TrueFoundry positions its Artificial Intelligence gateway as the core of an enterprise control plane for large language models, agents and tools, with a blog that spans implementation patterns, comparisons and case studies. Gartner recognizes TrueFoundry in the 2025 Gartner Market Guide for Artificial Intelligence Gateways, and the company emphasizes an “enterprise Artificial Intelligence control plane” that centralizes routing, security, observability and governance. Recent posts highlight how the TrueFoundry Artificial Intelligence Gateway integrates with MCP, OpenTelemetry and third party observability stacks to unlock multi‑agent workloads, while enforcing policies for access control, data residency and prompt management across heterogeneous environments.
The content focuses heavily on practical infrastructure guides for deploying and orchestrating Artificial Intelligence systems across major clouds and Kubernetes platforms. Technical deep dives cover how to architect large language models on Red Hat OpenShift, including solving Security Context Constraints and hybrid identity, and how to integrate the TrueFoundry control plane with AWS, GCP and Azure using services like EKS, GKE, AKS, Workload Identity Federation, Entra Workload ID, Spot VM orchestration and Azure OpenAI. Other articles describe multi‑cloud GPU orchestration that connects specialized providers such as CoreWeave and Lambda Labs, enabling a unified Kubernetes control plane to address compute scarcity for training and inference. Additional posts walk through on‑premise Artificial Intelligence gateway deployments, sovereign Artificial Intelligence on GovCloud and split‑plane architectures designed to keep customer data inside private VPCs.
Cost control, observability and vendor evaluation form another major theme. There are detailed pricing and capability breakdowns for LiteLLM, Portkey, Cloudflare Artificial Intelligence Gateway, Vercel Artificial Intelligence Gateway, Databricks Mosaic Artificial Intelligence Gateway, Amazon Bedrock, Amazon SageMaker, Azure Artificial Intelligence Gateway and Kong, alongside curated lists of alternatives and competitive landscape reviews. Multiple guides explain Artificial Intelligence cost observability, token tracking for tools like OpenCode, FinOps practices for Artificial Intelligence infrastructure, and how gateways provide request level budgeting, circuit breakers and chargebacks for both simple inference and complex multi‑agent systems. Specialized observability for voice Artificial Intelligence, Artificial Intelligence agents and production Artificial Intelligence gateways is presented as necessary to capture reasoning traces, tool calls, hallucinations and compliance signals beyond traditional APM logs.
Governance, security and compliance receive sustained attention, with posts on Artificial Intelligence governance concepts, the EU Artificial Intelligence Act, detecting shadow Artificial Intelligence, and building an ITAR compliant Artificial Intelligence gateway. TrueFoundry highlights its own milestones, including becoming the 1st Artificial Intelligence Gateway to announce ITAR compliance, as well as separate announcements around GDPR and FIPS aligned deployments on AWS and Azure Government. Articles on data residency and data sovereignty describe how the gateway enforces regional routing, per‑region inference, and separation of control and data planes to keep model artifacts, training data and logs in customer controlled regions. Additional content covers MCP access control, MCP authorization, MCP registries and service registries for the agentic era, outlining how the TrueFoundry MCP Gateway addresses the “N×M” integration problem, injects user level identity, and provides centralized tool governance for large fleets of agents.
Agentic Artificial Intelligence and multi‑agent systems are framed as the future of enterprise automation, and the blog provides a structured “Agent Gateway” series on how to build and operate these workflows. Topics include the seven pillars of the Agent Gateway, session management, identity, the A2A protocol for agent to agent communication, observability for non‑deterministic systems, a policy engine that secures intent and prevents privilege escalation, and FinOps techniques for autonomous agents that spend money with each reasoning step. Further posts explore Artificial Intelligence agent observability, Artificial Intelligence agent marketplaces, geographically distributed agentic Artificial Intelligence in banking, resilient web automation when APIs do not exist, and unifying the agentic stack so multi‑agent systems can collaborate across frameworks like LangChain, AutoGen and CrewAI. Throughout, TrueFoundry promotes capabilities such as truefailover for uninterrupted workflows, semantic caching for large language models, enterprise ready prompt evaluation via integrations like Promptfoo, and prompt management as a first class production discipline rather than ad hoc string handling.
The blog is anchored by case studies and interview formats that ground the infrastructure themes in real production usage. Case studies describe how Aviva centralized control, cost and velocity across multi‑cloud large language models using the Artificial Intelligence gateway, how a Fortune 50 and a Fortune 100 healthcare organization scaled agentic Artificial Intelligence and shipped 30+ large language model use cases in less than a year, and how Adopt Artificial Intelligence unified access to multi‑provider models to handle 15M+ requests and 40B+ input tokens. Other examples include NVIDIA improving GPU cluster utilization with large language model agents on TrueFoundry, Games 24×7 serving more than 200 requests per second while reducing deployment time, Wadhwani Artificial Intelligence scaling to reach millions of students at half the previous cost with 10X more scalability, and an online drug marketplace adding $1.5 million in revenue within a $200 Billion+ conglomerate. Regular “TrueML Talks” sessions and Tesseract conversations with industry leaders supplement these stories by discussing big data and machine learning practices, generative Artificial Intelligence adoption, and how Artificial Intelligence gateways are becoming a board level priority in enterprises.
