Find what your audience wants next.
Public YouTube comments are a continuous stream of audience language, questions, objections, and ideas. Fetch the conversation from relevant videos, then use the evidence to shape content, product research, and market understanding.
Trusted by developers shipping real products.
231,390 served today
The workflow
From scattered comments to a content plan.
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
Select the right videos
Start with your own launches, tutorials, comparison videos, and competitor content where the comments contain useful audience discussion.
- 02
Retrieve the conversation
Use FetchLayer to collect comments, choosing the top or newest view that fits the research question. Load reply threads on demand for deeper discussion.
- 03
Create an evidence-backed brief
Group what viewers ask for and why, then retain representative comments so every recommendation can be checked.
What to surface
What audience language reveals about what to build next.
Questions worth answering
Find the explanations, tutorials, and comparisons viewers repeatedly request in their own words.
Next-content ideas
Turn recurring audience requests into follow-up videos, newsletter sections, FAQs, and product education.
Objections and friction
Surface the concerns that stop a viewer from trying a product, believing a claim, or following the recommended path.
Market language
Learn the phrases audiences use to describe their goals and pain points before writing campaigns or product copy.
Competitor gaps
Read competitor comment sections to see the unanswered needs and requests that their content leaves behind.
Qualitative evidence
Pair quantitative audience metrics with the actual language and reasoning viewers share publicly.
AI-ready input
Point your agent at the conversation, not a survey.
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.
Analyze the comments across these YouTube videos. What outcomes do viewers care about most, what questions remain unanswered, and which three follow-up topics have the strongest evidence?
The research points to a clear next topic: a beginner-friendly comparison with real setup examples. The summary should include the repeated questions and comments that support that recommendation, not just a generic topic list.
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.
0
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.
Who is YouTube audience research for?
Creators, marketing teams, product teams, researchers, and developers can use public comment data to understand the language, questions, and unmet needs their audience expresses.
Can I research a competitor’s audience?
Yes. Fetch comments from public competitor videos to understand the questions, frustrations, and content gaps visible in their audience conversation.
Can comments help plan new content?
Yes. Look for repeated questions, requested examples, objections, and adjacent topics. The useful output is not only a summary, but the original comment evidence behind the recommendation.
Start with the public feedback.
Retrieve the source data, keep the evidence, and build the feedback workflow your team actually needs.