which is better disclosure than the category norm.

For an engineering team building internal tooling without a regulator in the picture, LiteLLM is a strong choice and Smartflow is likely more machinery than the problem needs.

When to choose Smartflow

• Regulatory obligations: HIPAA, SOX, FINRA, the EU AI Act, or any framework requiring per-user audit trails and reproducible evidence
• Enterprise identity as a governance boundary: Active Directory, Entra ID or SAML SSO as the control point for AI access, not only for the admin console
• Agent identity: a scoped, revocable credential held by the agent itself, where the audit record still names the person who authorized it
• Examination readiness: evidence generated from runtime records rather than assembled by hand when the request arrives

Migrating from LiteLLM

Both expose an OpenAI-compatible interface, so the application-side change is usually a base URL and a key. The work that is not trivial is policy translation: LiteLLM guardrail configuration and per-key controls do not map one-to-one onto a policy model that versions its rules and writes the version into every decision record. Plan that as a design exercise rather than a config conversion, and run both in parallel while the policy set is validated. Most teams keep the provider catalogue and virtual-key structure they already have.

Common questions

Does LiteLLM support semantic caching? Yes. LiteLLM documents both exact-match and semantic caching in the open-source proxy, across Redis, Valkey, managed cloud Redis services and object storage.

Is LiteLLM free for enterprise use? The proxy is MIT-licensed and free to self-host. SSO, SCIM, audit logs, secret-manager integrations, air-gapped deployment and several guardrail controls require a commercial license, quoted annually against request capacity rather than per seat.

Can the two run together? Yes, and some do — LiteLLM as the provider abstraction, Smartflow as the policy and evidence layer in front of it. Whether that is worth the extra hop depends on how much of the LiteLLM provider catalogue you actually use.

What changed in LiteLLM in 2026? Semantic caching, an MCP gateway, an A2A agent gateway, a Rust runtime in beta, SOC 2 Type 2 and ISO 27001, and a March 2026 supply-chain incident affecting two PyPI releases for roughly forty minutes, with official Docker images unaffected.

Related reading

Enterprise AI gateway guide · Semantic caching · Policy as code · Examination readiness · All comparisons · Model allowlist and denylist · AI guardrails · Agentic AI · MCP security

Verified against each vendor’s published documentation in September 2026. Vendor capabilities change. If something here is out of date, tell us and we will correct it.

LiteLLM sources: litellm.ai, litellm.ai/pricing, litellm.ai/enterprise, docs.litellm.ai (caching, enterprise, mcp, a2a, scim, data_security, deploy), docs.litellm.ai/blog (security-update-march-2026, soc2-type-2-report), github.com/BerriAI/litellm LICENSE.