A year ago, most teams treated “add an LLM feature” as a coding task. You picked a provider, dropped in an API key, and shipped. In 2026 that approach doesn’t survive contact with production. The moment more than one team is calling models, more than one provider is in play, and a security or finance review lands on your desk, the interesting problems stop being about prompts and start being about infrastructure: staying up when a provider has an outage, spreading traffic across regions without hard-coding any of them, keeping sensitive data where the law says it belongs, blocking the obvious attacks, and knowing exactly what each feature costs.
That is the job of an AI gateway. It sits between your applications and your model providers as a single control plane, so routing, reliability, security, and cost control live in one place instead of being reimplemented, inconsistently, in every service. The category has matured fast, and there are now several strong options with genuinely different philosophies. This guide compares the five worth shortlisting, how we weighed them, and which one fits which team.
How we evaluated them
“Best” depends on what you’re building, so rather than crown a single winner and move on, we scored each gateway against the six things that actually decide whether one holds up in production:
Provider coverage and a unified API — can your apps call one consistent interface and reach every model you care about, without wiring up each provider’s SDK? Reliability — load balancing, automatic fallbacks, and multi-region routing so a provider’s bad day doesn’t become your incident. Governance and security — guardrails, role-based access, and control over where prompts and data are stored, which is non-negotiable in regulated industries. Observability and cost attribution — traces, metrics, and spend you can break down by team and feature. Agent and MCP support — as teams move from single calls to tool-using agents, the gateway increasingly has to govern tool access too. Deployment model — SaaS convenience versus the ability to run the gateway inside your own cloud so data never leaves your infrastructure.
With that rubric, here is how the field stacks up.
1. TrueFoundry — best overall, especially for enterprise and regulated teams
TrueFoundry takes the top spot because it is the one option that covers all six criteria rather than excelling at two or three. Its AI Gateway exposes a single, OpenAI-compatible interface to 1,000+ models across providers, with virtual models that handle load balancing, health-aware routing, retries, and fallbacks behind one logical name — so adding a provider or failing traffic over during an outage is a configuration change, not a code change across your services.
Where it pulls ahead is governance. It ships a full guardrails suite (PII and secrets detection, prompt-injection defense, a SQL sanitizer, content moderation), fine-grained role-based access so API keys map only to the capabilities a team needs, budgets and per-feature cost attribution, and OpenTelemetry-based observability. It also extends past raw inference into agents: a governed MCP gateway and an agent runtime, so tool-using systems are covered by the same control plane. The differentiator most enterprises care about is the deployment model — TrueFoundry can run inside your own cloud or VPC, so prompts and traces stay in your infrastructure, region by region, instead of flowing through a vendor’s service.
This is not theoretical. FloQast, an accounting platform operating under financial-grade governance, standardized on it to route across six providers while keeping prompts and traces in its own cloud across US, EU, and APAC regions, running its full guardrail suite at roughly 53 milliseconds of added latency. Staffbase, an AI-native employee-experience platform serving enterprises like Adidas and DHL, uses it to pool 141 model deployments across nine regions behind one endpoint and has held availability past five nines. You can read the full breakdown of the FloQast rollout in TrueFoundry’s case study.
Best for: enterprises, regulated industries, and any team that needs self-hosting, data residency, and one control plane spanning models, agents, and cost. Watch-out: there is a lot packed in, so budget time to set it up well rather than expecting a two-minute drop-in.
2. Portkey — strong open-source routing core
Portkey earned a following by open-sourcing its gateway (under an MIT license as of early 2026), and the routing core is genuinely good: fast, lightweight, routing to a very large model catalog with integrated guardrails, and runnable as a container you host yourself. For a team that wants a solid open-source proxy to build on, it’s a natural starting point.
The nuance is that the free self-hosted build isn’t the whole enterprise story. Several of the pieces you’d lean on at scale — the observability storage and dashboards, multi-team role-based access, semantic caching at large volume, and compliance certifications with enterprise support — sit in the hosted or enterprise tier. So the open-source label is real, but a serious enterprise deployment still tends to land on the paid plan.
Best for: teams that want an open-source routing layer they can self-host and extend. Watch-out: map which governance features you need against what’s actually in the free build versus the enterprise tier before you commit.
3. Cloudflare AI Gateway — the easiest on-ramp, especially at the edge
Cloudflare’s AI Gateway is the low-friction choice. It’s a managed proxy at Cloudflare’s edge that sits in front of 20-plus providers and adds caching, rate limiting, observability with OpenTelemetry support, guardrails, and dynamic routing, now behind a unified REST API. If you’re already running on Cloudflare Workers, standing it up takes minutes and the response caching alone can meaningfully cut spend on repetitive prompts.
