TrueFoundry vs LiteLLM

LiteLLM is free to license, not free to operate. TrueFoundry delivers enterprise governance, guardrails across the full agent lifecycle, and a single platform your team does not have to assemble.

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A Proxy Is Not a Governance Layer

LiteLLM is a fast, code-first way to unify LLM providers. What an enterprise needs on top of that routing is either gated behind a paid tier, left as infrastructure for your team to run, or absent from the agent path altogether.

Enterprise Controls Sit Behind a Paid Tier

SSO, SCIM, audit logs, per-key guardrail control and secrets detection all require a LiteLLM Enterprise licence. TrueFoundry treats these as core enterprise capabilities rather than an upgrade path.

Nothing Inspects What a Tool Returns

LiteLLM runs guardrails before and during an MCP tool call. There is no documented hook after the tool returns, and nothing holds a sensitive call for human approval. TrueFoundry does both.

The Gateway Is Yours to Operate

A production deployment means the proxy, a Postgres database, and Redis as soon as you run more than one instance. TrueFoundry ships as one Helm chart, fully supported.

Feature Comparison

What an Enterprise Evaluation Actually Tests

Both platforms route model traffic well. The differences appear in identity, guardrails on the agent path, cost enforcement, and what your team has to operate.

Capability
TrueFoundry logo TrueFoundry
Litellm logo LiteLLM
The basics: what both platforms give you
Unified multi-provider API
1,600+ models behind one API
Broad provider catalog
OpenAI-compatible interface
No lock-in
No lock-in
Self-hosted deployment
VPC, on-prem and air-gapped
Docker or Helm
Fallbacks and load balancing
Latency-based routing with SLA cutoffs
Router with fallbacks and retries
Routing to self-hosted endpoints
Native
Supported
MCP server registry with tool-level access control
Tool-level RBAC
Per key, team and organization
Enterprise identity and access control
SSO (OIDC, SAML)
Included
Requires LiteLLM Enterprise
SCIM provisioning
Included
Requires a premium license
Audit logs with retention policy
Included
Requires LiteLLM Enterprise
Guardrail control per API key
Included
Requires LiteLLM Enterprise
Secrets detection and redaction
Built in, with no external API calls
Requires LiteLLM Enterprise
Workload isolation
Kubernetes namespace boundaries, with per-tenant compute planes
Logical, through virtual keys and teams
Compliance posture
SOC 2 Type II certified, HIPAA and GDPR compliant
Not stated in product documentation
Guardrails across the agent lifecycle
Guardrails on LLM input and output
Sync hooks on both
pre_call, during_call and post_call
Guardrails before an MCP tool call
MCP Pre Tool hook, the tool does not run
pre_mcp_call and during_mcp_call
Guardrails after a tool returns
MCP Post Tool hook, the result is withheld from the model
No post-tool hook documented
Human approval gate on a sensitive tool
The call is held until a named person decides
Not documented
Policy engine
Cedar and OPA policies
Not documented
Cost control and enforcement
Budget checked before the provider call
Requests are rejected at the limit
Cost is reserved before the request
Durability of the spend counter
Enforced from gateway state
A restarted Redis counter can read lower than recorded spend, allowing spend past max_budget until corrected
Limits when the shared store is unavailable
Enforced in-memory on the hot path
Each instance enforces independently, up to N times the limit across N instances
Attribution by team, user, model and application
Full
Keys, teams, tags and end users
Cost tracking for self-hosted and private-rate models
Private cost rates, including on-prem
Custom pricing configured per model
What your team operates
Systems to run in production
One Helm chart
Proxy plus Postgres, and Redis once you run more than one instance
Published per-pod benchmark
250 RPS on 1 vCPU and 1 GB, under 5ms overhead
Not published in documentation
Configuration propagation across pods
Distributed from the control plane
No cross-pod push, a pod converges within one polling interval
Prompt management
Versioning with a side-by-side diff view
Versioning and rollback, labelled Beta
Model deployment, training and fine-tuning
Same platform, a config change rather than a migration
Out of scope, routing only
Production support
24x7 Slack and on-call engineers, dedicated AM
Community, with Enterprise support available
Production challenges

Why teams look for a LiteLLM alternative

LiteLLM serves early-stage routing well. These are the limits teams encounter when workloads move into regulated, production-scale environments.

01

The controls your security team asks for are gated

SSO, SCIM, audit logs, per-key guardrail control and secrets detection each require a LiteLLM Enterprise licence. The capabilities procurement asks about first sit behind the paid tier.

