TrueFoundry vs Kong

Kong 2.0 runs in Kong’s cloud. TrueFoundry runs in yours. Choose TrueFoundry to get full visibility and control into every dollar of usage across models, teams, and users.

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Air-gapped Full gateway, on-prem

30% Reduction in LLM costs

1T+ Tokens processed/day

9.9/10 G2 rating

Kong 2.0 Is a New Product, Not an Upgrade

Kong’s AI Gateway 2.0 is a separate product from its API Gateway, with its own users and groups and a partly manual migration from 1.x. TrueFoundry runs all AI traffic from one control plane.

Built AI-Native. Not Bolted On.

Kong 2.0 still has no approval gates, per-user MCP tokens or ID-JAG. The result is 4-5x more setup each time you add a model on Kong versus TrueFoundry.

Complete Cost Observability

Kong 2.0 can’t be self-hosted, and its cost-based policies need a paid premium plugin. 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

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

AI Gateway 2.0: hosted only

Compliance certifications

SOC 2 Type II, HIPAA, GDPR

SOC 2 Type II, PCI

Configuration burden per model

Full AI gateway on-prem

Yes, including air-gapped

No, hosted control plane only

Shared users for API and AI traffic

One control plane

Separate products

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

Agent identity across apps (ID-JAG)

Supported

Not supported

Cost observability and control

Cost tracking vs token counting

Native per-token cost tracking

Hosted control plane 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

Paid premium plugin

Block or route on a cost threshold

Cost-based policy enforcement

Paid plugin, hosted only

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

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; no self-hosted option

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.

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.

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.

Spend visibility lives in Kong’s cloud

Kong 2.0 shows dollar costs only in Konnect, its hosted control plane, and enforcing a budget needs a paid premium plugin.

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.

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.

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.

Kong 2.0 doesn’t run on-prem

Kong AI Gateway 2.0 runs only on Kong’s hosted control plane, with no on-prem timeline.

The fix

How TrueFoundry acts as a painkiller

Where Kong breaks

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.

Cost controls are paid and hosted-only

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.

Two products for API and AI traffic

One control plane for all AI traffic — adding or swapping a model is a single change

One set of users and permissions to govern. The configuration surface stays flat.

No on-prem option in Kong 2.0

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.

Check what "on-prem" actually means

Ask where the control plane runs in the hybrid deployment, and which compliance features are license-gated. For Kong 2.0, it’s Kong’s cloud.

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.

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.

Count configuration cost every time, not once

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

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.

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 operate in a regulated industry where you need the full AI gateway, control plane included, on-prem or air-gapped
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
You are fine running the AI gateway’s control plane in Kong’s cloud

Veelgestelde vragen/Veelvoorkomende bezwaren

Moeten we Kong vervangen?

Nee. Kong is een uitstekende API-gateway en verdient zijn plek in je stack. Het praktische antwoord is taakverdeling: Kong blijft aan de edge voor REST, gRPC en Kafka; een speciaal gebouwde AI-gateway neemt LLM-, MCP- en agentverkeer voor zijn rekening. TrueFoundry wordt daarnaast ingezet. Overstappen naar Kong 2.0 is al een migratie, dus het is een goed moment om te vergelijken.

Kong heeft een MCP OAuth-plugin. Dekt dat de authenticatie niet?

Het dekt wie je gateway aanroept, niet namens wie je agents downstream handelen. Kong valideert inkomende tokens en ondersteunt tokenuitwisseling waar je identiteitsprovider dit aanbiedt, maar er is geen autorisatiecode-toestemmingsstroom naar externe providers. Om een agent als een specifiek persoon in Slack of GitHub te laten handelen, moeten je engineers toestemming, tokenopslag en verversing zelf bouwen en beheren.

Kunnen we wachten? Kong levert snel.

Sommige hiaten staan op de roadmap. Twee zijn ontwerpkeuzes: goedkeuringshandhaving wordt gedelegeerd aan de client van de agent, en tool-authenticatie per gebruiker gaat ervan uit dat je identiteitsprovider dit afhandelt. Het dichten van een van beide vereist wat een fasegebaseerde proxy juist probeert te vermijden: status die de aanvraag overleeft. Dat is een nieuw stateful subsysteem met eigen opslag, failover en tenancy, geen plugin.

We doen momenteel alleen modelroutering. Hebben we dit nodig?

TrueFoundry werkt prima als een lichtgewicht routeringslaag met monitoring, guardrails en kostenbeheersing. Maar routering is het eenvoudige onderdeel om later te verplaatsen. Goedkeuringspoorten, inloggegevens per gebruiker en kosten op ketenniveau zijn de redenen waarom een herplatforming noodzakelijk wordt.

Wat verandert er op de eerste dag?

Niets aan je edge. Je stuurt AI-verkeer naar de AI-gateway en laat Kong staan waar het staat.

Grey wavy lines on white background, abstract wave pattern with multiple curved lines intersecting smoothly.

Houd Kong voor API's. Plaats AI achter een AI-gateway.

Het gratis abonnement bevat AI Gateway, MCP Gateway en promptbeheer.

Probeer de AI-gateway gratis

Spreek met een expert

Geen creditcard vereist · SOC 2 · G2 9.9/10

Echte resultaten bij TrueFoundry

Waarom ondernemingen voor TrueFoundry kiezen

3x

sneller resultaat met autonome LLM-agents

80%

hogere benutting van GPU-clusters na geautomatiseerde agent-optimalisatie

Aaron Erickson

Founder, Applied AI Lab

TrueFoundry veranderde ons GPU-park in een autonome, zelfoptimaliserende engine – wat leidde tot 80% hogere benutting en ons miljoenen aan ongebruikte rekenkracht bespaarde.

5x

snellere ingebruikname van het interne AI/ML-platform

50%

lagere cloudkosten na het migreren van workloads naar TrueFoundry

Pratik Agrawal

Sr. Director, Data Science & AI Innovation

TrueFoundry hielp ons in recordtempo van experimenteren naar productie te gaan. Wat meer dan een jaar zou hebben geduurd, was in enkele maanden klaar – met een betere adoptie door ontwikkelaars.

80%

kortere doorlooptijd voor het in productie brengen van modellen

35%

cloudkostenbesparing vergeleken met de vorige SageMaker-opstelling

Vibhas Gejji

Staff ML Engineer

We hebben de DevOps-last verminderd en de uitrol naar productie voor teams vereenvoudigd. TrueFoundry heeft de levering van ML versneld met een infrastructuur die schaalt van experimenten naar robuuste services.

50%

snellere implementatie van RAG/Agent-stack

60%

reductie in onderhoudslast voor RAG/agent-pipelines

Indroneel G.

Intelligent Process Leader

TrueFoundry hielp ons bij het implementeren van een volledige RAG-stack - inclusief pipelines, vector-databases, API's en UI - twee keer zo snel, met volledige controle over onze eigen infrastructuur.

60%

snellere AI-implementaties

~40-50%

Effectieve kostenreductie in ontwikkelomgevingen

Nilav Ghosh

Senior Director, AI

Met TrueFoundry hebben we de implementatietijden met meer dan de helft verkort en de overhead van de infrastructuur verlaagd via een uniforme MLOps-interface, waardoor we sneller waarde kunnen leveren.

<2

weken om alle productiemodellen te migreren

75%

reductie in coördinatietijd voor data science, wat modelupdates en de uitrol van functies versnelt

Rajat Bansal

CTO

We hebben flink bespaard op infrastructuurkosten en de coördinatietijd voor data science met 75% verminderd. TrueFoundry heeft onze snelheid van modelimplementatie over alle teams heen vergroot.