Judge the output before the feature list
The best scraper is the one that returns the fields your workflow can actually use. Start with two known channels and check channel identity, video URLs, timestamps, public metrics, profile links, and status fields before comparing scale claims.
Keep the first test intentionally small. A compact dataset makes missing fields, duplicate videos, Shorts-only channels, and source-specific limitations easier to recognize before a scheduled workflow creates hundreds of rows.
Choose for repeatable operation
For recurring research, hosted runs, logs, schedules, datasets, and API access matter as much as extraction coverage. A workflow that requires a local browser and manual export every week creates maintenance work even when the first run looks successful.
Apify tasks let you preserve a validated input, while datasets provide CSV and JSON exports plus API access. Confirm the current actor documentation and pricing on the public listing before committing to a larger cadence.
Compare cost with usable results
Do not compare tools only by the advertised price per run. Compare the cost of obtaining complete, deduplicated, reviewable records, including reruns, cleanup, engineering time, and storage or automation requirements.
Record the exact input, result limit, run status, row count, and fields used by the downstream workflow. That creates a fair benchmark between a managed actor, the official YouTube API, and custom browser automation.
Use public channel data responsibly
Limit collection to public information needed for a defined research purpose. Review source terms, applicable laws, and your own retention rules before storing or enriching creator information.
Treat scraped metrics as observations at a point in time, not permanent facts. Keep source URLs and collection timestamps so analysts can verify records and avoid making decisions from stale data.