Which Endpoint Should I Use?

With 381 endpoints across 48 platforms, a few of them do near-identical jobs. Picking the wrong one costs extra credits or returns less than you needed. Each table below answers one "these two look the same, which do I want?" question. Costs are per successful call; failed and cached responses are not charged.

A note on the numbers below. Most endpoints sit on the flat 1 / 5 / 10 tier ladder, and the figure in the Credit-cost column is what you pay. Some are metered: they hold a ceiling upfront and refund down to the work actually done, so their honest price is a range, and this page prints the range rather than the tier. See Endpoint pricing for every endpoint's min-max.

A profile, or a profile plus its posts?

profile is one upstream call that returns the unified author object. The populated fields vary by platform. Facebook supplies follower and page-like counts, but not following or total-post counts. profile/full is a composite: it returns that same profile, the account's recent posts, and computed analytics in a single call, so you skip the follow-up list request.

profile/full is available for /v1/tiktok/profile/full, /v1/instagram/profile/full, /v1/youtube/profile/full, /v1/twitter/profile/full, /v1/facebook/profile/full, and /v1/linkedin/profile/full. If you already hold the handle and only need counts, use plain profile. If your next step is always "and their recent posts", profile/full is one call instead of two.

Instagram: the 1-credit or the 5-credit endpoint?

Instagram is served by two upstreams. The 1-credit endpoints cover the public surface (profile, posts, reels, a single post, keyword and reel search). The 5-credit endpoints unlock data the public surface hides: share counts, follower and following lists, stories, tagged posts, locations, and comment lists.

The trap is /v1/instagram/post (1cr) versus /v1/instagram/post/stats (5cr): the cheap one returns everything about a post except the share count. Reach for post/stats only when you specifically need shares. Note also /v1/instagram/basic-profile (1cr), a slimmer profile payload than /v1/instagram/profile.

A raw list, or the enriched /full list?

For Instagram posts and reels you can pull a plain page or the enriched composite. The plain list is one fast, cheap page. The /full variant fans out per item to attach views, likes, comments, and per-item share counts where available.

If you page through a long back catalogue and do not need per-item share counts, the plain list is far cheaper. If you need engagement on every item and want one call, use /full.

The two meanings of limit

limit does two different jobs on this API, and on the /full composites it is the one that moves the price.

• Page size. On most list endpoints limit caps a single upstream page and maps to that upstream's native parameter (Naver display, GitHub per_page, and so on). One call, one page, one charge. This is the behaviour Pagination documents.
• Collect-until-N. On a small set of endpoints limit instead tells the endpoint to keep walking upstream pages itself until it has that many unique items, deduping server-side. You are billed per page or window actually consumed, and the unused budget is refunded. Omit limit and the budget is one page, so the price is unchanged.

The collect-until-N endpoints and their ceilings:

So /v1/instagram/profile/reels/full?handle=nike&limit=50 is five pages, not one, and costs 25 credits rather than 5. Budget from the range, not the floor.

Search one platform, the forums, or everywhere?

Six different search jobs, six different endpoints. Per-platform search is for when you already know the platform. The universal endpoint plans, fuses, and reranks across many platforms at once.

Start with per-platform search (1cr) when the platform is decided. Escalate to /v1/search/everywhere (flat 20cr) only when you genuinely want the cross-platform sweep, not fourteen separate 1cr calls you would have to fuse yourself.

The three Reddit endpoints are a ladder, not alternatives. /v1/reddit/search (1cr) is a plain site-wide post list. /v1/reddit/subreddit/search (1cr) narrows the same job to one community. /v1/reddit/omni-search (5cr floor) runs that search and expands the top threads' comment trees in parallel and returns per-subreddit volume with a tone label — so it is never the cheap option. Take it when you want voice-of-customer output rather than a list of links; the threads parameter is what moves it between 5 and 9 credits.

Source names on /v1/search/everywhere are not platform names, and unknown ones are dropped silently at full price — the X source is twitter-ai-search, not twitter. See the full source table.

Getting a transcript

Transcript pricing depends on the platform's upstream. YouTube is the cheapest and has two options; the ScrapeCreators-backed platforms are a flat 10cr; a composite bundles the transcript with the rest of a video's data.

