DEV Community: TrueFoundry

Best MCP Gateway in 2026: TrueFoundry, Docker, IBM ContextForge, Lasso & Zuplo

TrueFoundry — Mon, 06 Jul 2026 05:48:24 +0000

The Model Context Protocol went from a curiosity to core infrastructure in barely a year. Agents now reach databases, SaaS APIs, and internal services through MCP servers — and every server a developer wires up widens the attack surface, scatters more credentials, and adds another thing nobody’s auditing. That’s exactly the gap an MCP gateway fills. It sits between your agents and the MCP servers they call, giving you one place to control access, isolate credentials, apply guardrails, and log every tool call.

The five gateways below take genuinely different approaches — an integrated AI control plane, a container-native developer tool, an open-source federation layer, a security-first proxy, and an enterprise governance platform. This guide is for platform and security teams choosing where to route their agent traffic. Everything reflects each project’s public documentation at the time of writing.

What Separates A Real MCP Gateway From a Proxy

Registering a few MCP servers behind one URL is the easy part. The dimensions that actually matter in production are narrower: does it isolate credentials so agents never see secrets, can it enforce tool-level access control, does it scan for prompt injection and tool poisoning, does it produce a real audit trail, and can it run where your compliance team needs it to. And increasingly: does it govern models and agents in the same place, or only the tools? Keep that last one in mind — a standalone MCP gateway still leaves your model layer ungoverned.

1. TrueFoundry — Best For Unified Model, Agent, and Tool Governance

TrueFoundry’s MCP Gateway stands out because it isn’t a standalone tool — it’s part of the same control plane that governs models and agents. You register approved servers from one place, whether they’re TrueFoundry-managed, official remotes (GitHub, Sentry, Atlassian), any custom remote server, or an existing API imported from an OpenAPI spec.

Authentication is split into inbound (how callers reach the gateway) and outbound (how the gateway authenticates to each downstream server), supporting API key, OAuth2, and token passthrough — so credentials stay in the gateway and agents call tools by name without ever seeing a secret. Access control is tool-level: you decide which users, teams, or agents can invoke which servers and even which individual tools, and virtual MCP servers let you expose a curated subset per team.

Once a server is registered, developers point their IDE at the gateway rather than wiring up raw servers and keys — one endpoint, governed centrally:
{
"mcpServers": {
"truefoundry": {
"url": "https://.truefoundry.com/api/mcp-servers//mcp",
"headers": { "Authorization": "Bearer " }
}
}
}

On top of access, it applies pre- and post-execution guardrails on tool calls (via Cedar/OPA policies), approval gates that pause destructive operations for human confirmation, and a full OpenTelemetry audit trail of every call. Because it’s the same platform as the AI Gateway, model access and tool calls share one set of identities, policies, and logs — and it all runs in your own VPC if you need it, on infrastructure that is SOC 2, ISO 27001, GDPR, and HIPAA compliant.

Best for: Enterprises that want model, agent, and tool governance unified in one self-hostable control plane.

2. Docker MCP Gateway — Best For Container-Native Development

Docker’s open-source MCP Gateway brings MCP into the workflow developers already know. It runs each MCP server as an isolated container with restricted privileges, network access, and resources, and it keeps secrets out of environment variables by mounting them into the target container only at runtime. It manages server catalogs (fork the official one or build your own), handles remote OAuth flows, verifies container image signatures for provenance, and can scan payloads for content that looks like leaked secrets.

For teams building and testing agents locally — or standardizing on containers — it’s a natural fit with strong isolation guarantees. It’s more a secure runtime for MCP servers than a full enterprise governance plane.

Best for: Developers and teams who live in Docker and want isolation and secret safety by default.

