TrueFoundry vs Kong

Kong tracks tokens, not dollars. Choose TrueFoundry to get full visibility and control into every dollar of usage across models, teams, and users.

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4–5x Lower configuration burden
30% Reduction in LLM costs
1T+ Tokens processed/day
9.9/10 G2 rating

An API Gateway is Not an AI Gateway

Kong is fundamentally an API gateway, engineered for cheap, instant, stateless API calls, not expensive, chained AI requests. TrueFoundry was built AI-native, with AI Gateway, MCP Gateway, and Agent Gateway deployed inside your VPC.

Built AI-Native. Not Bolted On.

Kong added AI capability as ~20-25 plugins on top of an API gateway. The result is 4-5x more setup each time you add a model on Kong versus TrueFoundry.

Complete Cost Observability

Kong has no per-token cost tracking for self-hosted customers, and no way to enforce cost-based policies. TrueFoundry has both, natively.

Human-in-the-Loop for Agents

Kong's MCP Gateway has no way to pause a tool call for human approval. With TrueFoundry, you can govern every agentic action even as you scale.

Feature Comparison

What you give up by choosing Kong

Grouped the way a real evaluation runs — from table stakes to the things that decide whether AI ships.

Capability
TrueFoundry logo TrueFoundry
Kong logo Kong
The basics: what any gateway should already give you
Model coverage
2,100+ models behind one API
Major providers
Cross-provider failover
Fallback with per-target retries
Balancer and circuit breaker
Rate limiting per consumer and model
Per user, team, key, model, tag
Enterprise tier only
SSO, RBAC and SCIM
OIDC, SAML, SCIM, custom roles
SSO and audit on paid tiers
Air-gapped deployment
SaaS through VPC to air-gapped
OSS, Enterprise, hybrid, K8s
Compliance certifications
SOC 2 Type II, HIPAA, GDPR
SOC 2 Type II, PCI
Configuration burden per model
Objects needed to add one model
One virtual model object
Three every time: Service, Route, plugin
Does setup cost grow per model?
No — the abstraction is reused
Yes — rebuilt per model
Scoped, auto-rotating key as one object
Virtual accounts with auto-rotation
Consumer + key auth + plugin
Pooled quota across provider keys
One virtual model, weighted spillover
No pooled quota object
Prompt versioning with rollback
Versioned, reusable, traceable
Templating only
AI layer: designed in or added on?
AI-native from the ground up
20–25 plugins on an API proxy
Cost observability and control
Cost tracking vs token counting
Native per-token cost tracking
Token counting and rate limiting only
Cost tracking for self-hosted models
Yes, including on-prem
None built in
Survives a provider price change
Maintained price catalog
Token-to-cost mapping breaks each time
Spend caps and budgets
Per-team dollar budgets
No way to set one
Block or route on a cost threshold
Cost-based policy enforcement
No cost-based policies
Budget alerts before the cap
Slack, Teams, PagerDuty
No budget alerts
Human control over agents
Pause a tool call for human approval
Native approval gate
No way to pause one
Paused call held in durable state and resumed
Held until approved or denied
Not supported
Guardrails on tool calls, not just prompts
Checks before and after every call
Listed as not supported
MCP authentication production-ready
GA, standards-based OAuth
Tech Preview, not for production
Agent acts as the logged-in user
Consent with stored refresh tokens
Per-request pass or exchange only
Per-tool user credentials
Auth overrides
Not documented
Self-hostable tool registry
GA and self-hostable
Tech Preview, hosted cloud only
Tool-level access control for MCP
Tool-level RBAC
Tool ACLs at GA
Observability into every AI request
Model, tool and guardrail as one trace
One waterfall per request
No shared trace ID across plugins
Debug a failed run without stitching logs
Every span on one timeline
Manual correlation across log streams
Request logs on by default
On by default
Off until you add a plugin
Dashboards in self-hosted deployments
Built in, plus OTEL and Prometheus
Hosted cloud only; self-host is DIY
Logs to your own storage bucket
Native S3, GCS, Azure
Wire up Fluent Bit or Kafka yourself
Infra-level visibility (GPU, pods, logs)
Same UI as LLM traces
Out of scope — Kong does not host models
Production challenges

Why teams look for a Kong alternative

Kong might work as an API Gateway, but as teams move AI workflows into large-scale production, they begin to see the limitations of extending Kong as an AI Gateway.