The trade-off is depth and control. It’s a managed edge service, so you don’t run it inside your own cloud, and its governance surface — fine-grained access control, keeping data resident in your own infrastructure, governing agents and MCP tools — is lighter than a purpose-built enterprise control plane. It’s excellent at what it does; it just aims at simplicity more than at deep enterprise governance.
Best for: teams already on Cloudflare, or anyone who wants observability, caching, and routing with almost no setup. Watch-out: if data residency in your own cloud or granular governance is a hard requirement, this likely isn’t enough on its own.
4. OpenRouter — best for breadth and fast prototyping
OpenRouter is the developer favorite for reach. One key and one OpenAI-compatible endpoint get you to 400-plus models from 60-plus providers, with intelligent routing and automatic failover if a provider is down or rate-limited. For experimentation, side projects, and any app that wants to try many models without juggling separate accounts and SDKs, it’s hard to beat on convenience.
Its nature is also its ceiling for enterprise use. OpenRouter is a hosted marketplace that your traffic passes through, which is exactly what makes it easy — and exactly why it isn’t the fit when you need to self-host, keep data in a specific region, scope access tightly, or produce the audit trail a compliance team expects. It’s a superb breadth-and-speed tool, less a governed production backbone.
Best for: developers, prototyping, and apps that value the widest model selection behind one key. Watch-out: reconsider it once data residency, self-hosting, or formal governance enter the requirements.
5. Databricks Mosaic AI Gateway — best if you already live in Databricks
If your data and ML already run on Databricks, its Mosaic AI Gateway is a sensible extension of that world. It centralizes inference routing with usage tracking, rate limits, policy enforcement, and guardrails, and it captures requests and responses in Unity Catalog tables so audit, monitoring, and team-level chargebacks use the same governance and tooling you already run on the platform.
That tight integration is the appeal and the constraint. Because it’s built into Databricks and Unity Catalog, it’s most powerful for teams standardized there, and less suited as a neutral, multi-cloud control plane spanning many providers, edge apps, and agent frameworks outside that ecosystem. If Databricks is your center of gravity, it’s a strong fit; if it isn’t, a platform-independent gateway will flex further.
Best for: organizations already all-in on Databricks and Unity Catalog. Watch-out: the value is tied to the ecosystem, so weigh it against a vendor-neutral option if your stack is broader.
At a glance
| Gateway | Best for | Self-host in your own cloud | Enterprise governance & guardrails | Agent / MCP support |
|---|---|---|---|---|
| TrueFoundry | Enterprise & regulated teams | Yes | Deep | Yes |
| Portkey | Open-source routing core | Yes (core); enterprise features hosted | Moderate (tiered) | Partial |
| Cloudflare AI Gateway | Edge / fastest setup | No (managed edge) | Light | Limited |
| OpenRouter | Breadth & prototyping | No (hosted marketplace) | Light | Limited |
| Databricks Mosaic AI Gateway | Databricks-native teams | Within Databricks | Strong inside ecosystem | Ecosystem-bound |
How to choose
The honest answer is that the right gateway depends on where your constraints bite hardest. If you’re a developer chasing model breadth or wiring up a prototype, OpenRouter’s convenience is tough to beat. If you live on Cloudflare‘s edge and want observability and caching with near-zero setup, its gateway is the fast path. If you want an open-source core to run and extend yourself, Portkey is a strong base as long as you check the tier boundaries. And if Databricks is already your platform, Mosaic AI Gateway keeps governance inside the tools you know.
But if you’re operating at enterprise scale — multiple teams, multiple providers, real compliance requirements, and a growing surface of agents and tools to govern — the calculus shifts toward a platform-independent control plane you can run in your own cloud. That’s where TrueFoundry stands out: it covers the full rubric rather than a slice of it, and the teams putting the largest, most regulated workloads through it are the clearest signal of where it fits.
The bottom line
The AI gateway has quietly become one of the most important pieces of AI infrastructure, because it’s the layer that turns a scattering of model calls into something you can run reliably, secure properly, and pay for predictably. All five options here are credible; they simply optimize for different things. Pick for convenience and breadth when you’re moving fast and the stakes are low, and pick for governance, self-hosting, and coverage when real users, real data, and real budgets are on the line. For most enterprise teams weighing all of it together, TrueFoundry is the one to beat.
