What the heck is GetXAPI? And why it's the best Twitter API alternative

A while back I wrote a short post about the bill that made me start building this. Enough has changed since then that the short version no longer covers it, so this is the longer one: what the thing actually is now, why it is priced the way it is, and the parts I did not see coming.

The origin is still the same, and it is boring: I wanted Twitter data at scale for a project in early 2025, and I priced it out on the official API. The number that came back was $5,000 a month for the tier that included 1M tweets.

The project was making nothing and was never going to make $5K a month. So it died right there, on a spreadsheet, before a line of code got written. Not because the data was not worth having. Because the unit economics made no sense for anything smaller than a funded company.

I did shop around first. TwitterAPI.io came in around $0.15 per 1,000 tweets. Apify actors landed anywhere from $0.40 to $1.50 per 1,000 depending on the actor and how long it sat there running. RapidAPI wrappers were a lottery, $0.30 to $2.00 per 1,000 with uptime to match.

All cheaper than $5,000. All still carrying a catch: a credit system I had to model in my head, an actor that billed me for its own boot time, or a wrapper one bored maintainer away from disappearing.

The thought I could not get past: this data is public. Anyone can open a browser and read it. Why does reading it programmatically get priced like an enterprise contract?

So I built the thing I wanted, and then kept building it for a year.

How much does the Twitter API actually cost?

Worth pinning down, because "expensive" is vague and the pricing has changed more than once.

The shape of official Twitter API pricing has always been tiers with a cliff. A free tier that is effectively write-only, a cheap tier that runs out almost immediately, then a jump to enterprise numbers. When I was pricing it, the Pro tier sat at $5,000 a month for 1M posts. Enterprise pricing was "talk to sales", which is its own answer.

So the honest answer to "what is the Twitter API cost" is: it depends entirely on whether you fit inside a tier, and the moment you do not, the cost jumps by an order of magnitude rather than scaling with your usage.

The part that hurts is not the top-line number, it is the granularity. You are billed per resource read. One post read is one billable unit. There is no batch discount for the fact that you asked for 100 posts in one request.

That detail is the entire reason a cheaper Twitter API alternative can exist.

Why a per-call model is so much cheaper

The pricing I landed on is $0.001 per API call. One call returns about 20 tweets.

That works out to about $0.05 per 1,000 tweets.

The trick is not magic and it is not arbitrage. It is batching. If you bill per record, 1,000 tweets is 1,000 billable events. If you bill per call and a call returns a page of ~20, the same 1,000 tweets is 50 calls. Same data, two orders of magnitude apart on the invoice.

Normalized to the same unit so it is actually comparable:

The other quiet cost most comparisons miss: the official API charges you for searches that return zero results, and burns a rate-limit slot doing it. If your workload is exploratory (you do not know which queries will hit), that adds up in a way that never shows on a pricing page.

I keep a full Twitter API pricing comparison updated with the current per-call numbers across providers, since the official rates have moved twice now and most blog posts quoting them are out of date.

The launch

I put it on Reddit on February 8th. No landing page polish, no waitlist, no launch video. Just a link and an honest description of what it did and what it cost.

People used it. Then people started asking how to pay for it.

I added payments on the 11th.

Three days between "here is a thing" and "someone is paying for this thing" is the fastest feedback loop I have had on a side project. It told me the problem was real and not just mine. Every "I gave up on my project because of API costs" reply in that thread was another person who had done the same math and quit at the same place.

That is where the actual work started.

What it looks like to use

The whole auth model is one bearer token in a header. No OAuth dance, no app registration, no developer account application, no waiting on approval, no consent screen. You sign up, copy a key, and hit a plain REST endpoint. The part of the official flow that used to take days takes about a minute.

Search is a GET with a query and a product filter (latest or top), and it hands back a list of tweets with the fields you would expect: text, timestamp, author, engagement counts, and a cursor for the next page. Anyone who has used a REST API has already used this API. That was deliberate. The whole point was to remove ceremony, not to invent a new one.

The follower endpoint is the one people underestimate. It returns up to 200 full profiles per call at $0.001, and each profile carries 16 fields: handle, display name, bio, follower and following counts, tweet count, verification status, account creation date, location, and whether DMs are open. That last set matters more than it sounds, because it means you can filter an audience properly (by follower threshold, account age, bio keyword, or DM availability) without a second lookup per user. Most cheap alternatives make you pay twice for that.

For Python people, it is a requests.get with an Authorization header, and the response is plain JSON. There is no SDK you are obliged to adopt and no client library to keep in sync. Whatever HTTP library you already use is the integration.

Signup comes with $0.10 in free credits, no card. That is roughly 2,000 tweets, which is enough to run your actual workload once and check whether the numbers hold up. That is the only benchmark that matters, and it is the one I would want before believing any of this.

The other broken thing: Twitter API rate limits

Cost was the loud problem. Twitter API rate limits were the quiet one that ate more of my evenings.

