What is an MCP Gateway?

An MCP Gateway is a centralized control plane that securely manages access, discovery, and orchestration of MCP Servers across an enterprise. It acts as the operational backbone for agentic AI systems by enabling AI agents and applications to interface with enterprise tools via a standardized protocol. With support for authentication, RBAC, observability, and workflow execution, the MCP Gateway makes connecting and scaling intelligent systems seamless and secure.

What is an MCP Server and how does it work with the MCP Gateway?

An MCP Server (Model Context Protocol Server) is a standardized interface layer that wraps around enterprise APIs or tools, making them easily discoverable and callable by AI agents. When integrated with an MCP Gateway, each MCP Server registers itself, becomes accessible through a unified endpoint, and inherits enterprise-grade features like RBAC, federated authentication (via Okta, Azure AD), and observability—making orchestration across tools like Slack, Jira, or internal APIs effortless.

How do I build and deploy an MCP Server?

You can build an MCP Server using TrueFoundry’s SDK or your preferred backend stack. MCP Servers are containerized and typically deployed on Kubernetes or cloud-native infrastructure. Once live, they register with the MCP Gateway and are made available for secure discovery and task execution via agents or users—streamlining the AI integration pipeline.

What are the key features of an MCP Gateway?

The MCP Gateway provides unified access to all registered MCP Servers, instant discovery via a central registry, and secure access control with OAuth 2.0 and federated identity providers. It enables agentic task execution across tools, offers enterprise-grade observability with request-level tracing and audit logs, supports out-of-the-box and custom integrations (e.g., Slack, Datadog, internal APIs), and ensures high-performance operation across cloud, on-prem, and hybrid environments.

What are the benefits of using an MCP Gateway in enterprise environments?

There are various benefits of using an MCP Gateway in enterprise environments. It dramatically simplifies tool integrations, accelerates onboarding via prebuilt MCP Servers, and unifies security and compliance controls. It enables plug-and-play agentic workflows, supports distributed environments, and provides deep observability for cost and performance. The result is a scalable, secure, and maintainable AI system capable of handling real-time enterprise workloads with minimal engineering effort.

How does the MCP Gateway handle authorization and access control?

Authorization is enforced through Role-Based Access Control (RBAC) policies integrated with enterprise Identity Providers such as Okta or Azure AD. Each MCP Server, endpoint, or tool function can be governed by specific access rules, ensuring only authorized users or agents can trigger actions or retrieve sensitive data.

Can I use my existing SSO or IdP with the MCP Gateway?

Yes, the MCP Gateway and all MCP Servers fully support existing enterprise identity providers. Federated login via Okta, Azure AD, or custom SSO setups is supported out-of-the-box, enabling seamless integration into your organization's existing authentication and compliance stack.

What enterprise tools can I connect using MCP Servers?

You can integrate both standard and proprietary tools. MCP Gateway offers prebuilt MCP Servers for platforms like Slack, Confluence, Datadog, and Sentry. Additionally, you can create custom MCP Servers to connect any internal service, REST API, or data platform—extending orchestration across your unique tech stack.

How does MCP Gateway enable agentic task execution?

Through the MCP Gateway, AI agents can autonomously discover, authenticate, and call MCP Servers. This enables them to execute multi-step workflows (e.g., “create a Jira ticket from Slack messages”), generate and run code, or orchestrate tools—all governed by standardized interactions and enterprise policies.

What kind of observability does the MCP Gateway offer?

The MCP Gateway provides full visibility into every interaction with MCP Servers. It supports end-to-end tracing, metadata tagging (e.g., team, user, tool), and audit logging for compliance. Enterprises can monitor latency, usage, errors, and cost attribution in real-time—ensuring traceability and control across AI workloads.

Is the MCP Gateway secure and scalable for enterprise deployment?

Absolutely. The MCP Gateway is designed for production-grade deployments. It supports federated SSO, OAuth 2.0, dynamic discovery, multi-region failover, and role-based security—all while operating at high throughput under real-time enterprise load. It’s built to power large-scale, AI-first systems with confidence.

統合AI展開 – AIワークロードのデプロイ、スケール、運用を実現

TrueFoundryはSeldon AIの買収を発表し、エンタープライズAI向けコントロールプレーンを拡張します。プレスリリース全文はこちら→

LLM

オープンソースまたはプロプライエタリなLLMを、GPUアクセラレーションと本番環境レベルの信頼性でデプロイし、提供。

エージェント

メモリ、ツール実行機能を備え、AI GatewayおよびMCPサーバーとシームレスに統合された、長時間稼働するAIエージェントを実行。

MCPサーバー

ツール、API、エンタープライズシステムをAIエージェントに安全に公開するため、MCPサーバーをデプロイ。

ワークフロー

モデル、エージェント、サービスにわたる多段階のAIワークフローを、単一のコントロールプレーンからオーケストレーション。

ジョブ

バッチジョブ、トレーニングワークロード、スケジュールされたAIタスクをオンデマンドで実行。

従来のMLモデル

従来の機械学習モデルとLLMを同じプラットフォームでデプロイし、提供。

Purple gradient square with white background, shiny surface, and rounded corners in rhombus shape.

