Today we're announcing a partnership between TrueFoundry and OpenTools designed to solve one of the hardest problems in enterprise AI deployment: how to move fast on new models and tools without losing the routing, observability, and governance that production systems require. OpenTools brings the discovery layer - the directory enterprises rely on to find, compare, and evaluate AI tools. TrueFoundry brings the AI gateway -the unified control plane that turns that discovery into reliable, governed production deployments across 250+ LLMs.
Together, we are closing the gap between "this model looks promising" and "this model is running in production for thousands of users."
The Enterprise AI Deployment Problem: Speed vs. Control
Every team building with AI today faces the same tension: the pace of innovation versus the discipline production demands.
On one side, the AI ecosystem is moving faster than any team can track. New models, new providers, and new tools ship every week - each one potentially better for a specific job than what teams are using today. Falling behind is not just a missed feature; it is a competitive risk. The teams that can evaluate and integrate new capabilities in days, not quarters, are the ones pulling ahead.
On the other side, enterprises cannot just drop a new model into a customer‑facing workflow. Production AI demands routing logic, cost controls, rate limits, fallback strategies, guardrails for safety and compliance, audit trails, and observability across every call. Without that operational layer, AI becomes a liability instead of an advantage - and a single bad output can outweigh months of progress.
Most teams end up choosing between the two: move fast and accept the risk, or move carefully and accept the lag. Neither is acceptable, and neither is necessary. This partnership exists to make that choice unnecessary.
What Is an AI Gateway - and Why Discovery Alone Isn't Enough
An AI gateway is the infrastructure layer that sits between your applications and the dozens of model providers, agent frameworks, and AI tools you depend on. Instead of integrating directly with OpenAI, Anthropic, Google, open‑source models, and every new provider separately, your applications talk to one unified endpoint. The gateway handles authentication, routing, retries, caching, rate limiting, observability, and policy enforcement - so your engineering team does not have to rebuild that plumbing every time the AI landscape shifts.
This is the gap discovery alone cannot close. Knowing which model is best for a job is necessary but not sufficient. Without an AI gateway, every new model you adopt means new credentials, new SDKs, new failure modes, new cost tracking, and a new surface area for compliance review. Multiply that across a dozen tools and the integration tax eats the productivity gains.
Here's how the two pieces fit together:
OpenTools is where AI builders go to figure out what to use. With detailed listings, side‑by‑side comparisons, and an audience of practitioners actively evaluating tools, OpenTools shortens the path from "we have a problem" to "here is the tool that solves it." It is the discovery layer enterprise AI has been missing - neutral, community‑informed, and continuously updated.
TrueFoundry's AI gateway is what makes those tools deployable. A single, unified LLM gateway to 250+ models across providers, with built‑in routing, semantic caching, guardrails, LLM observability, governance, and full on‑prem support for teams with strict data residency or regulatory requirements. It is the control plane that turns model access into production AI.
Discovery without deployment is research. Deployment without discovery is stagnation. Together, they are how enterprises actually move.
How LLM Routing Works
LLM routing is the part of the AI gateway that decides, for each request, which model should handle it. Most enterprise teams quickly discover that no single model is best at everything - and even if one were, costs, latency, rate limits, and availability would still vary by provider. Smart routing turns this from a problem into a competitive advantage.
A well‑designed routing layer can decide based on several signals:
- Use case. A simple classification task can go to a small, fast, cheap model. A complex reasoning step can go to a frontier model. The application does not need to know - the gateway handles it.
- Cost and latency budgets. Route to the cheapest model that meets the latency SLA for that endpoint, with automatic fallback to a more capable model if confidence is low.
- Provider health. When a provider degrades or rate‑limits, traffic automatically shifts to a backup with no application change required.
- Data residency. Route EU traffic to EU‑resident models, on‑prem traffic to self‑hosted models, and everything else to the optimal cloud option.
- Experimentation. Run A/B tests across models on live traffic, measure quality and cost differences, and graduate winners - all without redeploying applications.
With TrueFoundry, LLM routing is configured as policy, not code. Teams change models, providers, and routing logic without shipping new application versions, which collapses the time‑to‑test for any new capability the OpenTools community surfaces.
Why LLM Observability Matters for Enterprise AI
LLM observability is the ability to see - in real time and historically - what every model call in your system is doing: who made it, what it cost, how long it took, what was in the prompt, what came back, and whether it passed your safety and quality checks. It is the equivalent of APM for AI workloads, and it is non‑negotiable for any enterprise running models in production.
