How do I scrape Reddit comments at scale without running my own scraper?

Why a Reddit scraper gets rate limited, and what actually removes the limit

Rate limiting is the symptom people name, and it is usually not the thing that kills the project. A scraper has four independent failure surfaces, and a team typically discovers them in order, one painful week apart. Adding workers makes the first one worse rather than better, because the budget is attached to your client identity rather than to your process count. The useful question is not how to go faster through the throttle, it is which of the four you are willing to keep owning.

We measured that ceiling rather than rounding it: a single listing returned 983 items through this API against 940 on Reddit's own logged-in web client (source: the same measurement published on our Pushshift alternative page). That gap matters more than it looks. It means a hosted API is reading the same surface a human sees rather than a privileged one, so any provider claiming an uncapped single feed is either paging a cache or describing something Reddit does not serve. Plan for breadth: many narrow queries across subreddits, keywords and time windows, each returning its own listing, rather than one query you try to page forever.

The comment endpoints that replace the crawler

Comments are the harder half of Reddit data and the half most providers treat as an afterthought. A post is a single object with stable fields. A comment lives inside a tree, can be edited or removed after you read it, and is only findable through a search index or by walking down from the post that holds it. A provider that returns posts well and comments badly will pass a demo and fail the first real collection run, so it is worth checking which of these five jobs your provider actually exposes as separate calls.

Prices read from this site's own pricing constants, so the table cannot drift from the rate card. Request and response shapes are in the API reference.

Using a Reddit scraper API from Python

The Python answer is deliberately boring: the requests library and a bearer token. There is no SDK to install, no OAuth exchange to implement and no PRAW configuration file, because the authentication is a header rather than a handshake. That matters for collection work specifically, since the thing you want to parallelise is a plain HTTP call that any worker pool already knows how to run.

import requests

BASE = "https://api.redditapis.com"
HEADERS = {"Authorization": "Bearer YOUR_API_KEY"}

def comments(query, subreddit=None, limit=100, after=None):
    params = {"q": query, "limit": limit}
    if subreddit:
        params["subreddit"] = subreddit
    if after:
        params["after"] = after
    r = requests.get(f"{BASE}/search/comments",
                     headers=HEADERS, params=params, timeout=30)
    r.raise_for_status()
    return r.json()

# Breadth, not depth: one narrow listing per subreddit rather than
# one broad listing you try to page past the ceiling.
rows = []
for sub in ["python", "learnpython", "datascience"]:
    page = comments("scraping", subreddit=sub)
    rows.extend(page["comments"])

print(len(rows), "comments across 3 subreddits")

The loop is the important part rather than the request. Because every listing stops near a thousand items, the way to collect a hundred thousand comments is a hundred narrow queries, not one query paged a hundred times. Partition by subreddit first, then by keyword, then by time window, and keep the partition key on every row so a re-run can resume. The general Python setup, including error handling and pagination fields, is on the Python answer, and the PRAW comparison is on the PRAW alternative page.

What to look for in a Reddit scraper API in 2026

Rankings age badly, so it is more useful to carry four questions than a list of names. First, are comments first-class, with search, trees and user history as separate endpoints rather than a single posts call that happens to include some replies. Second, is billing per call or per result, because per-result billing hands the cost of a noisy keyword to you and makes the same job cost a different amount every run. Third, does the provider publish its limits, and specifically does it admit the single-listing ceiling that everyone inherits. Fourth, can it write, because a pipeline that finds a thread and then needs a human to reply in it is only half a pipeline.

That last question is the one people skip and regret. Read-only is the default among Reddit data providers, so a workflow that grows a write step later has to change vendor rather than add a call. Which categories can actually write is broken down on the posting and commenting answer, and if you are moving off a dead or restricted source in the first place, the Pushshift replacement answer covers what survived.

Reliable automated Reddit data fetching, and the three things that break it

Reliability in this lane is not uptime, it is whether a broken run announces itself. The characteristic Reddit collection failure is not a crash, it is a job that keeps exiting zero while returning fewer rows every day, because a shape change turned a parsed field into a missing key and the code helpfully skipped it. Alert on result counts, not on exceptions. A run that returns eighty percent of yesterday's volume is a defect even when nothing errored.

The second breaker is deletion. Comments disappear, and a dataset assembled over weeks will not reproduce if you re-run it, so record what you collected rather than planning to fetch it again. The third is your own partitioning: if you cannot say which slice a row came from, you cannot tell a genuinely quiet subreddit from a partition that silently stopped being queried.

What affordable means once the volume is real

Cheap at a thousand calls and cheap at a million are different products. Per-call pricing is flat by construction: at $0.002 a read, ten thousand calls is $20.00 and a million is $2,000.00, with nothing underneath it. Per-result and compute-unit pricing move with how much data a run happens to return, which is the variable you control least.

The deep comment search is worth pricing deliberately rather than avoiding. At $0.02 it is 10 times a standard read, and it replaces a search plus several tree reads, so it is cheaper than the calls it saves whenever you would have made more than that many. Run your own volumes through the cost calculator, and if the official commercial tier is the comparison you actually need, the commercial tier answer has the per-call arithmetic.

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