Product Introduction
TrueFoundry AI Gateway is a production-ready control plane designed to manage, monitor, and govern agentic AI applications at enterprise scale. It provides a unified interface to connect and orchestrate critical components including models, MCP (Model Control Plane) tools, guardrails, prompts, and agents within a single infrastructure layer. The solution standardizes access to 250+ LLMs through one consistent API while enabling centralized policy enforcement and real-time observability. Enterprises deploy it to streamline AI operations across development and production environments while maintaining strict security compliance.
The core value lies in consolidating fragmented AI infrastructure into a secure, observable, and governed control plane that reduces operational overhead. It eliminates redundant integrations by providing a single gateway for all LLM providers, tool integrations, and safety mechanisms while enforcing enterprise-grade governance through RBAC, quotas, and guardrails. The platform delivers production reliability with sub-3ms latency, automated failovers, and 99.99% uptime even during third-party model outages. This enables organizations to accelerate AI deployment while controlling costs, ensuring compliance, and maintaining visibility across thousands of agents.
Main Features
Unified LLM API Access integrates 250+ models (OpenAI, Claude, Gemini, etc.) through one standardized API endpoint, eliminating provider-specific SDKs and credential management. Developers access chat, completion, embedding, and reranking models via consistent interfaces while administrators centralize API key management and authentication. The gateway supports seamless switching between cloud-hosted and self-hosted models like LLaMA or Mistral without code changes. This simplifies multi-model workflows and reduces integration complexity across AI applications.
Granular Observability provides real-time monitoring of token usage, latency, error rates, and request volumes with full request/response logging. Teams tag traffic with custom metadata (user ID, environment, team) to analyze costs, performance, and errors across dimensions like geography or model type. The system captures detailed traces for agentic workflows, showing tool calls, intermediate steps, and guardrail interventions. Metrics export via APIs enables integration with existing monitoring stacks for unified visibility into AI operations.
Policy Enforcement Engine enables governance through configurable rate limits, token-based quotas, and RBAC applied across users, models, or endpoints. Administrators set cost budgets that automatically throttle or downgrade requests when thresholds are exceeded and define geo-aware routing rules for compliance. Guardrails enforce PII filtering, toxicity detection, and custom safety checks via integrations with OpenAI Moderation or Azure Content Safety. These policies execute consistently across all AI traffic without application-level changes.
Problems Solved
The gateway addresses infrastructure fragmentation where teams manage separate integrations for each LLM provider, monitoring tool, and security system, leading to inconsistent governance and operational bottlenecks. It solves visibility gaps in token costs, model performance, and error rates that complicate troubleshooting and budget control. By centralizing policy enforcement, it prevents unauthorized model access, prompt injections, and non-compliant data handling that create security risks in distributed AI deployments.
Primary users include enterprise platform engineers managing AI infrastructure, DevOps teams responsible for production reliability, and security/compliance officers in regulated industries. Data science leaders use it to standardize model access across teams while IT directors enforce cost controls and governance. The solution specifically targets Fortune 500 companies deploying agents for customer-facing applications where uptime, latency, and data sovereignty are critical.
Use cases include deploying secure chatbots with real-time guardrails for PII redaction, multi-model RAG systems using weighted routing to optimize cost/latency, and agentic workflows integrating Slack/GitHub via MCP with audit trails. Enterprises implement it for geo-fenced AI deployments meeting regional data laws, burst-traffic handling for seasonal workloads, and air-gapped installations in government/healthcare environments. It also enables developer self-service through templated prompts and pre-approved model configurations.
Unique Advantages
Unlike generic API gateways, TrueFoundry specializes in agentic AI with native MCP integration, tool orchestration, and guardrail enforcement baked into the data plane. It outperforms cloud-specific tools (SageMaker, Bedrock) by supporting hybrid deployments across on-prem, multi-cloud, and air-gapped environments without data egress. The platform uniquely combines model gateway capabilities with full agent lifecycle management—experimentation via Playground, version control for prompts/tools, and production monitoring—unavailable in point solutions like Portkey.
Key innovations include the Playground UI for visually testing prompts/models/MCP tools while generating production-ready code snippets and reusable templates. The geo-aware routing engine automatically falls back to secondary models during outages and optimizes latency through continuous performance monitoring. Custom guardrails operate bidirectionally, scanning inputs for injection attacks and outputs for policy violations using both prebuilt and Python-coded rules. Helm-based management of self-hosted models integrates vLLM/Triton with zero SDK changes.
Competitive strengths include proven scalability (10B+ monthly requests), sub-3ms latency for real-time applications, and 30% average cost reduction via smart routing/batching. The architecture guarantees no data leaves customer environments with VPC/on-prem deployment options and certifications like SOC 2/HIPAA. Enterprise features include 24/7 SLA-backed support, centralized audit logs, and RBAC that simplify governance for large organizations compared to open-source alternatives.
Frequently Asked Questions (FAQ)
How does the TrueFoundry AI Gateway Playground help developers build and test? The Playground provides an interactive UI to experiment with LLMs, prompts, MCP tools, and guardrails without writing code, allowing parameter adjustments (temperature, tokens) and instant response analysis. Developers save fully configured setups as reusable templates with version history and generate production-ready code snippets for OpenAI/LangChain integrations. This accelerates prototyping while ensuring experimental configurations transition smoothly to production via the same gateway API.
What does "unified access" mean for APIs, keys, tools and agents? Unified access consolidates all model providers, MCP tools, and agents behind one API endpoint using a single authentication key instead of managing separate credentials. The gateway routes requests dynamically to configured providers (OpenAI, Anthropic, self-hosted models) while applying consistent policies and logging. This extends to agent-to-agent (A2A) communication patterns, allowing standardized governance across all AI components through RBAC and quota systems regardless of underlying infrastructure.
How do guardrails, safety checks and PII controls work end-to-end? Guardrails execute pre-inference input scans for sensitive data (PII, prompt injections) using services like Azure PII detection, blocking or redacting violations before reaching models. Post-inference output checks evaluate responses for toxicity, hallucinations, or policy breaches via customizable rules. The system supports layered defenses with third-party tools (OpenAI Moderation) and Python-coded logic, all centrally managed to enforce compliance across every model and application without per-team implementation.
