TrueFoundry is a cloud-agnostic enterprise AI platform for building, deploying, managing and monitoring AI applications, large language models, machine learning models, MCP servers and AI agents across an organization.

The platform provides a unified control layer between applications, users, AI models and enterprise tools. Development teams can access models from multiple providers, as well as internally hosted and open-source models, through a consistent API and governance framework without separately managing provider credentials, routing logic, access policies and monitoring systems for every project.

TrueFoundry also enables organizations to operationalize AI agents and MCP servers securely. It provides centralized authentication, granular authorization, end-to-end tracing and policy enforcement for agent actions and tool calls. Configurable guardrails can help detect or redact personally identifiable information, identify prompt injection attempts and enforce organizational content and safety policies.

TrueFoundry can be deployed as a managed service or within VPC, on-premises, hybrid, multi-cloud and air-gapped environments. With a self-hosted deployment, organizational data, models and artifacts can remain within infrastructure controlled by the organization.


Key Features:

• Unified AI Gateway: Connect hosted, open-source and self-hosted AI models through a single, consistent API while centralizing provider credentials, authentication and model access.
• Intelligent Routing, Load Balancing and Failover: Route requests based on latency, cost, geography or organizational policy. Distribute workloads across models and automatically fail over to alternative providers when errors or service disruptions occur.
• AI Governance and Access Control: Control access to models, endpoints, MCP servers and AI agents by user, team, application or environment through role-based access control, SSO, OAuth 2.0, JWT and API key authentication.
• MCP Gateway and Registry: Register, discover and manage internal and third-party MCP servers through a centralized registry, allowing AI agents to securely access only authorized enterprise tools and services.
• Agentic AI and Model Deployment: Deploy AI agents, large language models, embedding models, machine learning models and custom AI services using a broad range of agent frameworks and model-serving technologies, with autoscaling and CPU or GPU resource management.
• AI Guardrails and Content Safety: Apply configurable controls to model inputs and outputs, including personally identifiable information detection and redaction, content filtering, prompt injection detection and organization-specific safety policies.
• End-to-End Observability: Trace requests, prompts, responses, tool calls and agent execution steps while monitoring latency, error rates, token consumption, cost and infrastructure performance from centralized dashboards.
• Prompt Lifecycle Management: Create, store, version, test and manage prompts centrally, enabling teams to improve AI behavior systematically, maintain an audit history and roll back to previous configurations when required.
• Flexible Enterprise Deployment: Deploy as a managed service or within public cloud, VPC, on-premises, hybrid, multi-cloud or air-gapped environments to support organizational data residency, security and compliance requirements.