TrueFoundry’s AI Gateway is the production-ready, control plane to experiment with, monitor and govern your agents. Experiment with connecting all agent components together (Models, MCP, Guardrails, Prompts & Agents) in the playground. Maintain complete visibility over responses with traces and health metrics. Govern by setting up rules/limits on request volumes, cost, response content (Guardrails) and more. Being used in production for 1000s of agents by multiple F100 companies!
Hey Product Hunt, Anuraag here, co-founder at TrueFoundry 👋
When we first thought about a “gateway”, we imagined a simple LLM routing layer in front of models. Pick a model, send traffic, switch if needed. Easy… or so we thought.
Once teams started putting agents and MCPs into production, we realised the hard stuff wasn’t just about routing. It is:
Different MCP auth flows for every internal system.
Traces & logs that break once you chain models, tools, and agents
Data residency and “this data must stay in this region” rules,
Security asking “who called what, when, with which payload?”,
Product teams need to swap models without rewriting everything.
So the “router” slowly turned into a proper control plane that sits between your apps, LLMs, and MCPs - making sure traffic is reliable, auditable, compliant, and still fast for developers to ship on.
Today, TrueFoundry’s AI Gateway sits at the center of production traffic across 10+ Fortune 500s, powering their internal copilots and agents while platform teams use it to keep costs, safety, and observability under control - rather than maintaining a pile of custom glue code.
Congrats on the launch! This is definitely useful from a security and compliance standpoint
We’ve been testing the TrueFoundry AI Gateway for a few weeks, and it genuinely solved one of our biggest headaches — managing multiple LLM providers and internal models without drowning in glue code. The observability layer alone is worth it; being able to trace prompts, responses, latency, and failures in one place has saved us hours of debugging.
If you’re running production AI workloads or multiple teams rely on LLMs, this is a game-changer. If you’re just experimenting, it might be overkill — but for scaling, governance, and reliability, it’s one of the cleanest solutions we’ve tried.
🚀 Congrats on the launch! Love how TrueFoundry AI Gateway simplifies multi-model access, centralizes keys, and adds real observability + guardrails. Feels like the missing layer for taking AI apps from prototype to production. Great work!
I've been testing this for a while, and the observability layer is revolutionary on its own.
Having model latency breakdowns, token usage insights, agent traces, and failure cases all in a single interface saves a huge amount of time when debugging. This is exactly the kind of tooling that pushes LLM applications toward true, mature engineering systems.
Demo looks so good! This product is kind of complicated one for me. Demo helped me understand a little bit. I doesn't look like a marketer's tool, though.
This sounds so interesting. Wondering how it connects to/works with final deployed Copilots. Does it let you design workflows for the Copilot in addition to managing the MCP connections and base LLM?
Its a must to have an AI Gateway while building AI applications & TrueFoundry's AI Gateway has been getting strong traction lately. With security and governance becoming very challenging while building AI applications, looks like TrueFoundry is truly going to win the race here. Thanks for coming up with this amazing launch.
This solves a real problem. Congrats on the launch @agutgutia@nikunj_bajaj@deeptishukla I’m curious how TrueFoundry’s AI Gateway manages policy enforcement and observability when multiple agents and models are chained together, does it maintain full traceability through the entire workflow?
@TrueFoundry AI Gateway Unified control for LLMs and guardrails is crucial as AI deployments scale. The observability angle is key for production systems. How are teams using the MCP integration in practice?
Can developers set custom guardrails for different use cases or client requirements?
Curious about how the prompt management works across multiple models.
Love how this goes beyond simple routing – finally a serious control layer for production-grade AI stacks. Impressive work! 🔥
Congratulations on the launch!
Just wished to know if there were some mechanisms implemented which avoid serving cached outputs(stale responses)?
Looks like a comprehensive solution, can sure be very useful. Wish you success!
This is the missing glue for agent workflows. Love how everything sits under one control plane.
Interesting solution, especially the prompt management system! that's something hard to find and that works properly
Hey Product Hunt, Anuraag here, co-founder at TrueFoundry 👋
When we first thought about a “gateway”, we imagined a simple LLM routing layer in front of models. Pick a model, send traffic, switch if needed. Easy… or so we thought.
Once teams started putting agents and MCPs into production, we realised the hard stuff wasn’t just about routing. It is:
Different MCP auth flows for every internal system.
Traces & logs that break once you chain models, tools, and agents
Data residency and “this data must stay in this region” rules,
Security asking “who called what, when, with which payload?”,
Product teams need to swap models without rewriting everything.
So the “router” slowly turned into a proper control plane that sits between your apps, LLMs, and MCPs - making sure traffic is reliable, auditable, compliant, and still fast for developers to ship on.
Today, TrueFoundry’s AI Gateway sits at the center of production traffic across 10+ Fortune 500s, powering their internal copilots and agents while platform teams use it to keep costs, safety, and observability under control - rather than maintaining a pile of custom glue code.
🔗 Sign Up Link: Please try and give us feedback! 🙏
🎁 Launch perk: 3‑month free trial for the PH community
If you’re wrestling with MCP auth, logging, or data policies, drop your setup in the comments - curious to see how you are wiring your stack today!