TrueFoundry AI Gateway
5.0 (1)
OtherPaidUnify, govern, and scale GenAI with one OpenAI‑compatible gateway.
Last updated Jun 10, 2026
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What is TrueFoundry AI Gateway?
TrueFoundry AI Gateway's Top Features
Key capabilities that make TrueFoundry AI Gateway stand out.
Unified OpenAI-compatible API for chat, completion, embedding, and reranking
Access to 250+ LLMs (OpenAI, Claude, Gemini, Groq, Mistral, and more) via one endpoint
Centralized API key management, SSO, RBAC, and team/workspace authentication
Multi-model orchestration with intelligent routing, retries, and failover
Rate limiting, quotas, and policy enforcement per user/team/model
Input/output guardrails for toxicity, bias, hallucinations, PII, and policy violations
Comprehensive observability: requests, tokens, cost, latency by model/team/user/metadata
OpenTelemetry exports and integrations with Prometheus, Grafana, and Elastic
Audit logging and full request tracing for governance and debugging
Playground for experimentation, prompt templating, and code snippet generation
Native MCP support for agent workflows and secure enterprise tool access
Streaming and non-streaming support with tool/function calling and structured outputs
On-premise and hybrid deployments with data residency controls
Low-latency, in-memory decisioning using stateless gateway pods and NATS
High-scale performance (10B+ requests/month, multi-region, 99.99% uptime targets)
ClickHouse-backed analytics and Postgres for configuration/state
Compatibility with LangChain and popular OpenAI SDKs (no app code changes)
Cost and usage breakdowns by model, team, user, environment, or metadata
Fallback rules, caching, and health‑based routing for reliability
Compliance-ready posture (e.g., SOC 2, HIPAA, GDPR) and air‑gapped support
Use Cases
Who benefits most from this tool.
Platform teams
Provide a single secure endpoint and API key for all LLM providers across the company.
ML/AI engineers
A/B test and route across multiple models based on cost, latency, or output quality.
Application developers
Ship features faster using OpenAI-compatible SDKs, tool calling, and prompt templates.
Security/compliance
Enforce RBAC, quotas, audit logs, and guardrails for PII/toxicity across all LLM traffic.
SRE/DevOps
Ensure reliability with retries, failovers, caching, and real-time health-based routing.
FinOps/operations
Track and optimize spend with token and cost analytics by model, team, and user.
Data privacy teams
Deploy on-prem or in a controlled cloud for data residency and regulatory compliance.
Product managers
Experiment in a Playground, compare models, and standardize prompts and configurations.
Agent builders
Use MCP to connect enterprise tools (Slack, GitHub, Confluence) with policy controls.
Enterprises migrating vendors
Switch models/providers without app changes via the unified OpenAI-compatible interface.
Tags
LLM proxyunified access250+ modelsOpenAI-compatible APIcentralized key managementmulti-model routing/failoverguardrailsrate limitingobservabilityflexible deployment optionshigh-scalecompliant GenAI workloads
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Frequently Asked Questions
What is TrueFoundry AI Gateway?
It’s an enterprise-grade proxy between your apps and LLM providers/MCP servers, offering a unified OpenAI-compatible API to access 250+ models with built-in governance, security, and observability.
How does the AI Gateway work?
Your application calls a single gateway endpoint using one API key; the gateway routes to the configured model or tool, handling authentication, policies, retries, and failovers.
Which models and integrations are supported?
OpenAI, Anthropic (Claude), Google Gemini, Groq, Mistral, and 250+ LLMs; plus MCP servers for tools like Slack, GitHub, Confluence, and Datadog. Supports chat, completion, embedding, reranking, tool calling, and safety services.
What deployment options are available?
Fully managed SaaS, hybrid (data in your storage), or self-hosted on-prem/cloud with components like stateless gateway pods, NATS, Postgres, and ClickHouse for data residency and low latency.
What governance and security features are included?
Centralized API keys, SSO/RBAC, OAuth2 tool policies, rate limiting/quotas, input/output guardrails (toxicity, bias, PII, hallucinations), audit logging, and policy-based routing/fallbacks.
What observability and analytics does it provide?
Per-request metrics for requests, tokens, cost, and latency, broken down by model, team, user, and metadata; tracing via OpenTelemetry and exports to Prometheus, Grafana, Elastic; analytics stored in ClickHouse.
Does it support on-premise deployment?
Yes. On-prem delivers the same unified API with in-memory decisions for ultra-low latency and includes governance (access control, rate limits, guardrails, audit logs) without external calls.
Can it handle tool calling and agent workflows?
Yes. It supports native MCP for secure tool access with OAuth2/RBAC, and simulates function/tool calls with structured outputs and per-call policies.
What performance and scale can it handle?
Designed for high-scale production (10B+ requests/month), streaming/non-streaming, and multi-region; supports failover, retries, and intelligent routing to meet 99.99% uptime goals.
Is the API compatible with existing SDKs?
Yes. It’s OpenAI API–compatible, so you can use existing OpenAI clients and LangChain without code changes when switching models or providers.