JPL Close Approach vs TensorFeed
Same instrument, two spec sheets — measured, not claimed.
JPL Close Approach vs TensorFeed: common questions
Which is more reliable, JPL Close Approach or TensorFeed?
On our scheduled checks, TensorFeed leads on measured uptime — JPL Close Approach at 98.0% versus TensorFeed at 100% over 90 days. These are our own probe results, not provider claims; the uptime bars above show the day-by-day record for both.
Which is faster, JPL Close Approach or TensorFeed?
TensorFeed has the lower median latency in our checks — JPL Close Approach responds in 676 ms versus TensorFeed at 306 ms (P50). Tail latency (P95) is in the table above; for most workloads the median is the number that shapes how the API feels.
Do JPL Close Approach and TensorFeed need an API key?
Neither needs a paid key — JPL Close Approach is callable with no signup, and TensorFeed is callable with no signup. Both are quick to prototype with; rate limits still apply.
Can I call JPL Close Approach and TensorFeed from the browser?
Only TensorFeed is browser-friendly — it returns CORS headers over HTTPS. JPL Close Approach needs a server-side call or proxy, so factor that into which one fits a front-end project.
Are JPL Close Approach and TensorFeed free for commercial use?
JPL Close Approach has unclear commercial terms, and TensorFeed has unclear commercial terms. We track service terms and the data license as separate fields — see the Commercial use and Data license rows above, and confirm both before shipping either in a paid product.