Turn YouTube comments into audience insight.
Extract a public video's comments as structured JSON, then ask focused questions about the feedback. Find what viewers liked, what confused them, what they want next, and where the strongest conversation is happening.
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The workflow
From raw comments to a sentiment breakdown.
FetchLayer gives you the public data in a predictable shape. Your code, automation, or AI agent can turn it into an ongoing source of customer insight.
- 01
Fetch the relevant conversation
Provide a public video URL and choose top or newest comments and pages based on the feedback you need.
- 02
Load reply threads on demand
Use the /comments/replies endpoint to pull full discussion threads for comments where the conversation matters most.
- 03
Ask for an actionable synthesis
Use an LLM or your own analysis to group feedback into themes, cite representative comments, and suggest the next action.
What to surface
The signal hiding in viewer reactions.
Audience reaction
Summarize the tone of the response while retaining the individual comments behind broad sentiment labels.
Repeated questions
Find questions that should become a follow-up video, documentation improvement, or a clearer product explanation.
Content requests
Surface the topics, comparisons, and examples viewers explicitly ask the creator to cover next.
Product feedback
When a video introduces a product, comments become public qualitative feedback about objections, gaps, and value.
Conversation context
Include nested replies where the actual insight comes from the discussion rather than the first comment alone.
Competitor research
Analyze public responses to competitor videos to understand what their audience praises, questions, or rejects.
AI-ready input
Hand your agent the comments — and the actual question.
Use FetchLayer through REST or MCP. The important part is keeping the original feedback alongside the summary so your team can inspect the evidence behind every conclusion.
Read these YouTube comments and replies. Group the audience feedback into praise, confusion, objections, and requests for future content. Cite the most representative comments for every theme.
Viewers understand the main idea but repeatedly ask for a practical setup tutorial. The strongest objection is uncertainty about pricing; make both themes easy for the creator to inspect in the source comments.
Pricing
Start free. Scale when ready.
Pay per request with no commitment, or lock in a flat monthly rate with built-in savings.
Free plan — 30 requests
Verify responses, inspect the API, no credit card required.
Pay as you go
$1.99
per 1,000 requests
$0.00199 per request · credits never expire
How credits work — no multipliers
One credit = one API call, on every endpoint and platform. No matter how many results come back, you pay for the call, not the output size. Most scraping APIs charge multipliers of 5x, 25x, even 75x per call depending on the target — here it's always 1.
- No multipliers — 1 request = 1 credit
- No monthly commitment
- Credits never expire
- Same API & MCP access as subscribers
- ∞ req/min, ∞ API keys
∞
Expiry
Credits stay until you use them. No monthly reset, no pressure to hit a quota.
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Commitments
No subscription, no contract, no minimums. Start, pause, or scale whenever.
100%
Access
Every endpoint and MCP included. Not a limited tier — same data as any subscriber.
FAQ
Common questions.
Can FetchLayer analyze YouTube comment sentiment?
FetchLayer retrieves structured public comment data. Send that data to your own model, analytics stack, or MCP-connected agent for sentiment, themes, questions, and qualitative analysis.
Should I analyze top or newest comments?
Use top comments to understand the most visible and engaged feedback. Use newest comments when you need a more current snapshot of how viewers are reacting.
Can I include comment replies in the analysis?
Yes. Use the /comments/replies endpoint with a comment ID to load a specific reply thread. Set the depth parameter to control how many levels of nested replies to include.
Start with the public feedback.
Retrieve the source data, keep the evidence, and build the feedback workflow your team actually needs.