4 Best YouTube Comments APIs and Scrapers in 2026

The “best” YouTube comments API depends on whether you need official owner capabilities, massive scraping infrastructure, a marketplace workflow, or simply a clean way to turn a video URL into feedback you can use.

For most research, product, and creator workflows, the real deliverable is not a page of comments. It is a ranked list of audience questions, pain points, objections, content requests, and source evidence that someone can act on. That is why the retrieval layer, the analysis layer, and the export layer all matter.

This comparison was last checked on July 31, 2026. FetchLayer is our product, so we state that plainly and include the cases where the other tools are better.

Quick answer: which YouTube comments API should you choose?

What a useful YouTube comments workflow actually needs

Video URL → public comments and replies → themes and evidence → CSV/JSON → decision or automation

Getting a comment array is only step one. You may still need to flatten replies, preserve parent context, deduplicate runs, classify the discussion, identify repeated questions, write a CSV, and explain the result to a non-technical teammate.

1. FetchLayer: best for turning comments into an insight quickly

FetchLayer’s YouTube Comments API takes a public video URL and returns structured comments. You choose top or newest and the number of pages to retrieve. Use the separate /comments/replies endpoint to load a specific comment thread, and /transcript to extract video transcripts.

It is built for the work that follows collection:

• Research a competitor’s audience without owning the channel.
• Monitor reaction to a launch video or announcement.
• Give an AI agent structured comments through MCP.
• Ask Research Chat to group questions, objections, praise, and follow-up content ideas.
• Download the comment-level analysis as CSV or JSON.

JavaScript and TypeScript developers can install the official @fetchlayer/youtube npm package instead of wiring raw HTTP. It is open source on GitHub.

For example:

Analyze the comments on this video. Find recurring questions, product objections, and requests for future content. Export the comments, theme, sentiment, and representative evidence as CSV.

The point is not to force every user to build an LLM pipeline. Developers can use REST or MCP; researchers and creators can use Chat and get a reusable file immediately.

And if your team already works in Claude, Codex, Hermes, or another MCP-compatible AI client, FetchLayer can be the data layer behind that agent. Connect MCP once, then have it retrieve comments and replies, analyze the evidence, and produce a CSV or research brief where your work already happens. Set up FetchLayer MCP.

Best for: audience research, product feedback, creator research, competitor analysis, AI agents, and teams that want usable output rather than scraper operations.

Analyze YouTube comments in Research Chat

2. YouTube Data API: best for official owner workflows

Google’s YouTube Data API is the official choice when you need the wider YouTube API surface. It supports comment-thread retrieval, authorized moderation actions, inserts, updates, and other platform capabilities that a public-data retrieval product should not claim to replace.

Where Google is stronger

• You own or manage the channel and need authorized moderation or posting actions.
• You need broad official YouTube resources beyond comments.
• Your team can operate a Google Cloud project and maintain the integration.

Where Google costs time and creates scale constraints

The direct API may be free within its allocation, but “free” is not the same as zero cost. Google requires a Google Account, a Google Developers Console project, API enablement, and OAuth 2.0 for user authorization where needed. Projects have a default quota allocation, and additional quota requires a compliance audit and extension request. See Google’s quota and audit requirements.

Comments also require implementation choices. Google’s documentation explains that commentThreads.list can include replies, but does not necessarily contain every reply; complete retrieval may require comments.list for particular threads. See Google’s comment implementation guide.

That is the right complexity for a first-party integration. It is unnecessary overhead if all you need is public feedback from a video URL, a useful analysis, and a downloadable file.

Best for: channel owners and product teams that need official, authorized YouTube capabilities.

3. Bright Data: best for enterprise-scale collection infrastructure

Bright Data’s YouTube scraper is built for high-volume data collection. Its product emphasizes bulk handling, proxy and unblock infrastructure, multiple output formats, webhook/API delivery, and broader enterprise data operations.

That can be a strong choice when you have a large, established data program: many URLs, delivery into cloud storage or a warehouse, strict operational requirements, and engineering resources to own the rest of the pipeline.