3. IBM ContextForge — Best For Open-Source Federation

IBM’s ContextForge is an open-source AI gateway, registry, and proxy that sits in front of MCP, A2A, and REST/gRPC APIs, exposing a unified endpoint with centralized discovery and management. It federates many MCP servers behind one gateway, translates REST and gRPC into MCP, and adds an agent gateway for A2A and OpenAI/Anthropic-style agent routing. You get built-in auth with user-scoped OAuth, rate limiting, retries, OpenTelemetry tracing, a plugin ecosystem, and an Admin UI — deployable via PyPI or Docker and scalable to multi-cluster Kubernetes with Redis-backed federation, including airgapped setups.

It’s a strong, vendor-neutral choice for teams that want to own an open-source federation layer across tools and agents. As with any self-run platform, the surrounding operations are yours.

Best for: Teams wanting an open-source gateway to federate MCP, agents, and legacy APIs.

4. Lasso Security MCP Gateway — Best For a Security-First Posture

Lasso approaches MCP from the security side. Its open-source gateway acts as a proxy and orchestrator that inspects every MCP interaction, with a security scanner that evaluates server reputation before loading, tool-description scanning that detects hidden instructions and tool-poisoning attempts, PII masking (via Presidio), token masking, and prompt-injection filters. A plugin architecture lets you add custom guardrails, and risky servers can be blocked automatically based on reputation scores.

If your primary concern is the threat surface MCP introduces — malicious servers, poisoned tool descriptions, data exfiltration — Lasso is built specifically for that. It’s often paired with a broader gateway rather than used as the sole control plane.

Best for: Security teams that want deep MCP threat detection and content sanitization.

5. Zuplo — Best For Enterprise MCP Governance at Scale

Zuplo offers a commercial MCP gateway focused on governance. It gives you a single control plane for the MCP servers you publish and the ones your teams consume, with an OAuth 2.0 authorization server, virtual MCP servers, RBAC, brokered credentials, and audit logs on every call. It federates remote MCP servers — your own, partners’, or third-party — behind one spec-compliant gateway, and enforces security policies including PII redaction and prompt-injection blocking. It carries SOC 2 Type II, SSO, multi-cloud and self-hosted deployment, and contractual SLAs.

For enterprises fighting shadow-MCP sprawl and needing governance without expanding the platform team, it’s a focused, well-rounded option.

Best for: Enterprises that want strong MCP federation and governance as a managed product.

Head-to-head comparison
The table reflects publicly documented functionality at the time of writing.

How to choose

Choose TrueFoundry if you want tool governance unified with model and agent governance in one VPC-deployable control plane. Pick Docker for container-native development with strong isolation. Go with IBM ContextForge for open-source federation across MCP, agents, and APIs. Use Lasso when MCP security is the top concern, and Zuplo for enterprise MCP governance as a managed product. Many teams pair a security-first tool with a broader control plane — the two aren’t mutually exclusive.

FAQ

Q: What is the best MCP gateway in 2026?

A: It depends on your priority. For unified model, agent, and tool governance in your own VPC, TrueFoundry is the most complete; Docker leads for container-native dev, IBM ContextForge for open-source federation, Lasso for MCP security, and Zuplo for enterprise governance as a product.

Q: What does an MCP gateway actually do?

A: It sits between your agents and MCP servers, centralizing which servers and tools are reachable, isolating credentials so agents never see secrets, applying guardrails and approval gates on tool calls, and logging every call for audit.

Q: Can an MCP gateway run in my own environment?

A: Yes — TrueFoundry, Docker, IBM ContextForge, and Lasso can be self-hosted, and Zuplo offers self-hosted deployment. TrueFoundry specifically runs fully in your VPC, hybrid, or air-gapped.
Related reading

MCP Gateway vs AI Gateway

What is an LLM Gateway?

AI Gateway Security & Governance

Conclusion
An MCP gateway is quickly becoming as essential as the AI gateway itself — it’s where agent actions get authorized, credentialed, and audited. The right pick depends on whether you want deep security, open-source federation, container isolation, or governance as a product. For teams that want all of it unified with their model and agent stack, TrueFoundry delivers the MCP Gateway inside one self-hostable control plane, which is why it tops this list.