01

Kong sees one call at a time

One agent request turns into dozens of model and tool calls. Kong checks each one on its own and forgets it, so nothing tracks what the full request cost, who it ran for, or whether it should have been allowed.

02

You can't require human sign-off on an agent action

Kong has no way to hold a tool call while a person reviews it. So an agent that can send money, delete records or push code does it the moment it decides to — which is why most teams keep their agents read-only.

03

You get token counts, not spend

Kong counts tokens, not dollars. To see cost, someone on your team enters the price of every model by hand and updates it each time a provider reprices. Self-hosted models get no cost tracking at all, and there is no way to set a budget that actually blocks a request.

04

Guardrails don't check what tools do

Kong's guardrails read prompts and model responses only. The tool call itself, and the data it sends back, are never inspected, so a prompt-injection test passes while the risky path goes unchecked.

05

Debugging means piecing logs together

Kong doesn't give a request one shared ID across its plugins, so the model call, the tool call and the guardrail all land in different places. Every investigation starts with rebuilding what happened by hand.

06

Kong routes to models, it doesn't run them

Deploying, fine-tuning and serving your own models are outside Kong's scope. The day you move a workload to a private model, you are buying and integrating a second platform.

The fix

How TrueFoundry acts as a painkiller

Where Kong breaks
TrueFoundry logo How TrueFoundry solves it
Business impact
Limited MCP and agent governance
Human approval gates on destructive tool calls, pre/post-tool guardrail hooks, Virtual MCP Servers
Every risky action has a named approver and a recorded decision. Agents move from pilot to production.
No dollar-based cost controls
Native per-token cost tracking, budgets enforced on the hot path, attribution by team, user, model and application
Budgets stop overspend at the limit, not after the invoice. Nobody reconciles model prices by hand.
Plugin complexity that grows with your stack
One virtual model object per model — adding or swapping a model is a single change
Engineering time shifts from gateway plumbing to AI products. The configuration surface stays flat.
Incomplete data sovereignty
Auth, rate limits, guardrails and PII/PHI detection all run in-process inside your cluster
Security signs off without exceptions. Regulated and air-gapped workloads use the same architecture.
No native support for self-hosted models
External API routing and self-hosted deployment from one interface
No second platform, no second integration, no unbudgeted migration.
Slow time-to-production
Platform teams set policy once; application teams self-serve within those bounds
Teams ship in hours, not tickets. The platform team stops being the bottleneck.

Route on dollars, not just requests

Latency-based routing, per-team budgets enforced before spend, and guardrails that read prompts and tool calls. No AI plugin required.

Try the AI gateway free
Evaluation checklist

Six things to pressure-test before you standardize

Before using Kong as your AI Gateway, ask your provider these six critical questions.

1

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 delete the wrong database, and every check will have passed.

2

Test guardrails on a tool call, not a prompt

Most evaluations send a prompt-injection string and confirm it is blocked. Kong's guardrails attach per Route or Service, so they never see the tool-call path.

3

Count configuration cost every time, not once

The first model looks reasonable anywhere. Multiply by every model, provider and environment over two years.

4

Confirm you can cap, alert on and attribute spend in dollars

Token limits are not budgets. Models differ by orders of magnitude per token, so a request well inside quota can still be expensive.

5

Check what "on-prem" actually means

Ask where the control plane runs in the hybrid deployment, and which compliance features are license-gated.

6

Ask what state survives a request

Approval gates, brokered per-user credentials and long-running agent loops all need state that outlives the call. Without it, your team owns the orchestration layer indefinitely.

How to decide

When to settle and when to scale

Choose TrueFoundry when

  • You are adding models and teams faster than you can configure a Service, Route, and plugin for each one
  • You are running agents that execute tool calls which write, transact, or deploy
  • You need spend attributed by team and capped in dollars before a request is made
  • You need security and compliance to see data residency, guardrails, and approval records
  • You expect to run self-hosted or fine-tuned models alongside provider APIs

Kong might be adequate when

  • You are running one or two providers and a small set of models from a single team
  • You are still in prototypes and internal tools, where an incorrect response carries limited consequence

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.