If you have built against the official API you know the ritual. Read x-rate-limit-remaining on every response. Sleep until x-rate-limit-reset, not a fixed delay, or you extend the penalty. Keep a separate queue per endpoint family, because search, user lookup, and writes each have their own window. Then discover the 24-hour cap the hard way at 4pm on a Friday when a nightly job that passed every 15-minute check quietly blows the daily ceiling.

There are no platform-level rate limits here. Throughput is bounded by your credit balance, not a rolling window. If you are stuck debugging the official ones right now, I wrote up how Twitter API rate limits actually work, including the per-endpoint windows and the 429 handling pattern.

In practice that means a backfill is a plain loop over your list of accounts instead of a state machine. No backoff scaffolding, no per-endpoint queues, no reset-header math, no persisting "next eligible run time" between jobs.

I did not appreciate how much that deletes until I ported an old scraper over and removed about 200 lines of retry logic. The job went from something I had to babysit to something I could leave running.

Then MCP happened

In May 2026 I turned the whole API into a Model Context Protocol server. In hindsight it is the most useful thing I have shipped, and I did not plan it.

Every endpoint becomes a native tool your AI client can call. Installing it is a single npx command, then a six-line block in your Claude Desktop config or the Cursor MCP settings panel. Restart the client and the Twitter tools just appear. On Claude Code it is one line and no config file at all. The full setup is on the MCP page.

Now you can say "find the last 50 tweets about vector databases and summarize the sentiment" and the model does the fetching itself. No glue code, no writing a tool wrapper for your agent, no OAuth screen anywhere in the flow.

It is open source on GitHub and listed on the official mcpservers.org registry.

This is the part that surprised me. I built a Twitter data API expecting dashboards and monitoring tools. The interesting use case turned out to be agents, which need exactly this: cheap, per-call, no-auth-ceremony access to a data source.

The endpoints nobody asks for until they need them

There are 60 endpoints now. The obvious ones are obvious: search, user profiles, followers and following, timelines, mentions, lists, bookmarks. Reads are $0.001. Writes (post, reply, like, retweet, follow) are in the same range, DMs at $0.002.

Two I did not expect anyone to care about:

Spaces. You can pull Space info for $0.001, and you can actually download a Space as an MP3 with an optional transcript. That one is usage-priced ($0.05 base plus $0.015 per transcript-minute) because there is real compute behind it, and polling the job status is free. People use it to make audio conversations searchable, which were previously a total black box. Nobody requested this before I shipped it. Now it is one of the stickiest endpoints.

Trends. A dedicated endpoint returns the top trends for any country or city, and every trend comes back with a ready-made search query you can feed straight back into search. I originally told people to build trends themselves by aggregating hashtags out of a search feed, which works but is annoying. Enough people asked for the simple version that I shipped it.

That is basically how the whole surface grew. Someone hits a wall, I look at whether it is a real gap or a one-off, it becomes an endpoint.

The same pattern produced a set of free Twitter tools built on those endpoints: a shadowban checker, a fake-follower audit, and a username-to-ID converter. No login, nothing to install. They exist mostly because they are the fastest way to show what the data can do without asking anyone to write a line of code first.

Where I will be honest with you

This is my product, so read everything above with that bias in mind. Things I would want to know if I were on your side of this:

• It is a third-party API. If your requirement is a first-party contract with X for compliance or legal reasons, buy the official one. That is a legitimate reason and no price comparison changes it.
• Public data. Reads cover what the platform shows publicly. Write actions run on your own account's session, so they are subject to the same automation rules as any account. Pace your automation. An API does not exempt you from acting like a human.
• It is not a magic bypass. It is a cheaper, simpler way to read public data with sane defaults and no rate-limit ceremony. That is the entire claim. There is no genuinely free Twitter API with unlimited reads, and anyone promising one is either a trial in disguise or an open-source scraper that will break next month. The free credits here are a trial too, I just say so.
• Open-source scrapers are a real option. snscrape and friends cost nothing in fees. They also break when X changes its markup, have no support, and the logged-in ones risk your account. Fine for a weekend project, rough for anything a business depends on. That tradeoff is real and I am not going to pretend it is not.

The takeaway

The thing I keep thinking about is not the pricing table. It is that a five-figure annual floor quietly kills an entire category of projects that would otherwise exist.

Not big companies. They can pay, and honestly they should. It is the student doing a sentiment analysis thesis. The indie hacker building a niche monitoring tool. The researcher tracking public discourse. The person who just wants an alert when someone tweets a keyword.

Every one of those does the math, sees the number, and quietly closes the tab. I know because I was one of them, and the Reddit thread was full of people who had done exactly the same.

Making the read cost $0.05 per 1,000 instead of a subscription with a cliff does not sound revolutionary. But it moves a whole class of ideas from "not worth it" to "sure, why not". That is a bigger deal than it looks.

That is what I have been building. It lives at GetXAPI if you want to poke at it.

Happy to answer anything in the comments, including the parts where it is the wrong tool for your job.