Best AI Gateway in 2026: TrueFoundry, Vercel, Envoy, Portkey & Braintrust Compared

TrueFoundry — Thu, 02 Jul 2026 08:38:43 +0000

Picking an AI gateway used to be a developer’s afternoon decision. It isn’t anymore. Once a company has dozens of LLM-powered features and a growing fleet of agents, the gateway becomes the single layer that decides how reliably, how safely, and how cheaply every model call happens. Get it right and nobody notices. Get it wrong and you find out during an outage, a security review, or a finance meeting about a surprise bill.

The five platforms below — TrueFoundry, Vercel AI Gateway, Envoy AI Gateway, Portkey, and Braintrust — all clear the same baseline: they put many models behind one API. Where they part ways is who they were built for, and how far they go on governance, deployment, and the agent-era stuff like MCP. This guide is written for the people who actually have to sign off on the choice — platform leads, architects, and IT decision-makers evaluating a gateway for production. Everything here reflects each vendor’s public documentation at the time of writing, so verify anything time-sensitive before you commit.

The criteria that actually separate the contenders

Provider coverage and an OpenAI-compatible API don’t tell you much. Every serious gateway has both. The dimensions that decide whether a deployment ships and survives an audit are narrower:

Deployment flexibility. Can it run fully inside your VPC or on-prem, or is it only ever someone else’s SaaS?
Governance. RBAC, SSO, per-team budgets, rate limits, and audit logging that a compliance team will accept.
Guardrails. PII redaction, prompt-injection defense, and content moderation, applied in the request path — not bolted on later.
Agent and MCP support. Can it govern the tools agents call, or does it stop at chat completions?
Latency overhead. How much does it cost you in milliseconds for sitting in the hot path?
Surface area. One platform for routing, observability, and serving — or three tools you have to integrate yourself.

Keep these in mind as you read. Notice that “number of models” isn’t on the list. It rarely decides anything.

1. TrueFoundry — best for enterprise and agentic AI at scale

TrueFoundry is a full AI control plane, built for production rather than retrofitted onto a proxy. It puts 1,000+ LLMs behind one OpenAI-compatible API, and handles load balancing, automatic fallbacks, semantic caching, and batch jobs at the gateway itself. The performance numbers hold up under scrutiny: roughly 3 ms of gateway processing overhead, around 350 RPS on a single vCPU, and far higher with more replicas.

The real differentiator is breadth that doesn’t turn into sprawl. Governance is native — RBAC, scoped keys, per-team rate limits, hard budgets, and OpenTelemetry-based observability. Guardrails handle PII, prompt injection, and moderation, with integrations across Azure, AWS Bedrock, and policy engines like OPA and Cedar. And it ships a real MCP Gateway with tool-level access control and full audit trails, which matters more every month as agents spread. Crucially, the whole thing runs in your own VPC, hybrid, or air-gapped, so nothing leaves your domain, with support for SOC 2, HIPAA, and GDPR.

Best for: Regulated or large organizations running many LLM apps and agents that need governance, in-VPC deployment, and gateway plus model serving in one place. You can see the full picture on the TrueFoundry AI Gateway page.

2. Vercel AI Gateway — best for Vercel-native teams

Vercel’s gateway gives developers one endpoint to hundreds of models, with budgets, usage monitoring, load balancing, fallbacks, and Bring Your Own Key at no markup. It routes dynamically across providers based on uptime and latency, and it fits like a glove if you already live in the Vercel and AI SDK world. Pricing is pay-as-you-go on credits, with a small free monthly allotment to experiment.

It’s genuinely pleasant to use. The catch is that it’s a SaaS product running on Vercel’s cloud, which rules it out for teams that need in-VPC deployment or deep compliance controls.

Best for: Teams already building on Vercel who want fast, managed model access without standing up infrastructure.

3. Envoy AI Gateway — best for open-source, infra-heavy teams

Envoy AI Gateway hit v1.0 as an open-source gateway built on CNCF’s Envoy Gateway. You get a single OpenAI-compatible endpoint across 16 providers, an MCP gateway, multimodal support, token-based rate limiting, auth, and OpenTelemetry observability aligned with the Kubernetes Gateway API. A couple of the headline enterprise features — dollar-based cost control, deeper MCP authorization — are on the roadmap rather than shipping today.

