Validating a payload answers "is this instance right." Conformance testing answers a harder question: "does this system behave the way the spec says, across the flows that matter" — and the tools that answer it are not interchangeable, because each one proves something different.
Inferno (ONC-backed, open source, free) is the closest thing US interoperability has to "will this survive a real review." Its test kits drive your live server through the scenarios an IG demands — US Core, and the Da Vinci prior-auth guides among them — and the reports are the artifact reviewers actually recognize. One precision worth carrying: a suite passing tells you the tested assertions held, not that everything the IG says is true of your system. Our own CQL-or-flow explainer documents a concrete case — the payer-side suite checks that pre-population logic is present and well-formed, which is not the same claim as correct or executable. Read the test list before you read the green check.
Touchstone (AEGIS, commercial) is the other conformance platform you will meet, with its own TestScript-driven suites and certification programs. Point, don't host, applies to your budget too: know which program a partner means before agreeing that "Touchstone-tested" settles anything.
Synthea (MITRE, open source) is where test data comes from when you need volume: whole synthetic patient histories, generated, carrying no upstream license restriction. It solves a different problem from our Washer — Synthea invents patients from nothing; the Washer takes a payload you are already authorized to hold and re-mints its identifiers while keeping its shape. Volume versus shape; most real test plans want both.
Connectathons (HL7-run, several per year) are where implementations meet each other before they meet production. The Da Vinci tracks are the ones to watch for prior-auth work — many of the behaviors the IGs pin were argued out at a connectathon table first.
What ImOnFHIR does with all this: our workbench is for the payload-level question — what validation can prove, against exact IG versions, with findings that carry their evidence. When you need to prove your system to someone else, take Inferno's report, not ours: we are a learning lab, and saying so precisely is the point.