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Comparez TrueFoundry à Portkey

Quand TrueFoundry a-t-il du sens ?

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Principaux facteurs de différenciation concurrentiels
True Foundry
Clé de port
Architecture et performances des passerelles
Niveau entreprise avec des performances rapides de seulement ~~3ms latency at 250 RPS per pod, scaling linearly. Auth, rate limiting, and guardrails all run in-memory on the hot path — no plugin chain overhead, no licensing surprises.
Passerelle open source avec des performances décentes (latence supplémentaire d'environ 20 à 40 ms)
Routing & Load Balancing
Native latency-based routing using inter-token latency / TPOT, adaptive priority with SLA cutoffs, typed YAML policies, and OTEL export. Routing is configurable at team, model, and application level
AI routing is a plugin layer on top of a general API gateway. Feature availability depends on your plugin version and license tier.
Routage et fiabilité
Fournit
Conçu pour garantir la fiabilité de la production avec de nouvelles tentatives automatiques, un basculement par le fournisseur et une mise en cache.
Options de déploiement
Déploiement natif de Kubernetes dans le VPC du client (dans le cloud ou sur site)
Peut être auto-hébergé ou utilisé comme service cloud ; principalement un intergiciel d'API (sans état)
Flexibilité du LLM
N'importe quel modèle, n'importe quelle pile : sur votre infrastructure ou sur votre route vers des API externes, selon les besoins. Pas de dépendance entre Bedrock/fournisseur : une seule passerelle pour les modèles locaux et distants
Se connecte à plus de 250 modèles (OpenAI, Anthropic, Cohere, etc.) via une API unifiée ;
Fonctionnalité MCP
fournit un accès unifié à tous les serveurs MCP enregistrés, une découverte instantanée via un registre central et un contrôle d'accès sécurisé avec OAuth 2.0 et des fournisseurs d'identité fédérés —
Fonctionnalité limitée pour l'intégration du MCP pour une utilisation en entreprise
Guardrails
Subject-scoped rules, MCP per-invocation hooks, built-in PII/PHI detection — all in-process, zero external dependencies. HIPAA, GDPR, GovCloud, and air-gap ready.
Native semantic guardrails (embedding-based prompt and response guards) available. But they're incremental plugins on a general gateway, not a cohesive AI governance architecture.
Observability
Full-stack observability: OTEL export, Prometheus/Grafana integration, and built-in Metrics Dashboard.
Integrates into existing Kong OTel/Prometheus/Grafana pipelines — great if you're already there. AI metrics require explicit plugin configuration. Prompt body capture needs a deliberate redaction strategy.
Open source contre freemium

Communauté open source avec
Intégration de l'écosystème
Intégration étendue : fonctionne au sein de vos pipelines CI/CD et GitOps ; se connecte à Kafka/SQS pour les pipelines asynchrones. Fonctionne bien avec les services cloud (AWS, GCP) mais reste indépendant du cloud. API ouvertes pour intégrer des outils personnalisés.

connecteurs pour LangChain, LLamaIndex,
soutien
Assistance aux entreprises 24 h/24 et 7 j/7 via Slack et sur appel
ingénieurs (AM dédiée).
Support piloté par la communauté (Discord/GitHub pour OSS). Le plan Enterprise offre un support SLA, mais globalement configuration de support plus petite (échelle de démarrage).