For a team that wants to own and operate its gateway on Kubernetes, this is a strong, vendor-neutral foundation. The trade is that the surrounding governance is yours to assemble and maintain.

Best for: Platform teams standardized on Envoy and Kubernetes who are comfortable running it themselves.

4. Portkey — best for a fast, guarded start

Portkey routes to a very large catalog of LLMs with 40+ built-in guardrails, load balancing, virtual keys, request timeouts, and multimodal support. Its enterprise tier layers on governance, observability, and custom deployments, and it markets a tiny latency footprint and big daily token throughput.

If you want guardrails and observability quickly, it’s a great pick. Enterprises with strict in-VPC, agent-governance, or model-serving requirements should map those needs against what’s available before standardizing on it.

Best for: Startups and scaling teams that want a quick path to a guarded, observable gateway.

5. Braintrust — best for evaluation-driven teams

Braintrust is, first and foremost, an evaluation and observability platform that happens to include a gateway. The gateway unifies access across OpenAI, Anthropic, Google, Bedrock, and others, then feeds every request into Braintrust’s tracing, scoring (25+ built-in scorers), and experiments. It shines as the on-ramp to rigorous eval — not as a standalone governance layer.

Best for: Teams whose top priority is measuring and improving model quality with evals wired into CI/CD.

Head-to-head comparison

The table reflects publicly documented functionality at the time of writing.

How to choose

Reach for TrueFoundry if you’re running regulated or large-scale AI, need governance and VPC deployment, and want the gateway, MCP, and model serving under one roof. Pick Vercel if you live in its ecosystem. Choose Envoy if you want an open-source gateway you operate yourself. Go with Portkey for a fast, guarded start, and Braintrust if evaluation is the center of your world. None of these is wrong — they’re just built for different teams.

FAQ

Q: What is the best AI gateway in 2026?

A: It depends on what you’re optimizing for. For enterprises that need governance, in-VPC deployment, and agent/MCP support, TrueFoundry is the most complete option. Vercel, Envoy, Portkey, and Braintrust each lead for narrower, well-defined use cases.

Q: Which AI gateway can run in my own VPC?

A: TrueFoundry and Envoy AI Gateway both run fully self-hosted. TrueFoundry runs in your VPC, hybrid, or air-gapped with no data leaving your domain; Portkey offers self-hosting on its enterprise tier.

Q: Do these gateways support AI agents and MCP?

A: Coverage varies a lot. TrueFoundry ships a full MCP Gateway with tool-level access control today, Envoy includes a basic MCP gateway with more on the roadmap, and the others are more limited.

Conclusion

The best AI gateway isn’t the one with the longest model list — it’s the one that matches how you deploy, how you govern, and where your agents are headed. For teams scaling production AI under real compliance pressure, TrueFoundry packs the widest coverage into a single, self-hostable control plane, which is why it tops this list. If your requirements are simpler, one of the other four will likely serve you well — and that’s the point of comparing them honestly.

Why Organizations Need an AI Gateway

TrueFoundry — Tue, 30 Jun 2026 06:33:07 +0000

An AI gateway is the control point between your applications and the LLMs they call. It’s where cost, security, reliability, and governance get managed across every model and provider at once. Skip it, and AI sprawl quietly turns into runaway spend, security gaps, and outages you didn’t see coming. Here’s why a gateway has become core infrastructure.

Almost nobody adopts AI in a tidy, planned way. One team ships a support chatbot on OpenAI. Another prototypes on Anthropic. A third fine-tunes an open model on its own GPUs because the latency was better. A year later you’ve got dozens of applications, several providers, API keys scattered across repos, and no single answer to a simple question: what are we spending, and what data are we sending where?

