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

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.

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.

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.

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.

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.

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.

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Evaluation checklist

Six things to pressure-test before you standardize

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

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.

Check what "on-prem" actually means

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

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

FAQs/Common Objections

Do we have to replace Kong?

No. Kong is an excellent API gateway and deserves its place in your stack. The practical answer is division of labour: Kong stays at the edge for REST, gRPC and Kafka; a purpose-built AI gateway takes LLM, MCP and agent traffic. TrueFoundry deploys alongside it.

Kong has an MCP OAuth plugin. Doesn't that cover auth?

It covers who is calling your gateway, not who your agents act as downstream. Kong validates inbound tokens and supports token exchange where your identity provider offers it, but there is no authorization-code consent flow to third-party providers. For an agent to act as a specific person in Slack or GitHub, your engineers build and operate consent, token storage and refresh.

Can we wait? Kong ships quickly.

Some gaps are roadmap items. Two are design choices: approval enforcement is delegated to the agent's client, and per-user tool authentication assumes your identity provider handles it. Closing either requires what a phase-based proxy is built to avoid — state that survives the request. That is a new stateful subsystem with its own storage, failover and tenancy, not a plugin.

We only do model routing today. Do we need this?

TrueFoundry runs fine as a lightweight routing layer with monitoring, guardrails and cost controls. But routing is the easy thing to move later. Approval gates, per-user credentials and chain-level cost are what force a re-platform.

What changes on day one?

Nothing at your edge. You point AI traffic at the AI gateway and keep Kong where it is.

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Keep Kong for APIs. Put AI behind an AI gateway.

Free tier includes AI Gateway, MCP Gateway and prompt management.

Try the AI gateway free

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Real Outcomes at TrueFoundry

Why Enterprises Choose TrueFoundry

3x

faster time to value with autonomous LLM agents

80%

higher GPU‑cluster utilization after automated agent optimization

Aaron Erickson

Founder, Applied AI Lab

TrueFoundry turned our GPU fleet into an autonomous, self‑optimizing engine - driving 80 % more utilization and saving us millions in idle compute.

5x

faster time to productionize internal AI/ML platform

50%

lower cloud spend after migrating workloads to TrueFoundry

Pratik Agrawal

Sr. Director, Data Science & AI Innovation

TrueFoundry helped us move from experimentation to production in record time. What would've taken over a year was done in months - with better dev adoption.

80%

reduction in time-to-production for models

35%

cloud cost savings compared to the previous SageMaker setup

Vibhas Gejji

Staff ML Engineer

We cut DevOps burden and simplified production rollouts across teams. TrueFoundry accelerated ML delivery with infra that scales from experiments to robust services.

50%

faster RAG/Agent stack deployment

60%

reduction in maintenance overhead for RAG/agent pipelines

Indroneel G.

Intelligent Process Leader

TrueFoundry helped us deploy a full RAG stack - including pipelines, vector DBs, APIs, and UI—twice as fast with full control over self-hosted infrastructure.

60%

faster AI deployments

~40-50%

Effective Cost reduction of across dev environments

Nilav Ghosh

Senior Director, AI

With TrueFoundry, we reduced deployment timelines by over half and lowered infrastructure overhead through a unified MLOps interface—accelerating value delivery.

<2

weeks to migrate all production models

75%

reduction in data‑science coordination time, accelerating model updates and feature rollouts

Rajat Bansal

CTO

We saved big on infra costs and cut DS coordination time by 75%. TrueFoundry boosted our model deployment velocity across teams.