02

Nothing inspects what a tool returns

MCP guardrails run before and during a tool call. There is no documented hook after the tool returns, so data coming back from a tool reaches the model uninspected.

03

No approval gate on a destructive tool call

Nothing in the documentation holds a sensitive call while a person reviews it. An agent with valid permissions still acts the moment it decides to, which is why most teams keep agents read-only.

04

Your budget is only as durable as Redis

Cost is reserved before the request, but the spend counter lives in Redis. The documentation notes that a restarted counter can read lower than recorded spend, letting a key spend past its limit until it is corrected.

05

You are operating a proxy, Postgres and Redis

Postgres is required for teams and budgets, and Redis becomes necessary as soon as you run more than one instance. That is three systems with three failure modes, maintained by your platform team.

06

LiteLLM routes to your models, it does not run them

Deployment, training and fine-tuning are outside its scope. The day a workload moves to a private model, you are buying and integrating a second platform.

The fix

How TrueFoundry acts as a painkiller

Where LiteLLM breaks
How TrueFoundry solves it
Business impact
Enterprise controls arrive as a licence negotiation
SSO, SCIM, audit logs, per-key guardrail control and secrets detection are included rather than tiered
Security review proceeds on the deployment you already have, without a mid-evaluation upgrade.
The agent path is governed only up to the tool call
Guardrail hooks fire before the tool runs and again after it returns, with the result withheld from the model when a check fails
Data coming back from a tool is inspected rather than trusted, which is what a security team asks to see.
No human checkpoint on a sensitive action
Tool approval policies hold the call until a named person approves or denies it
Every risky action has an approver and a recorded decision, so agents move beyond read-only pilots.
Your team operates infrastructure instead of building AI
One Helm chart covering gateway, MCP, guardrails and model deployment, with no Postgres or Redis to run alongside it
Platform engineering time returns to AI products rather than to the systems underneath them.
Enforcement depends on a shared counter staying healthy
Limits are enforced in-memory on the request path, with attribution across team, user, model and application
A cache restart does not become a spending incident.
Routing is the ceiling
External API routing and self-hosted model deployment, training and fine-tuning managed from one platform
Moving a workload to a private model is a configuration change rather than a second platform purchase.
Evaluation checklist

Six things to pressure-test before you standardize

Before standardizing on LiteLLM for production workloads, put these to your provider.

1

Price the tier you will actually need

SSO, SCIM, audit logs, per-key guardrail control and secrets detection sit behind the Enterprise licence. Confirm the cost once security has reviewed the deployment, not before.

2

Send a poisoned tool result, not a poisoned prompt

Pre-call checks cover a great deal. Ask to see a guardrail inspect what a tool returns before that data reaches the model, and confirm the hook exists.

3

Ask to see a tool call held for a person

Authorization and approval are not the same thing. An agent with valid credentials and correct permissions can still take an action nobody wanted, and every check will have passed.

4

Restart the cache and watch the budget

Budget reservation works well when the counter is healthy. Ask what happens to enforcement when that shared counter restarts and reloads an older snapshot.

5

Count the systems, not the proxy

A production deployment includes Postgres, Redis and every observability and guardrail integration you have added. Each one is scoped, maintained and reviewed separately.

6

Check the maturity label on anything compliance-critical

Prompt management is labelled Beta. Useful and improving, but worth a backup plan where prompt changes touch a regulated workflow.

How to decide

When to settle and when to scale

Choose TrueFoundry when

  • Security requires SSO, SCIM and audit logs as standard rather than as a licence upgrade
  • Agents take actions that need a guardrail on what a tool returns, and a named approver before they run
  • Enforcement has to hold on the request path rather than depend on a shared counter staying healthy
  • You want one supported platform instead of a proxy, a database and a cache to operate
  • You expect to deploy self-hosted or fine-tuned models alongside provider APIs

LiteLLM might be adequate when

  • You are routing across providers at low to moderate volume with a team that can operate the infrastructure
  • You are in development and experimentation, where enforcement and audit requirements have not yet arrived

FAQ / Objections courantes

Devons-nous remplacer Kong ?

Non. Kong est une excellente passerelle API qui mérite sa place dans votre stack. La réponse pragmatique est la division du travail : Kong reste en périphérie pour REST, gRPC et Kafka, tandis qu'une passerelle dédiée à l'IA prend en charge le trafic LLM, MCP et les agents. TrueFoundry se déploie en complément.

Kong dispose d'un plugin OAuth MCP. Cela ne couvre-t-il pas l'authentification ?

