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

よくある質問と懸念点

Kongを置き換える必要がありますか？

いいえ。Kongは優れたAPIゲートウェイであり、スタックに不可欠な存在です。現実的な答えは役割分担です。REST、gRPC、KafkaにはKongをエッジに配置し、LLM、MCP、エージェントトラフィックには専用のAIゲートウェイを使用します。TrueFoundryはその横にデプロイされます。

KongにはMCP OAuthプラグインがありますが、それで認証はカバーできないのでしょうか？

それはゲートウェイへの呼び出し元をカバーするものであり、エージェントが下流で誰として振る舞うかをカバーするものではありません。Kongはインバウンドトークンを検証し、IDプロバイダーが対応していればトークン交換をサポートしますが、サードパーティプロバイダーへの認可コード同意フローは存在しません。エージェントがSlackやGitHubで特定の個人として振る舞うには、エンジニアが同意、トークンストレージ、更新の仕組みを構築・運用する必要があります。

待つことは可能でしょうか？Kongは迅速にリリースされます。

一部の機能はロードマップ上の項目ですが、2点は設計上の選択です。承認の強制はエージェントのクライアントに委任されており、ユーザーごとのツール認証はIDプロバイダーが処理することを前提としています。これらを解決するには、フェーズベースのプロキシが避けるべき「リクエストをまたいで存続する状態」が必要になります。これはプラグインではなく、独自のストレージ、フェイルオーバー、テナント管理を備えた新しいステートフルなサブシステムです。

現在はモデルルーティングのみを行っていますが、これが必要でしょうか？

TrueFoundryは、監視、ガードレール、コスト管理を備えた軽量なルーティングレイヤーとして問題なく動作します。しかし、ルーティングの移行は後からでも容易です。承認ゲート、ユーザーごとの認証情報、チェーンレベルのコスト管理こそが、プラットフォームの再構築を必要とする要因となります。

導入初日に何が変わるのか？

エッジ側での変更は不要です。AIトラフィックをAIゲートウェイに向けるだけで、Kongは現在の場所に維持できます。
Grey wavy lines on white background, abstract wave pattern with multiple curved lines intersecting smoothly.

APIにはKongをそのまま使い、AIはAIゲートウェイの背後に配置しましょう。

無料プランには、AIゲートウェイ、MCPゲートウェイ、プロンプト管理が含まれます。

AIゲートウェイを無料で試す
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専門家に相談する
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クレジットカード不要　·　SOC 2準拠　·　G2評価 9.9/10

TrueFoundryがもたらす確かな成果

企業がTrueFoundryを選ぶ理由

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3倍

自律型LLMエージェントで価値創出までの時間を短縮

80%

自動エージェント最適化によるGPUクラスター稼働率の向上

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Aaron Erickson

Applied AI Lab 創設者

TrueFoundryは当社のGPUフリートを自律的かつ自己最適化するエンジンへと変貌させ、稼働率を80%向上させるとともに、アイドル状態のコンピューティングリソースにかかる数百万ドルのコストを削減しました。

5倍

社内AI/MLプラットフォームのプロダクション化までの期間短縮

50%

TrueFoundryへのワークロード移行によるクラウド支出の削減

Smiling Asian Indian business professional man in black suit jacket and white collared shirt portrait.

Pratik Agrawal

データサイエンス＆AIイノベーション担当シニアディレクター

TrueFoundryのおかげで、実験から本番環境への移行を記録的な速さで実現できました。1年以上かかると思われていた作業がわずか数ヶ月で完了し、開発チームへの導入もスムーズに進みました。

80%

モデルのプロダクション投入までの時間短縮

35%

従来のSageMaker環境と比較したクラウドコストの削減

Smiling man with short dark hair and glasses wearing a collared shirt and sweater indoors.

Vibhas Gejji

スタッフMLエンジニア

DevOpsの負荷を軽減し、チーム全体のプロダクションへの展開を簡素化しました。TrueFoundryは、実験から堅牢なサービスまで拡張可能なインフラストラクチャにより、MLデリバリーを加速させました。

50%

RAG/エージェントスタックのデプロイ高速化

60%

RAG/エージェントパイプラインの保守オーバーヘッド削減

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Indroneel G.

インテリジェントプロセスリーダー

TrueFoundryを活用することで、パイプライン、ベクトルDB、API、UIを含むRAGスタック全体を、セルフホスト型インフラを完全に制御しながら、従来の2倍の速さでデプロイできました。

60%

AIデプロイの高速化

約40〜50%

開発環境全体における実質的なコスト削減

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Nilav Ghosh

AI担当シニアディレクター

TrueFoundryの導入により、統一されたMLOpsインターフェースを通じてデプロイ期間を半分以下に短縮し、インフラのオーバーヘッドを削減。価値提供のスピードを加速させました。

2週間未満

本番環境の全モデル移行にかかった期間（週）

75%

データサイエンス関連の調整時間を削減し、モデルの更新と機能リリースのスピードを加速

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Rajat Bansal

CTO

インフラコストを大幅に削減し、データサイエンスチーム間の調整時間を75%短縮しました。TrueFoundryのおかげで、チーム全体のモデルデプロイ速度が飛躍的に向上しました。