Principales questions d'évaluation

Question
Comment TrueFoundry y remédie
Kong considerations
We need full data sovereignty — no payload or metadata egress.
UNE
Aucune option pour héberger des LLM open source sur leur plateforme. Faire face à une latence plus élevée que prévu
« Pouvons-nous optimiser nos coûts d'utilisation de LLM ? »
TrueFoundry peut
L'utilisation de plusieurs fournisseurs via Portkey peut éviter de surpayer un fournisseur et vous bénéficiez d'un suivi des coûts. Cependant, vous payez toujours par appel d'API (OpenAI, etc.) et l'hébergement de modèles locaux n'est pas automatisé. Toutes les économies réalisées grâce à l'auto-hébergement nécessitent de créer vous-même cette infrastructure.
« Souhaitez-vous essayer d'autres fonctionnalités sur les serveurs MCP ? »
Passerelle MCP TrueFoundry permet l'exécution de tâches agentiques sur tous les outils, offre une observabilité de niveau entreprise avec un suivi au niveau des demandes et des journaux d'audit, prend en charge les intégrations prêtes à l'emploi et personnalisées (par exemple, Slack, Datadog, API internes) et garantit un fonctionnement performant dans les environnements cloud, sur site et hybrides.
Portkey fournit des fonctionnalités limitées
How do we control AI costs across teams and self-hosted models?
TrueFoundry offre une observabilité de bout en bout : vous obtenez non seulement des statistiques sur les demandes, mais également des journaux de conteneurs, une surveillance en direct et des alertes jusqu'au niveau du pod. Les développeurs peuvent déboguer les échecs
Portkey donne du bien
Do we need full-stack observability or just LLM-level metrics?
La plateforme de TrueFoundry est
Portkey est
We want to move from external APIs to self-hosted models without re-architecting.
TrueFoundry manages both external API routing and self-hosted model deployment from one platform. Moving from OpenAI to a private Llama deployment is a configuration change, not a migration. Training, fine-tuning, serving, and gateway are unified.
Kong routes AI traffic to wherever you point it, external or self-hosted. But model deployment, training, and fine-tuning are entirely outside its scope. As your AI stack matures, you’ll need additional platforms to cover what Kong doesn’t.

Comment TrueFoundry agit en tant qu'analgésique

Principaux points faibles
Avantages de l'utilisation de TrueFoundry
Impact sur les clients
AI Features Held Hostage by License Tier
Plateforme unifiée pour
De multiples plateformes à gérer ;
Plugin Complexity That Grows With Your Stack
ou heures —
Les data scientists attendent l'ingénierie ;
No Native Support for Self-Hosted Models
contre les approches naïves. De plus, la possibilité d'héberger vos propres modèles réduit le recours à des fournisseurs d'API onéreux, ce qui réduit directement les coûts variables.
Dépassements de budget et factures surprises ; la direction suspend les projets en raison des coûts. L'exécution de modèles open source dans le cloud sans optimisation entraîne le paiement de ressources inutilisées ou d'instances hors de prix.
Incomplete Data Sovereignty
traces d'erreurs détaillées et mesures de performance
Angles morts
Limited MCP & Agent Governance
signifie Data Science et
Un outil DevOps intensif : les ingénieurs ajustent en permanence l'infrastructure, mettent à jour les images Docker, gèrent les politiques de dimensionnement. Cela nuit au développement de fonctionnalités et peut introduire des erreurs.
Slow Time-to-Production for AI Teams
Self-serve deployments in hours. TrueFoundry automates environment setup, scaling, routing, and CI/CD validation, including prompt version enforcement as a deployment gate. Teams achieve 80%+ reduction in time-to-production.
Kong is a powerful platform for teams with strong gateway operations expertise. For AI teams starting fresh, the configuration surface area, plugin chains, decK state files, license tier management, adds meaningful time between idea and production. TrueFoundry removes that ramp-up entirely.

Pièges courants à éviter

en utilisant une plateforme indépendante du cloud telle que TrueFoundry sur Portkey