Il gère qui appelle votre passerelle, pas pour qui vos agents agissent en aval. Kong valide les jetons entrants et prend en charge l'échange de jetons lorsque votre fournisseur d'identité le permet, mais il n'existe pas de flux de consentement par code d'autorisation vers des fournisseurs tiers. Pour qu'un agent agisse en tant qu'utilisateur spécifique dans Slack ou GitHub, vos ingénieurs doivent concevoir et gérer le consentement, le stockage des jetons et leur renouvellement.

Pouvons-nous attendre ? Kong évolue rapidement.

Certaines lacunes sont prévues dans notre feuille de route. Deux sont des choix de conception : l'application des validations est déléguée au client de l'agent, et l'authentification des outils par utilisateur suppose que votre fournisseur d'identité s'en charge. Combler l'une ou l'autre nécessite ce qu'un proxy par phases cherche justement à éviter : un état qui persiste au-delà de la requête. Il s'agit d'un nouveau sous-système avec état, doté de son propre stockage, de sa gestion de basculement et de sa propre gestion multi-tenant, et non d'un simple plugin.

Nous ne faisons que du routage de modèles pour l'instant. En avons-nous vraiment besoin ?

TrueFoundry fonctionne parfaitement comme couche de routage légère avec monitoring, garde-fous et contrôle des coûts. Mais le routage est l'élément le plus simple à migrer plus tard. Ce sont les étapes de validation, les identifiants par utilisateur et le suivi des coûts par chaîne qui imposent une refonte de la plateforme.

Qu'est-ce qui change dès le premier jour ?

Rien ne change à votre niveau. Vous redirigez le trafic IA vers la passerelle IA et conservez Kong là où il se trouve.
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Gardez Kong pour vos API. Placez l'IA derrière une passerelle dédiée.

L'offre gratuite inclut la passerelle IA, la passerelle MCP et la gestion des prompts.

Essayer gratuitement la passerelle IA
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Parler à un expert
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Aucune carte bancaire requise  ·  SOC 2  ·  G2 9,9/10

Résultats concrets avec TrueFoundry

Pourquoi les entreprises choisissent TrueFoundry

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Innovaccer Company Logo
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3x

accélération du retour sur investissement grâce aux agents LLM autonomes

80 %

meilleure utilisation des clusters GPU après optimisation automatisée des agents

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Aaron Erickson

Fondateur, Applied AI Lab

TrueFoundry a transformé notre parc de GPU en un moteur autonome et auto-optimisé, augmentant notre taux d'utilisation de 80 % et nous faisant économiser des millions en ressources informatiques inutilisées.

5x

accélération de la mise en production de la plateforme IA/ML interne

50 %

de réduction des dépenses cloud après la migration des charges de travail vers TrueFoundry

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Pratik Agrawal

Directeur principal, Data Science et innovation IA

TrueFoundry nous a permis de passer de l'expérimentation à la production en un temps record. Ce qui aurait pris plus d'un an a été réalisé en quelques mois, avec une meilleure adoption par les développeurs.

80 %

réduction du délai de mise en production des modèles

35 %

d'économies sur les coûts cloud par rapport à la configuration SageMaker précédente

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Vibhas Gejji

Ingénieur ML principal

Nous avons réduit la charge DevOps et simplifié les déploiements en production pour toutes les équipes. TrueFoundry a accéléré la livraison ML grâce à une infrastructure qui évolue des expérimentations vers des services robustes.

50 %

déploiement plus rapide de la pile RAG/Agent

60 %

réduction des coûts de maintenance pour les pipelines RAG/agents

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Indroneel G.

Responsable des processus intelligents

TrueFoundry nous a permis de déployer une pile RAG complète — incluant pipelines, bases de données vectorielles, API et interface utilisateur — deux fois plus rapidement, tout en conservant un contrôle total sur notre infrastructure auto-hébergée.

60 %

déploiements d'IA plus rapides

~40-50 %

Réduction effective des coûts sur l'ensemble des environnements de développement

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Nilav Ghosh

Directeur principal, IA

Grâce à TrueFoundry, nous avons réduit les délais de déploiement de plus de moitié et diminué les frais généraux d'infrastructure via une interface MLOps unifiée, accélérant ainsi la création de valeur.

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semaines pour migrer tous les modèles en production

75 %

réduction du temps de coordination en science des données, accélérant les mises à jour des modèles et le déploiement des fonctionnalités

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Rajat Bansal

CTO

Nous avons réalisé d'importantes économies sur les coûts d'infrastructure et réduit le temps de coordination en science des données de 75 %. TrueFoundry a accéléré la vitesse de déploiement de nos modèles au sein de nos équipes.