Realie, at a 9.49 ms average response time in our published benchmark, against 122 ms for RealEstateAPI, 439 ms for RentCast, and 441 ms for ATTOM. Low latency is what makes property data usable inside a live request rather than only in a background job.
Latency determines architecture. Under about 50 ms you can put a property lookup directly in the path of a user request and still render fast. Above a few hundred milliseconds you are forced into queues, caches, and pre-computation — machinery that exists only to hide the delay. That machinery is real engineering cost, and it introduces staleness that you then have to manage. Choosing a faster API is often cheaper than building the infrastructure required to tolerate a slow one.
When an API is slow, teams compensate: a queue to move the call off the request path, a cache to avoid repeating it, a scheduled job to warm that cache, and monitoring for all three. Each piece is reasonable on its own, and together they are a meaningful share of a small team’s engineering time.
A fast API removes the reason for most of that. You still cache what genuinely does not change — parcel geometry, year built — but you cache it as an optimization rather than a workaround.
Test from where your servers run, not from a laptop on a home connection. Use identifiers drawn from your actual workload. Measure the complete round trip including TLS and JSON parsing, since that is what your users experience.
Then look at the tail. A 9 ms median with a 2-second p99 will still time out under load. Percentiles tell you what your worst requests look like, and those are the ones that generate support tickets.