  • Assuming your existing Kong setup covers your AI requirements. Extending Kong to AI traffic is straightforward, but AI-specific capabilities like MCP governance, semantic guardrails, and PII sanitization are version and license-gated. Audit which features you actually need and confirm they’re available at your current tier before standardizing.
  • Underestimating MCP governance maturity requirements. Kong gives you tool-level access controls — but that's not the same as governing what tools actually do. Production agents need guardrails that fire before and after every tool call, proper credential isolation, and a real policy engine. Kong doesn't have that yet.
  • Conflating license tier flexibility with cost predictability. A lower entry-tier price looks attractive until the features you need are behind an enterprise gate. Factor in the full license cost for your required capability set, not the entry price, when comparing TCO.
  • Mistaking plugin composability for a unified AI platform. Kong’s plugin model is genuinely powerful, but composing the right plugins for AI governance requires ongoing version management and compatibility testing. That’s engineering overhead that grows with your AI stack.
  • Building agent infrastructure on a general-purpose API gateway. Retries, fallbacks, and plugin-based traffic governance handle individual calls well. Long-running agents need a native async execution substrate. Without one, your team owns the orchestration layer and maintains it indefinitely.
  • Underestimating the operational overhead for teams without existing Kong expertise. Kong rewards teams that already know it. For AI teams starting fresh, the configuration surface area adds meaningful ramp-up time before you’re shipping AI products to production.

Des résultats concrets chez TrueFoundry

Découvrez les résultats réels obtenus par TrueFoundry par rapport à SageMaker

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Déploie un déploiement de passerelle LLM multirégional et a configuré le RBAC pour l'accès au modèle et au MCP via la passerelle

Contrôle l'accès aux modèles et rétrofacturation aux équipes par le biais de la comptabilité analytique

Exploration et utilisation pour de multiples cas d'utilisation.

Acheminez tous les appels d'inférence d'IA entre l'expérimentation et la production, en traitant plus d'un milliard de jetons par mois sur environ 10 applications

Gérez et acheminez l'inférence entre plusieurs modèles, y compris ceux auto-hébergés, en traitant les demandes avec une fiabilité digne de la production.

FAQ/Objections courantes

What’s the key difference between TrueFoundry and Kong AI Gateway?

La différence entre Portkey et TrueFoundry est que Portkey est une passerelle IA. Il achemine et surveille vos appels d'API vers des fournisseurs de modèles externes. TrueFoundry est une plateforme d'infrastructure d'IA complète. Oui, notre passerelle gère le routage comme le fait Portkey, mais nous gérons également le calcul proprement dit en dessous. Cela signifie que vous pouvez former des modèles, les affiner et les déployer sur votre propre infrastructure, et pas simplement acheminer le trafic vers l'API de quelqu'un d'autre.

We’re already running Kong for our APIs. Should we use it for AI too?

If your AI requirements are stable and fit comfortably within Kong’s plugin model — external API routing, basic governance, and observability on top of your existing stack — extending Kong is a reasonable path. Where it gets complicated: AI-specific capabilities like MCP governance, semantic guardrails, and cost attribution at the self-hosted model level require enterprise licensing and specific plugin versions. And as your needs evolve toward self-hosted model deployment, agentic infrastructure, and full data sovereignty, Kong’s general-purpose architecture starts to work against you. TrueFoundry is worth evaluating as a purpose-built alternative before you’re locked into a plugin architecture that wasn’t designed for where AI infrastructure is heading.

How does MCP governance compare between the two platforms?

TrueFoundry provides a purpose-built MCP governance surface: dedicated pre/post-tool guardrail hooks, Virtual MCP Servers, Cedar-based policy engine, inbound OAuth, and Secret Groups for credential isolation — all running inside your K8s cluster and production-ready today. Kong’s AI MCP Proxy, per-tool ACLs, and AI MCP OAuth2 give it a real native MCP control surface — and for existing Kong users, it’s incremental to add. The practical gap is complexity: Kong’s MCP implementation requires careful route topology management to keep direct MCP routes separate from LLM-side AI plugin flows. That’s a meaningful operational burden as your agentic workload complexity grows.

How does data residency differ?

TrueFoundry runs the entire hot path — auth, rate limits, guardrails, PII/PHI detection, traces — inside your Kubernetes cluster with no external dependencies. Full sovereignty is the default architecture, not a configuration option. Kong has a strong data governance plugin catalog, but the most critical capabilities for regulated industries — bidirectional PII sanitization with restoration, advanced compliance controls — are enterprise license-gated and version-dependent. For teams where compliance is non-negotiable, that dependency on license tier is a risk worth pressure-testing early.