That’s the gap an AI gateway fills. It sits between your applications and the models, and it turns fragmented, ungoverned access into something you can actually manage. The reason organizations end up needing one is straightforward — production AI creates problems that application code was never designed to solve. Let’s walk through them.

The problems an AI gateway solves

Cost that’s invisible until the invoice arrives

LLM spend is uniquely easy to blow up. A retry bug, an agent stuck in a loop, an unbounded batch job — any of these can multiply tokens overnight. And when every team holds its own provider key, finance gets one large number with no story behind it. A gateway changes that. It enforces budgets and rate limits per user, team, and application, tracks token spend as it happens, and attributes every dollar to a cost center. TrueFoundry, for instance, lets platform teams set hard caps so a single bad deploy can’t drain the AI budget. The detail matters because cost control only works if it’s enforced before the spend, not discovered after it.

Security and credential sprawl

Without a gateway, provider keys end up hardcoded in notebooks, committed to repos, and copied onto laptops. There’s no clean way to rotate them, no record of who used what, and no enforcement of safe usage. Centralizing access behind scoped keys fixes the credential problem, and it opens the door to the controls security teams expect: role-based access, SSO, and immutable audit logs. Sensitive data gets a second layer of protection from guardrails — PII redaction, prompt-injection detection, content moderation — applied before a prompt ever reaches a provider.

Reliability when providers wobble

Model providers go down, throttle, and slow to a crawl. The major ones logged repeated incidents through 2025, and latency swings widely by time of day, region, and model. If your app calls one provider directly, that provider’s bad afternoon is your bad afternoon. Route through a gateway and it can load-balance across models and fail over to a healthy one automatically. Your uptime stops depending on any single vendor’s status page.

Visibility you don’t otherwise have

When AI calls are scattered across services, nobody can answer the basics: which models are used, by whom, how often, at what cost, with what quality. A gateway captures every request as structured, OpenTelemetry-compliant traces and logs, and pipes them into whatever you already run — Grafana, Datadog, Prometheus. That visibility is the foundation everything else is built on. You can’t optimize, debug, or report on what you can’t see.

Lock-in and slow iteration

Hardwiring an app to one provider’s SDK makes switching models painful, which means you switch slowly, if at all. A gateway exposes a single OpenAI-compatible API across providers, so changing models is changing a name in the request — same URL, same credentials. New model drops on a Tuesday? You can canary it that afternoon. That flexibility also helps at the negotiating table; it’s easier to talk price when you’re not locked in.

Why this matters even more in the agent era

The case keeps getting stronger because AI isn’t just chat completions anymore. Agents now call tools, query databases, and act on live systems through the Model Context Protocol (MCP). Every MCP server you add widens the attack surface and scatters more credentials. A modern gateway extends governance to that layer too — deciding which agents and users can invoke which tools, holding tool credentials centrally, and running guardrails and approval gates before anything risky executes. TrueFoundry’s MCP Gateway gives tool-level access control and a full audit trail for every tool call, so the governance protecting your models also covers what your agents actually do.

What to look for in an AI gateway

Not every product covers the full scope, so it’s worth being specific about what you need:

A unified API across providers so apps stay model-agnostic.
Cost governance — budgets, rate limits, per-team attribution.
**Security **through RBAC, SSO, scoped keys, and audit logging.
**Guardrails **for PII, injection, and content safety in the request path.
**Reliability **via load balancing and automatic fallbacks.
**Observability **that plugs into your existing stack.
Deployment flexibility — the option to run in your own VPC when data residency demands it.
Agent and MCP governance for the workloads already arriving.
A gateway that only does routing leaves the hardest problems — governance, compliance, agent control — for you to solve some other way.

How TrueFoundry approaches it
TrueFoundry was built as a single control plane for exactly this set of needs. It unifies 1,000+ LLMs behind one OpenAI-compatible API, adds roughly 3 ms of gateway overhead, and handles 350+ RPS on a single vCPU. Governance, guardrails, observability, cost controls, and an MCP Gateway are built in rather than stitched on, and the whole platform can run inside your VPC, hybrid, or air-gapped, with support for SOC 2, HIPAA, and GDPR. If you’re consolidating scattered AI usage into something you can actually manage, that combination of breadth and deployment control is what the AI Gateway is for.