Without observability, three things break down quickly:
- Cost. AI usage scales non‑linearly with adoption. Without per‑team, per‑endpoint, per‑model cost visibility, finance cannot forecast and engineering cannot optimize.
- Quality. Models drift. Providers update silently. Prompt regressions sneak in. Without continuous evaluation against real traffic, quality issues surface in customer complaints rather than dashboards.
- Compliance. Audit, security, and legal teams need to know what data went to which provider, when, on whose behalf, and under what policy. That is only possible if every call is logged consistently.
TrueFoundry's AI gateway captures every request and response, tags it with cost, latency, user, and policy context, and exposes it through dashboards, alerts, and exports into the observability tools enterprises already use. The result is that AI workloads become as inspectable as any other production system.
Benefits of a Unified LLM Gateway for Enterprise Teams
For teams using OpenTools to evaluate AI providers, TrueFoundry now offers a clear next step: a single integration that gives access to every model under consideration - without rewiring the stack each time a new candidate shows up.
For TrueFoundry customers, OpenTools becomes the trusted starting point for finding the right model, agent framework, or AI tool for a given workload - backed by community signal and structured comparison, not vendor marketing.
The combined experience delivers:
- Faster adoption. Discover, test, and deploy new models in days, not quarters.
- Better visibility. Every call, every cost, every latency event observable in one place.
- Real flexibility. Switch providers, route by use case, or run fully on‑prem without changing application code.
- Reliable deployment. Guardrails, fallbacks, and governance built in - not bolted on.
- Lower integration tax. One gateway, one auth model, one observability surface for the entire AI stack.
The business outcome is straightforward: enterprises stop treating AI as a series of one‑off experiments and start treating it as infrastructure. That shift unlocks real things - procurement gets a single point of governance instead of dozens of vendor relationships, engineering stops rebuilding integration plumbing, product teams can match the right model to the right job and switch when something better arrives, and finance sees actual unit economics on AI usage. AI becomes a capability the business can plan around, not a moving target it has to keep up with.
Get Started
If you're evaluating AI infrastructure for your team, here's where to begin:
- Explore the TrueFoundry AI Gateway - truefoundry.com/ai‑gateway
- Book a demo to see LLM routing, guardrails, and observability in action
- Read more on what an LLM gateway is and how it compares to building integrations in‑house
- Discover AI tools that work with TrueFoundry on OpenTools
- Partner with us - if you're building in the AI tooling space, we'd love to talk
Enterprise AI does not have to be a choice between speed and discipline. With OpenTools and TrueFoundry, you get both.
FAQ: AI Gateways, LLM Routing, and Enterprise AI Deployment
What is an AI gateway?
An AI gateway is the infrastructure layer that sits between applications and the various LLM providers, agent frameworks, and AI tools an enterprise uses. It provides a unified API, handles authentication, routes requests across models, enforces guardrails and policies, caches responses, and captures observability data for every call. In practice, it is the difference between integrating with one endpoint and integrating with a dozen - and the difference between AI as experiment and AI as production infrastructure.
How does LLM routing work?
LLM routing decides which model should handle each incoming request based on signals like use case, cost and latency budget, provider health, data residency requirements, and experimentation rules. A good routing layer makes these decisions as configurable policy rather than hard‑coded logic, so teams can change models or providers without changing application code. TrueFoundry's AI gateway supports rule‑based, weighted, fallback, and quality‑based routing out of the box.
What is the difference between an LLM gateway and an AI gateway?
An LLM gateway focuses specifically on routing and managing calls to large language models. An AI gateway is broader - it covers LLMs but also extends to embedding models, image and audio models, agent frameworks, and AI tool integrations. In practice the terms are often used interchangeably, and TrueFoundry's platform covers both: unified access to 250+ LLMs plus the broader AI tool surface area.
Why do enterprises need LLM observability?
Enterprises need LLM observability for three reasons: cost, quality, and compliance. Without observability, AI workloads are essentially a black box - and black boxes do not ship in regulated industries.
Can an AI gateway be deployed on‑premises?
Yes. TrueFoundry's AI gateway supports full on‑prem and VPC deployments for organizations with strict data residency, security, or regulatory requirements. The same routing, observability, and governance capabilities are available whether the gateway runs in TrueFoundry's cloud, a customer's cloud account, or fully air‑gapped on‑premises infrastructure.