An AI gateway is a single control point between your applications and the language models they call — one place to route requests across providers, cap spending, enforce guardrails, and log every prompt and response. Comparing gateways is therefore not a matter of counting features; it is a matter of testing six axes — routing, budgets, guardrails, observability, self-host versus SaaS, and sovereignty — against the way your organisation actually uses AI. Products in this category share a converging core and differ sharply in depth and operating model, which is why a week of running your own traffic through a shortlist tells you more than any feature grid.
The category exists because model sprawl arrived faster than control did. One team calls a frontier model with an API key pasted into a script; another runs a pilot on a second provider; a third is experimenting with an open-weight model on rented GPUs. Nobody can say what the monthly spend is, which prompts contain customer data, or what happens when a provider has an outage at month-end. A gateway turns that sprawl into one observable, governable pipe. Whether you need one is mostly a question of scale: two applications on one provider can live without it; ten teams across three providers cannot.
What does an AI gateway actually do?
Strip away the positioning and industry comparisons of the major products keep arriving at the same core: unified multi-model access, routing, budgets, guardrails, and observability. In practice that means five jobs.
- Unified access. One API surface in front of many providers, so an application can switch models without rewriting integration code.
- Routing and failover. Sending each request to the right model by cost, capability, or latency, and retrying elsewhere when a provider degrades. The logic and its trade-offs are covered in multi-model AI routing.
- Budgets and limits. Spending caps and rate limits per team, per application, or per key — the difference between an experiment and an invoice surprise.
- Guardrails. Policy applied to traffic in flight: redacting personal data before it leaves your network, blocking disallowed use, enforcing which teams may reach which models.
- Observability. Logs of who called what, with which prompt, at what cost, with what result — the raw material for both debugging and audit.
Because the core converges, the differences that matter live in emphasis: how deep each capability goes, and how the product is built and operated.
How do LiteLLM, TrueFoundry, and Kong differ?
Three names come up in most shortlists, and their durable positioning differs more than their feature lists do.
LiteLLM is the breadth-and-openness option. Industry comparisons credit it with the widest provider coverage in the category and note that it is free to self-host, which makes it the default starting point for engineering-led teams. The recurring caveat in those same comparisons is operational: its configuration approach is said to scale poorly as teams, keys, and policies multiply — manageable for one platform team, harder across a whole organisation.
TrueFoundry positions itself as an enterprise AI control plane rather than a router: the emphasis in its public positioning is compliance, data residency, and governance, offered self-hosted or as SaaS. It is aimed at buyers whose first question is "where does the data go and who signs off," not "how many providers do you support."
Kong comes at the problem from the opposite direction: a mature, traditional API gateway extended to handle LLM traffic. That heritage brings real strengths in conventional API management, but industry comparisons note the retrofit lacks token- and prompt-level awareness — it treats an LLM call as another HTTP request, when the things you need to govern (tokens, prompts, model choice, cost per call) are specific to AI traffic.
One more signal worth reading: Portkey, an India-origin gateway, was acquired by Palo Alto Networks in May 2026. Per industry reporting, the category is consolidating into security vendors — which means the independence, roadmap, and pricing of any gateway you pick may change under you. That is an evaluation question, not a disqualifier: ask every vendor what happens to self-hosted deployments, contracts, and data if they are acquired.
None of this ranks the three, and it should not. Positioning shifts, features converge, and the right answer depends on whether your binding constraint is provider breadth, governance depth, or fit with existing API infrastructure. Treat the descriptions above as hypotheses to test against your own traffic, not verdicts.
Should you self-host your AI gateway or use SaaS?
This is the axis buyers most often decide on instinct and regret on evidence. The honest trade-off:
| Question | Self-hosted | SaaS / managed |
|---|---|---|
| Who sees your prompts and logs? | Only you | The vendor's infrastructure |
| Who operates and upgrades it? | Your engineers | The vendor |
| Time to first value | Days to weeks | Hours |
| Fits residency mandates? | Yes, by construction | Only if the vendor offers in-region hosting |
| Real cost | Engineering hours, on-call | Subscription, per-request fees |
A gateway sits in the critical path of every AI feature you ship. Self-hosting puts a new production system on your platform team's pager; SaaS puts your prompt traffic — often your most sensitive data flow — through someone else's infrastructure. Neither is wrong. What is wrong is choosing self-host to "save money" without costing the engineering, or choosing SaaS without checking where the logs physically live.
Why sovereignty is becoming a first-order axis
For buyers in India and the Gulf, the gateway question increasingly starts with jurisdiction rather than features. Prompts routinely carry personal and commercial data, so the pipe they travel through falls squarely under India's DPDP Act and Saudi Arabia's PDPL, and public-sector or regulated work may require that traffic, logs, and the control plane itself stay in-country. A gateway that only runs as US-hosted SaaS fails that test before the feature comparison begins. The deployment options — and what "sovereign" actually requires beyond a local data centre — are worked through in private vs sovereign AI deployment.
Sovereignty is also where the gateway conversation merges with governance. A gateway is the natural enforcement point for AI policy — the place where "we redact PII" and "marketing may not use the expensive model" stop being memos and become configuration. If you are building towards that, evaluate the gateway as part of an AI governance control plane, not as a standalone router.
How to evaluate an AI gateway before you commit
A practical sequence that fits inside two weeks:
- Route real traffic, not a demo. Put one genuine application behind each shortlisted gateway. Synthetic benchmarks hide the integration friction that dominates real cost.
- Break something deliberately. Simulate a provider outage and watch the failover. Exhaust a budget cap and see what actually gets blocked, and how loudly.
- Test configuration at your real scale. Set up ten teams, ten keys, and three policies — not one of each. This is where the "scales poorly" caveats either materialise or don't.
- Trace one request end to end. From application to log to invoice. If you cannot reconstruct who spent what on which prompt, the observability is decorative.
- Check the exit. Export your logs and configuration and estimate honestly what moving to another gateway would cost. A gateway should reduce provider lock-in, not become a deeper layer of it.
The discipline is the same one that applies to any AI infrastructure purchase — including the parallel exercise of shortlisting intelligent document processing companies: converging feature lists, diverging depth, and no substitute for testing on your own workload.
The axis weighting is yours. An engineering-led firm with light compliance needs will weight breadth and cost; a regulated firm in Riyadh or Mumbai will weight residency and guardrails and be right to pay for them. What no buyer should do is pick on feature count. The features converge. The depth, the operating model, and the jurisdiction do not.