FAQ

*Q: Why do organizations need an AI gateway? *
A: Because production AI creates cost, security, reliability, and governance problems that application code can’t handle on its own. A gateway centralizes model access so you can control spend, protect data, keep apps online, and audit usage across every team and provider.

*Q: What’s the difference between an AI gateway and calling an LLM API directly? *
A: Calling an API directly ties each app to one provider with no central control. A gateway adds a managed layer for routing, budgets, guardrails, observability, and access control across all providers at once.

*Q: Can an AI gateway run in my own VPC or on-prem? *
A: Yes. TrueFoundry runs in your VPC, on-prem, air-gapped, or hybrid, so no data leaves your domain — which is exactly why regulated enterprises pick it over SaaS-only gateways.

Conclusion
An AI gateway has moved from nice-to-have to core infrastructure for one simple reason: it’s the only place you can manage cost, security, reliability, and governance across all your AI at once. As agents and MCP raise the stakes, that need only grows. If you’re weighing how to consolidate, it’s worth seeing how TrueFoundry’s AI Gateway handles all of it from a single control plane.

LLM Gateway vs MCP Gateway: Understanding the New AI Infrastructure Stack

TrueFoundry — Tue, 23 Jun 2026 06:03:25 +0000

As AI applications evolve from simple chatbots into autonomous agents, a new infrastructure layer is emerging. Terms like LLM Gateway, MCP Gateway, MCP Registry, LLM Router, and Agent Gateway are appearing everywhere—but what do they actually do?

Let's break it down.

The Challenge with Modern AI Systems

Early AI applications were simple:

Application → LLM

Today's enterprise AI systems are very different. A single AI agent may need to:

Access multiple LLM providers
Connect to GitHub, Slack, Jira, and internal APIs
Discover tools dynamically
Follow security and compliance policies
Track usage and costs

Without a centralized layer, managing these integrations quickly becomes messy and difficult to scale.

What Is an LLM Gateway?

An LLM Gateway provides a single entry point for all model interactions.

Instead of integrating separately with OpenAI, Anthropic, Gemini, or open-source models, applications connect to one gateway that handles:

Authentication
Rate limiting
Usage tracking
Cost monitoring
Security policies

For teams running multiple models, an LLM Gateway simplifies operations significantly.

If you're exploring production-grade AI infrastructure, TrueFoundry has a detailed guide on LLM Gateways:

👉 https://www.truefoundry.com/docs/gateway

Why LLM Routers Matter

Not every request needs the same model.

A coding task may require a different model than a customer-support query. An LLM Router automatically selects the most suitable model based on factors such as:

Cost
Latency
Performance
Availability

This helps organizations optimize both quality and spending.

Enter MCP: The Standard for AI Tools

The** Model Context Protocol (MCP)** is becoming the standard way for AI agents to interact with tools and external systems.

Instead of creating custom integrations for every service, developers can expose capabilities through MCP servers.

Examples include:

GitHub MCP Server
Slack MCP Server
Notion MCP Server
Internal enterprise tools

As MCP adoption grows, managing dozens or hundreds of MCP servers becomes a challenge.

What Is an MCP Gateway?

An MCP Gateway acts as a centralized access layer between agents and MCP servers.

It provides:

Unified authentication
Access control
Auditing
Observability
Governance

Rather than giving every agent direct access to every tool, organizations can enforce policies through a single gateway.

Learn more about MCP Gateway architecture here:

👉 https://www.truefoundry.com/blog/introducing-truefoundry-mcp-gateway

MCP Proxy vs MCP Gateway

These terms are often confused.

An MCP Proxy primarily forwards requests between agents and MCP servers while handling authentication and connectivity.

An MCP Gateway goes further by adding:

Governance
Monitoring
Policy enforcement
Access management
Registry integration

Think of a proxy as a connectivity layer and a gateway as a complete management layer.