Which platform is better for production agent workloads?

TrueFoundry is the only platform in this comparison explicitly documenting both gateway governance and execution lifecycle from one architecture. Guardrails fire at every point in the agent lifecycle — LLM input, LLM output, before a tool is called, and after it returns — and the split-plane design means the gateway governs traffic while async services handle durable, long-running loops. Kong’s AI MCP Proxy brings tool traffic into the plugin ecosystem without a separate governance plane, which is genuinely useful for existing Kong users. But there’s no native async execution substrate — long-running agent loops require application-side orchestration that your team builds and maintains separately.

How does observability compare?

TrueFoundry provides full-stack visibility out of the box: LLM request traces connected to GPU memory, pod health, and container logs in a single UI — no configuration required to get meaningful signal. Kong’s observability is genuinely powerful for teams already running its OTel/Prometheus/Grafana stack — LLM traffic joins the same pipeline as everything else. The tradeoff is setup: AI cost and token metrics require explicit plugin configuration to surface, and prompt body capture needs a deliberate redaction strategy before you see your first useful metric.

How does cost control work across teams and self-hosted models?

TrueFoundry enforces budgets on the hot path — overspend is blocked before it happens, not flagged after. Cost attribution runs across teams, users, models, and applications for both external API calls and self-hosted model fleets, with Public/Private Cost pricing for internal chargebacks. We document 35–50% TCO reduction through Kubernetes workload optimization and spot/GPU scheduling. Kong’s ai-rate-limiting-advanced plugin is strong for token and request enforcement, but cost-based blocking is slightly lagged. USD-level analytics require external tooling, and self-hosted model cost attribution isn’t natively supported.

Which platform is better for prompt management?

TrueFoundry offers the most GitOps-integrated prompt story: version history in the registry, compare/diff workflows, prompt version references enforced as CI gates, and dry-run/show-diff deployment previews. Prompt changes and infrastructure changes live in the same pipeline. Kong’s decK gives you a solid GitOps story for gateway configuration, and AI Prompt Decorator handles gateway-level prompt injection cleanly. The gap is prompt lifecycle depth: no versioning registry, no standalone playground, and no per-model prompt overrides. For teams doing active prompt iteration and needing CI-gated deployments, Kong’s tooling stops well short of what’s needed.

Kong has a large open-source community. How does TrueFoundry compete?

Kong’s open-source community is a genuine asset — years of production use, extensive plugin documentation, and a large ecosystem of operators who know it well. TrueFoundry competes on depth and focus: we’re built specifically for AI infrastructure, and our support model reflects that — 24×7 enterprise support via Slack and on-call engineers, a dedicated AM, and a G2 support rating of 9.9/10. Community support is valuable for API gateway operations. For production AI infrastructure with compliance requirements and SLA obligations, you want a team with direct accountability, not a forum thread.

Is TrueFoundry overkill if we only need AI gateway routing today?

TrueFoundry works well in a lightweight routing mode — you get unified monitoring across all providers, guardrails, and cost controls without requiring the full platform footprint. The more important question is where your AI stack is heading: cost pressures drive self-hosted models, compliance requirements demand full residency, and agentic use cases require MCP and agent governance. TrueFoundry is already built for that evolution. Teams that start with Kong for AI routing often face a more disruptive migration later when those needs emerge and Kong’s general-purpose architecture wasn’t designed to meet them.

Do teams with strong platform engineering capabilities need TrueFoundry?

Strong platform teams can absolutely make Kong work for AI. The plugin ecosystem is flexible and the operational model is well understood. The question is whether you want your best engineers spending cycles on plugin version management, AI feature compatibility testing, and building the orchestration layer that Kong doesn’t provide or on the AI products and models that actually create business value. TrueFoundry provides battle-tested automation for the infrastructure layer so strong teams can move faster, not slower.
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