TrueFoundry AI Gateway
AIA gateway for deploying, routing, governing and monitoring GenAI workloads with unified access, cost controls and observability.
-(0 Reviews)
Free Available
Starting from Free
Premium plans available
TrueFoundry AI Gateway
AI
A gateway for deploying, routing, governing and monitoring GenAI workloads with unified access, cost controls and observability.
-(0 Reviews)
Free Available
Starting from Free
Premium plans available
About TrueFoundry AI Gateway
TrueFoundry AI Gateway provides a centralized layer to route, monitor, secure and optimize requests to large language models and other AI services. It offers unified access control, policy enforcement, budgeting and observability across model providers so teams can run GenAI workloads reliably in production. The gateway exposes developer-friendly APIs and a UI for integration, enables request-level logging/metrics/tracing, and supports provider-agnostic model orchestration and cost-aware DevOps to reduce operational overhead and surface usage insights.
Screenshots
+
+
+
+
+
Key Features
Unified Access Control: Centralized authentication and role-based policy enforcement for model access and API usage across teams and environments, enabling consistent governance.
Cost-aware DevOps and Budgeting: Per-user and per-team budgeting, usage tracking and cost allocation tools to enforce spend limits and surface cost anomalies for GenAI workloads.
Provider-agnostic Model Routing: Route requests to multiple model providers or on-prem models via a single gateway layer, with configurable routing rules and fallback strategies.
Observability and Telemetry: Request-level logging, metrics, traces and dashboards that capture latency, token usage, error rates and model performance for troubleshooting and optimization.
Developer APIs and UI: RESTful APIs and an interface to integrate coding assistants, RAG pipelines and applications easily while exposing governance and telemetry controls.
Auditing and Compliance: Persistent audit logs of requests, model choices and policy decisions to support compliance, review and post-hoc analysis.
Request Orchestration and Enrichment: Support for common RAG workflows where inputs are embedded, retrievers queried, and final answers composed through the gateway with optional enrichment of metadata.
Unified access control and routing for model and assistant requests
Developer-friendly REST APIs and web UI for management and governance
Observability: request logging, metrics, tracing and feedback capture
Cost-aware DevOps: budgeting, usage tracking and cost controls per user/team
Integrations with RAG frameworks and retrieval workflows (embeddings, vector DBs)
Plugs into agentic deployments and MCP/FastAPI servers for production agents
Infrastructure automation support via Terraform and Kubernetes (EKS) modules
Documentation and example integrations (Cline, Cognita, Prisma AIRS guides)
Use Cases
Routing requests from coding assistants (e.g., in-editor tools) through a centralized gateway to apply access controls, budgeting and observability for developer-facing AI features.
Running RAG pipelines where user queries are embedded, vector DB retrievers are invoked and LLMs are called via the gateway to capture logs, metrics and feedback.
Enforcing enterprise governance and compliance by centralizing policy enforcement, audit trails and model selection across multiple teams and environments.
Cost control and chargeback for GenAI experiments by applying per-team budgets, usage limits and visibility into token/compute consumption.
Provider-agnostic deployment where applications can switch between cloud-hosted models and on-premise models without code changes by updating gateway routing.
Integrating security and policy scanning (e.g., Prisma AIRS) into AI workflows to enforce runtime checks and threat detection at the gateway layer.
Observability-driven optimization: analyze gateway telemetry to reduce latency, detect failing model providers and implement caching or fallback strategies.
Routing and governing LLM requests from coding assistants (e.g., Cline) with per-user budgeting and observability
Production RAG pipelines where embeddings/retrievers fetch documents and LLM calls are routed through a monitored gateway
Deploying and scaling agentic AI services behind a gateway with centralized access control and logging
Integrating security and policy enforcement into AI workflows via third-party integrations (e.g., Prisma AIRS)
Embedding TrueFoundry Gateway into microservices stacks using Python SDKs, FastAPI endpoints, or MCP servers