MCP Registry, Agent Registry, and Skills Registry

As AI ecosystems grow, discovery becomes just as important as connectivity.

*MCP Registry
*
A centralized catalog of available MCP servers, including metadata, ownership, and versions.

*Agent Registry
*
A directory of deployed AI agents and their capabilities.

*Skills Registry
*
A searchable catalog of reusable skills, tools, and workflows that agents can access.

Together, these registries help organizations avoid duplication and improve governance.

*Final Thoughts
*
The future of enterprise AI isn't just about better models. It's about managing how models, agents, and tools work together.

That's why technologies such as **LLM Gateway, LLM Router, MCP Gateway, MCP Proxy, MCP Registry, Agent Gateway, Agent Registry, and Skills Registry **are becoming critical components of modern AI platforms.

As organizations scale from a handful of AI applications to hundreds of agents and tools, these infrastructure layers will become as important as API gateways are in traditional software systems.

AI Gateway vs API Gateway: Why Enterprises Need a New Layer for Agentic AI

TrueFoundry — Tue, 16 Jun 2026 06:34:53 +0000

Artificial Intelligence is changing how software is built. Organizations are no longer integrating a single LLM into their applications—they're managing multiple models, AI agents, MCP servers, and tool integrations across providers.

While traditional API Gateways have been a core component of modern architectures, they weren't designed for AI workloads. This is where an AI Gateway comes in.

The Problem with Direct LLM Integrations

Many teams start with a simple architecture:

Application → OpenAI API

As AI adoption grows, the architecture becomes more complex:

Application → OpenAI
Application → Anthropic
Application → Gemini
Application → Open Source Models

Soon teams face challenges such as:

API key management
Cost tracking
Rate limiting
Provider failovers
Security policies
Usage observability
Compliance requirements

Managing each provider separately becomes difficult and expensive.

What Is an AI Gateway?

An AI Gateway sits between your applications and AI providers, acting as a centralized control plane.

Instead of applications connecting directly to every model provider, requests are routed through a single gateway.

Architecture:

Application → AI Gateway → OpenAI
→ Anthropic
→ Gemini
→ Open Source Models

The gateway provides:

Authentication
Authorization
Rate limiting
Cost controls
Logging
Monitoring
Provider routing
Compliance controls

Learn more about Enterprise AI Gateway architecture:
https://www.truefoundry.com/ai-gateway

Why AI Governance Matters

As organizations deploy AI into production, governance becomes essential.

Questions every enterprise must answer:

Which models are approved?
Who can access them?
What prompts are being sent?
How much is each team spending?
What happens if a provider goes down?

Without centralized governance, AI systems become difficult to manage and audit.

The Rise of MCP Gateways

The Model Context Protocol (MCP) is becoming the standard for connecting AI agents to tools and enterprise systems.

As organizations deploy more AI agents, they need secure ways to:

Discover tools
Authenticate access
Control permissions
Monitor activity

An MCP Gateway provides this governance layer.

Learn more:
https://www.truefoundry.com/mcp-gateway

Multi-Provider AI Is the Future

Most enterprises won't rely on a single AI provider.

Different workloads require different models:

GPT models for general reasoning
Claude for long-context tasks
Gemini for multimodal workloads
Open-source models for privacy-sensitive environments

An AI Gateway allows organizations to switch providers without changing application code.

This reduces vendor lock-in while improving resilience and cost efficiency.

Building Future-Safe AI Infrastructure

The next generation of AI applications will be agentic, autonomous, and deeply integrated into enterprise workflows.

To support these workloads, organizations need:

AI Governance
Model Routing
Cost Optimization
Security Controls
Agent Management
MCP Integration

An AI Gateway provides the foundation for secure and scalable AI operations.

*Final Thoughts
*
As AI infrastructure becomes more complex, enterprises need more than direct model access. They need a centralized layer that provides visibility, governance, and control across models, tools, and agents.

Organizations investing in AI Gateways today are building a future-safe foundation for the next generation of AI applications.