Credits & usage
How SnitchFeed measures credits for scheduled scans, API searches, AI reply drafts, and AI scoring—and how to estimate monthly usage.
Your workspace has a credit balance for each billing period. Credits are spent when SnitchFeed runs metered work on your behalf:
- AI reply drafts (each generation from the mention Respond dialog)
- Scheduled keyword scans on X and LinkedIn
- Tracked Profile fetches on X and LinkedIn (guide)
- Optional API, MCP, and Tools lookups (keyword search, LinkedIn person posts, LinkedIn person comments)
- AI Fit Score and intent scoring for stored mentions
Tracked platforms explains which networks refresh on a schedule versus in real time. This page lists exact per-action costs as implemented in the product today.
Per-action credit costs
These amounts are charged when the corresponding job completes successfully.
| Activity | Credits | Notes |
|---|---|---|
| X (Twitter) keyword scan | 2 | One charge per included keyword each time that keyword’s scheduled scan runs for a listener. |
| LinkedIn keyword scan | 4 | One charge per search each time a keyword’s scheduled scan runs. A keyword with multiple author job title filters generates one search per job title. |
| X (Twitter) tracked profile fetch | 2 | One charge per page fetch when a Tracked Profile on X is polled (same listener scan interval). |
| LinkedIn tracked profile fetch | 4 | One charge per page fetch when a LinkedIn person or company Tracked Profile is polled. |
| X (Twitter) API search | 2 | Each API-backed search your workspace runs (for example from the product UI or integrations). |
| LinkedIn API search | 4 | Each API-backed keyword search your workspace runs (full page returned). |
| LinkedIn person posts | 10 | One charge per page for Tools (LinkedIn Profile Posts), POST /v1/data/linkedin/person/posts, or MCP get_linkedin_profile_posts. |
| LinkedIn person comments | 10 | One charge per page for Tools (LinkedIn Profile Comments), POST /v1/data/linkedin/person/comments, or MCP get_linkedin_profile_comments (comments the person wrote, not comments on a post). |
| AI mention scoring | 0.5 | Charged per mention when SnitchFeed runs AI scoring and stores the match (all supported platforms that use this pipeline). |
| AI reply draft | 5 | One charge each time you generate a draft from a mention’s Respond dialog (after you change approach, profile settings, or regenerate). Transforms on an existing draft do not charge extra credits. |
Reddit, Bluesky, and credits
Reddit and Bluesky are delivered on a real-time ingestion model (not per-keyword scheduled scans like X and LinkedIn). You are not charged the per-keyword scan rates above for those networks. You are still charged AI mention scoring (0.5 credits per mention) when a match is scored and stored the same way as other platforms.
Estimating scheduled scan usage (X and LinkedIn)
For one listener, over an approximate 30-day month:
credits ≈ (24 hours in a day ÷ scan interval in hours) × 30 days × keyword count × credits per scanScan interval in hours is the value you pick in the listener (for example 4 for “every 4 hours”). Use 2 credits per scan for X, and 4 for LinkedIn.
For LinkedIn keywords with author job title filters, each job title generates a separate search. A keyword with 2 job titles counts as 2 searches per scan interval:
LinkedIn credits ≈ (24 ÷ scan interval in hours) × 30 days × keyword count × job title count × 4If no job title filter is set, job title count is 1 (unchanged from the base formula).
Example: 5 keywords, X every 4 hours, LinkedIn every 12 hours, both platforms enabled, LinkedIn keywords each have 2 job title filters (e.g. CEO and developer):
- X: (24 ÷ 4) × 30 × 5 × 2 = 1,800 credits/month
- LinkedIn: (24 ÷ 12) × 30 × 5 × 2 × 4 = 2,400 credits/month
Tracked Profiles use the same scan interval as the listener, but charge per profile fetch instead of per keyword. For one profile:
credits ≈ (24 ÷ scan interval in hours) × 30 × credits per profile fetchUse 2 for X and 4 for LinkedIn. Multiply by the number of tracked profiles on that listener.
Actual totals depend on how many listeners you run, how many keywords and Tracked Profiles each has, which platforms are toggled on, each listener’s Scan every interval (presets from 1 hour up to 24 hours), and how many job title filters are set on LinkedIn keywords.
Usage history
In the app, open workspace Settings and review Credit usage to see a line-by-line history with sources such as polls, API search, AI reply drafts, and AI scoring.
Getting more credits
To get more credits, you can either upgrade your plan, or buy more credits.
For plan limits and pricing, see snitchfeed.com.