あらゆるAIワークロードをデプロイ

あらゆるAIワークロードを、一貫性のある単一のデプロイレイヤーで展開。

LLMやGPUベースの推論ワークロードを、vLLM、Triton、KServeなどのフレームワークやカスタムコンテナを使ってデプロイ
一貫したランタイムとネットワークでAIエージェントとエージェントサービスをデプロイする
ツールや内部システムを安全に公開するためにMCPサーバーをデプロイする
バッチジョブ、API、および長時間実行されるAIサービスを同じプラットフォームで実行する

AIワークロードの自動スケーリング

実際の需要に基づいてAIワークロードを自動的にスケーリングする。

リクエスト量に基づいて推論エンドポイントとエージェントサービスを自動的にスケーリングする
ピーク需要時にはGPUワークロードをスケールアップし、トラフィックが減少したらスケールダウンする
チャット、RAG、エージェント駆動型ワークフローなどのバースト性の高いワークロードをサポートする
トラフィックの急増時でも予測可能なパフォーマンスを維持する

コストを抑えるための自動シャットダウン

アイドル状態のAIインフラによる予算の浪費を防ぐ。

設定可能なアイドル期間後にエンドポイント、エージェント、またはサービスを自動的にシャットダウンする
オフピーク時や実験中のGPUの無駄を削減する
手動介入なしでオンデマンドにワークロードを再起動する
チームや環境全体でコスト規律を徹底する

MCP Gateway Tool Discovery for MCP servers

Unified Deployment Experience Across Cloud/Onprem

One developer experience across AWS, Azure, GCP, and on-prem - no cloud-specific tooling required.

Connect and manage AWS, Azure, GCP, and on-prem clusters from a single control plane
Deploy the same workload to different environments using identical workflows and APIs
Abstract away cloud-specific complexity while retaining full control and isolation
Use the same deployment experience across dev, staging, and production, regardless of infrastructure

Built for a First-Class Developer Experience

Build, deploy, and debug AI workloads with speed and confidence.

Integrated logs, metrics, and events for every deployment
Native monitoring and alerting to quickly detect and resolve issues
Production-ready deployment features like health checks and rollout strategies
Secure secret management and seamless CI/CD integrations

Works Seamlessly with AI Gateway & Agent Gateway

Deployment is the execution layer; governance lives
above it.

AI Gateway governs model access, routing, and cost controls
MCP Gateway governs tool access and execution
Agent Gateway orchestrates and governs agent workflows
Unified AI Deployments power the actual execution and infrastructure

Made for Real-World AI at Scale

99.99%

uptime

Centralized failovers, routing, and guardrails ensure your AI apps stay online, even when model providers don’t.

10B+

Requests processed/month

Scalable, high-throughput inference for production AI.

30%

Average cost optimization

Smart routing, batching, and budget controls reduce token waste.

エンタープライズ対応

データとモデルをクラウド/オンプレミスインフラ内に保持する、セキュアなAIゲートウェイを導入。

HIPAA, GDPR, and AICPA SOC compliance badges for data security and privacy regulations standards.

コンプライアンスとセキュリティ
SOC 2、HIPAA、GDPRの各標準により、堅牢なデータ保護を確実にする
ガバナンスとアクセス制御
SSOとロールベースアクセス制御（RBAC）および監査ログ
エンタープライズサポートと信頼性
SLAに基づいた応答SLAを含む24時間年中無休サポート

Deploy TrueFoundry in any environment

VPC, on-prem, air-gapped, or across multiple clouds.

No data leaves your domain. Enjoy complete sovereignty, isolation, and enterprise-grade compliance wherever TrueFoundry runs

Get Started

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.

Frequently asked questions

What types of AI workloads can I deploy with Unified AI Deployments?

Unified AI Deployments support a wide range of AI workloads, including GPU-backed LLM inference services, long-running AI agents, MCP servers, batch and scheduled jobs, workflows, and classical machine learning models. All workload types are deployed and managed using the same underlying platform, allowing teams to standardize how AI systems are built, scaled, and operated across environments.

Does Unified AI Deployments support autoscaling?

Yes. Unified AI Deployments provide built-in autoscaling for inference services, agents, and other AI workloads based on real-time traffic, request volume, and resource utilization. This enables workloads to scale up automatically during peak demand and scale down when usage drops, ensuring predictable performance without over-provisioning infrastructure.

How does auto-shutdown work for AI workloads?

Auto-shutdown allows AI workloads to automatically stop when they remain idle beyond a configured duration. This is especially useful for GPU-intensive services, internal tools, development environments, and experimental workloads. By shutting down unused resources automatically, teams can significantly reduce infrastructure costs while maintaining the ability to quickly restart workloads when needed.

Can I deploy AI workloads in my own environment?

Yes. Unified AI Deployments are designed to run in environments you control, including public cloud accounts, private VPCs, on-premise Kubernetes clusters, and fully air-gapped setups. Regardless of where workloads run, teams use the same deployment workflows, configuration patterns, and operational controls through the TrueFoundry platform.

How does Unified AI Deployments integrate with AI Gateway?

Unified AI Deployments focus on how AI workloads are built, deployed, and scaled, while the AI Gateway governs how those workloads are accessed and used. Deployed services can be securely exposed through the AI Gateway, which provides routing, authentication, authorization, observability, and agent-aware controls. Together, they form a complete production AI stack—from infrastructure execution to access and governance.