For YouTube, prefer subtitles (1cr) if you can parse caption files yourself and transcript (3cr) if you want it cleaned. Other platforms only offer the 10cr transcript, for example /v1/instagram/media/transcript, /v1/twitter/tweet/transcript, /v1/reddit/post/transcript. If you also want the video's stats and top comments, /v1/prism/video-intel is one call.

Which comment endpoint?

Four endpoints touch comments and they are easy to mix up. The distinction is one page versus the whole tree, a single comment versus a batch re-check.

Use /v1/prism/comments when you want the complete thread without writing the pagination loop yourself — but read the range before you loop it. Its max parameter drives a page walk with a 200-credit hard cap, so "every comment on a post" on a very large thread is a genuinely expensive call. Use the per-platform post/comments when one page is enough and you want to control paging. Instagram is the exception on both: its comments run on a different upstream and bill a flat 5 credits per call rather than metering.

The two single-comment resolvers are a bounded server-side scan, not a fetch-by-id — neither Instagram nor TikTok exposes a read-one-comment route upstream. The scan stops the moment it finds the target, deep_scan=true widens the budget (and is the top of each range), and a not-found result is fully refunded.

Freshening numbers at scale (single vs batch)

When you already hold post or comment URLs and just want current numbers, the batch endpoints re-check many at once and refund the ones that fail, which is cheaper and simpler than looping single calls.

Hiring and job postings

Jobs live entirely on LinkedIn. The split is keyword search versus one company's board, and there is a cheap counter for when you only want the number.

If you are tracking hiring velocity across a competitive set, poll company/job-count (5cr) on a schedule and only spend the 10cr on company/jobs when the count moves. The company-scoped endpoints all key off the numeric company_id, which is not in the LinkedIn URL — resolve it once from /v1/linkedin/company?url=… (5cr) and cache it.

Ad libraries — which network, which door?

Each network's transparency library has a keyword door and an advertiser door. The keyword door is what you use when you only know a brand name; the advertiser door returns the complete run once you have resolved an id.

Reddit's ad library is currently unavailable: the two reddit/ads/* endpoints are soft-disabled and return 503 at no charge, so there is no live path to give you here. The Ad library aggregation recipe runs the three live networks as one parallel fan-out for 15 credits and records the Reddit gap in full.

App-store intelligence

The App Store and Google Play surfaces are deliberately symmetric — same nine resources, same prices, same parameter names — so one adapter covers both. Swap app_store for google_play and the call is otherwise identical.

For a cross-store view, skip the pairwise calls: /v1/prism/apps-lookup (30cr) resolves one title across both stores in a single composite, and /v1/prism/app-reviews (10-15cr, metered by how many stores you scope) returns reviews from both sides at once.

Commerce, product data, and reviews

Nine marketplaces, one repeating shape: a keyword search that returns ids, then a product call and a reviews call keyed off that id. What differs is which id each marketplace uses.

Two shortcuts worth knowing. /v1/prism/product-reviews (30cr) pulls reviews for one product across marketplaces in a single call instead of you fanning out, and /v1/prism/review-integrity (30cr) runs the same corpus through authenticity checks when the question is "are these reviews real?" rather than "what do they say?". For pricing across sellers on a single item, /v1/amazon/sellers (1cr), /v1/google_shopping/sellers (1cr), and /v1/walmart/offers (5cr) each return the offer list.

Which Prism composite answers my question?

Prism composites fan out across several platforms and return one answered question rather than one platform's rows. They are the most expensive endpoints on the API and the least interchangeable, so pick by the question, not by the price.

The metered ones scale with what you ask for: share-of-voice bills per brand in brands, ai-visibility per probe, org-radar per repository, creator-vet and creator-card per platform covered. Check the range in Endpoint pricing before you put one in a loop — ai-visibility in particular reaches four figures on a wide probe set.

Still unsure?

• Point a URL at /v1/prism/lookup (0cr) and it dispatches to the correct detail endpoint automatically.
• Browse the full catalogue in the API Reference, or see Credits for how the 1 / 5 / 10 tier ladder and refunds work.