Where Bright Data is stronger

• High-volume and multi-URL collection.
• Enterprise-oriented delivery options and scraper infrastructure.
• Teams that need a wider scraping vendor beyond YouTube comments.

Where it is overkill for feedback research

Bright Data’s job is data delivery. You still need to decide what to collect, normalize the data, build the analysis layer, create exports, and present the insight to the team. That is sensible for a large data platform; it is a lot of surface area for “what are viewers asking for in this video?”

Bright Data publishes record-based pricing and a free monthly credit allowance, but its total cost should be evaluated on your actual volume, delivery method, and surrounding engineering. Review its current YouTube scraper details.

Best for: large-scale collection and enterprise data-delivery use cases.

4. Apify: best when your workflow already lives in the Actor ecosystem

Apify offers several community-published YouTube comment Actors. For example, its APIHQ YouTube Comments Scraper accepts video URLs or IDs, supports top/newest ordering and replies, and can run through Apify’s API, SDKs, schedules, webhooks, and integrations.

This makes Apify attractive if your company already has an Apify account, dataset workflows, and a process for selecting and monitoring marketplace Actors.

The marketplace trade-off

An Actor is not the same thing as a stable product API with one provider accountable for the whole capability. It is a separate third-party implementation inside a marketplace. Before making one production-critical, you should evaluate its release history, output schema, support expectations, limits, and pricing—not only its demo output.

Apify’s pricing can also combine platform usage with the Actor’s model. Apify documents pricing based on compute, storage, proxies, data transfer, and Actor-defined events or outputs. The company is retiring its rental model in 2026; remaining rental Actors are scheduled to migrate to pay-per-usage on October 1, 2026. Read the current Actor-pricing migration notice.

That does not mean Apify is a bad option. It means a team that needs predictable unit economics should model the actual Actor and workload, then plan for pricing and supplier changes rather than treating a marketplace listing as a fixed, long-term contract.

Where Apify is stronger

• You already use its datasets, schedules, webhooks, SDKs, and automations.
• You want access to a broad catalog of community Actors.
• You need specialized inputs or exports offered by a particular Actor.

Where FetchLayer is simpler

FetchLayer removes Actor selection from the public-comment workflow. You make a direct request, get structured comments and replies, use the same account across supported platforms, and can pass the output directly to MCP or Research Chat.

Best for: teams committed to the Apify ecosystem and comfortable owning Actor-level due diligence.

The salesy but practical recommendation

Use the official Google API when official authorization and channel-owner actions are the requirement.

Use Bright Data when you are operating a serious collection program and have the budget and engineering to build the analysis product on top.

Use Apify when its marketplace is already part of your stack and the specific Actor meets your reliability and cost requirements.

Use FetchLayer when you want to go from a public video URL to an answer, an agent workflow, or a downloadable CSV without building a scraper platform around the retrieval step.

Frequently asked questions

Is the official YouTube Data API free?

Google provides default quota rather than a simple unlimited free API. The practical cost includes Google Cloud setup, quota monitoring, implementation work, and the audit process if you need more quota. Check the current requirements in Google’s official quota documentation.

Which YouTube comments API is best for AI analysis?

FetchLayer is the shortest route if the output needs to be AI-ready. You can retrieve structured comments through REST or MCP, or use Research Chat to analyze the discussion and download CSV/JSON without writing the analysis pipeline yourself.

Can I export YouTube comments to CSV?

Yes. FetchLayer Research Chat can prepare the file directly. For an automated workflow, use the API and generate the CSV in your own application. See how to export YouTube comments to CSV.

Is FetchLayer affiliated with YouTube or Google?

No. FetchLayer retrieves publicly available data and is not affiliated with, endorsed by, or sponsored by YouTube or Google.

Get started

• Explore the YouTube Comments API
• Analyze YouTube comments with AI
• Research an audience from YouTube comments
• Export YouTube comments to CSV