Guides

Do you need an AI gateway or AI observability?

Observability platforms watch your AI traffic; a gateway sits in the request path and decides. Which one you need depends on one question: do you need to see what happened, or prove and enforce what happens? Often the honest answer is both.

Two product categories now claim the "govern your AI" job, and buyers are routinely asked to choose between them without a clean definition of either. Here is the clean definition.

What each one is

AI observability (Langfuse, LangSmith, Splunk's agent observability, and similar) instruments your applications to collect traces, token counts, latencies, and costs, then gives you dashboards, evaluations, and alerts. It sits beside the request path. Its job is visibility.

An AI gateway sits in the request path: your applications call one base URL, and every request passes through routing, guardrails, and policy before it reaches a provider. Its job is decision and enforcement: which model, whether the prompt is safe to send, what the spend limit is, and, in RemKey's case, writing every decision to a signed, tamper-evident ledger as it happens.

The differences that actually matter

• Tense. Observability reports what happened. A gateway decides what happens. A blocked prompt injection, a redacted SSN, a request routed to a cheaper model: these are interventions, and only an in-path system can make them.
• Evidence vs. telemetry. Observability data lives in a mutable store and asks to be believed. For engineering questions that's fine. For an auditor's records-integrity question it structurally isn't: a log anyone with access can edit is testimony, not evidence. A hash-chained, signed export verified offline is the difference, and it can only be produced credibly by the system that made the decision.
• Coverage blind spot, shared. Both categories only see traffic that goes through or reports to them. The calls that bypass everything are the shadow AI problem, and neither a dashboard nor a gateway fixes it by existing; discovery does.
• Adoption cost. Observability means instrumenting applications with an SDK per service. A gateway is a base-URL swap; existing OpenAI and Anthropic client code is unchanged.

So which do you need?

Ask what question you have to answer. "Why is this agent slow, and which prompt version performs better?" is an observability question; buy observability. "Can you prove what your AI did, block what it shouldn't do, and control what it costs?" is a gateway question, and that is the job RemKey exists to do: be the AI layer you can prove to a regulator. One base-URL swap, and every call your teams make is screened fail-closed, routed to the cheapest capable model with the downgrade verified, and signed onto a hash-chained, Ed25519-signed ledger your auditor checks offline with a standalone tool, no trust in us required. Large estates often end up with a gateway and observability, and they compose cleanly: the gateway governs and produces the evidence, observability analyzes whatever else you instrument. What we would caution against is buying visibility and believing you bought control. Watching a thing is not governing it.