If you want tweets in 2026, you have three options. You can pay Twitter $100 a month for 10,000 reads. You can roll your own scraper and spend half your week fighting anti-bot. Or you can call a managed actor that costs $0.30 per 1,000 tweets and ships JSON to your code.
This post covers option three — what changed in May 2026, how the pricing breaks down against the alternatives, and the five lines of Python you need to start pulling tweets.
In February 2023 Twitter killed the free API tier. The replacement tiers, three years later, look like this:
For most real workloads this is broken. Academic researchers cannot justify $5K a month. Indie dev tools cannot pass on $100/month per customer. Sentiment analysis startups need volumes that only Enterprise covers, at prices only enterprises pay.
What people actually want is the old deal — pay for the data you use, no monthly minimum, no rate limit games, no sales call.
Our Twitter scraper on Apify went live earlier this year and on May 22, 2026 we cut the price from $4.90 per 1,000 tweets to $0.30 per 1,000. That is a 16x reduction. We did it because we hit the volume needed to make the math work on cheaper proxies, and we would rather have a thousand users at $0.30 than a hundred at $4.90.
What you get:
Numbers, all normalized to cost per 1,000 tweets:
| Option | Cost per 1,000 tweets | Monthly minimum | API key? |
|---|---|---|---|
| Twitter API Basic | $10.00 | $100 | Yes |
| Twitter API Pro | $5.00 | $5,000 | Yes |
| Twitter API Enterprise | ~$3.50 | $42,000/yr | Yes |
| Competing Apify actors | $5.00 – $8.00 | None | No |
| Our actor (May 2026) | $0.30 | None | No |
The Twitter Basic comparison is almost flattering to Twitter — it assumes you actually use the full 10K quota. If you only pull 2,000 tweets in a month, Basic costs you $50 per 1,000. With the actor, it costs $0.60 total.
Five lines, give or take. Install the official Apify client:
pip install apify-client
Then call the actor:
from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("cryptosignals/twitter-scraper").call(
run_input={
"searchTerms": ["openai", "anthropic"],
"maxTweets": 500,
"sort": "Latest",
}
)
for tweet in client.dataset(run["defaultDatasetId"]).iterate_items():
print(tweet["author"], "|", tweet["text"][:80])
That is the whole integration. run_input takes a list of search terms, a tweet cap, and a sort order. The dataset comes back as a stream of dicts — iterate, push to your database, send to your sentiment model, whatever you are building.
A few notes on the input schema:
searchTerms — list of strings. Hashtags work (#crypto), so do user handles (from:elonmusk) and date filters (since:2026-05-01).maxTweets — cap per run, up to 10,000.sort — Latest or Top. Latest pulls reverse-chronological, Top pulls highest-engagement.language — optional ISO code (en, pl, de, etc.) to filter.Each item in the output dataset looks roughly like this:
{
"id": "1791234567890123456",
"url": "https://x.com/handle/status/1791234567890123456",
"text": "...",
"author": "handle",
"createdAt": "2026-05-23T14:22:11Z",
"likeCount": 142,
"retweetCount": 18,
"replyCount": 9,
"lang": "en",
"media": ["https://pbs.twimg.com/..."],
"conversationId": "1791234567890123456"
}
The people pulling tweets through this actor today are doing roughly four things:
If you are doing none of these and just want to play with tweets — that works too. The pricing means you can pull 10K tweets, spend $3, and decide whether the data is useful before committing to anything.
Sign up to Apify (free), grab your token, and run the Python snippet above. If you need bulk pricing or a custom variant — a different output schema, longer history, a higher per-run cap — email [email protected].
The price cut is permanent. The actor will stay at $0.30 per 1,000 tweets for the foreseeable future.