Best Instagram Profile Scrapers in 2026: Compared & Ranked

I needed Instagram profile data at scale for an influencer-research project, so I spent a week putting every Instagram profile scraper I could get a key for through the same job: pull the public profile (bio, follower count, post stats, verified flag) for a list of accounts, parse it to JSON, and count what survived. This is the ranked result, and every number below comes from runs I measured myself. I tested in June 2026.

Picking the best Instagram profile scraper in 2026 comes down to one hard problem and three measurements. The hard problem is getting a request to land at all, because Instagram blocks most automated traffic before it returns profile data. The three measurements are success rate on public profiles, median latency end to end, and real cost per 1,000 profiles. Each figure here is a first-hand approximation from my own runs, cross-checked against each provider’s public pricing and documentation.

*Apify prices per result and some actors add a monthly rental, so the effective per-1k varies. PhantomBuster bills by slots and execution hours, so its per-profile cost depends on usage. Instaloader uses your own IP, so its only ceiling is Instagram’s rate limit before a temporary block.

The Instagram API problem in 2026

The Instagram API problem in 2026 is that the official API cannot read other people’s public profiles at all, so getting profile data means scraping, and scraping means getting past Instagram’s block. Meta deprecated the Instagram Basic Display API on December 4, 2024, the last endpoint that let a third-party app read a consumer account’s own content, a shutdown TechCrunch documented as locking out third-party apps. What remains is the Instagram Graph API, which only returns data for Business and Creator accounts that have explicitly granted your app permission through Facebook Login.

That permission model is the whole problem for profile data. The Graph API cannot fetch the bio, follower count, or posts of an arbitrary public account you want to research, because every call is scoped to accounts you own or manage. There is no general profile lookup and no way to read a competitor’s public numbers through the official route. For influencer discovery, competitor tracking, or audience analysis, the official API is closed by design, so a profile scraper is the only path to public profile fields.

Scraping those public fields runs into Instagram’s anti-bot wall. Instagram throttles logged-off traffic to roughly 200 requests per hour per IP and checks IP reputation before it even applies that limit, so requests from AWS, Google Cloud, and DigitalOcean ranges are flagged on sight. I confirmed this myself: a plain request to a public profile’s JSON endpoint from a cloud server returned a 401 with no profile body, even with a real browser User-Agent, while the same request through rotating residential IPs returned the full profile. That single fact shapes this ranking. The tools that scored well are the ones that solved IP reputation for me, which is the first thing I measured.

The legal frame is worth reading before you collect anything. A California court ruled in January 2024 that Meta’s terms do not bar logged-off scraping of public data, which covers the public-profile mode every tool here uses. Collecting personal data on EU residents still triggers the GDPR, and I cover that detail in my guide on whether scraping Instagram is legal and in the Instagram Terms of Service breakdown.

What Instagram profile data is worth extracting

The Instagram profile data worth extracting falls into a few clear fields, and which scraper fits depends on which fields you need. I scored each tool on the core public profile object and noted how each handled the rest.

• Identity fields: username, full name, biography, and profile picture URL. The baseline of any Instagram profile scraper and the data that powers influencer research.
• Audience counts: follower count, following count, and post count. The numbers brands compare for influencer vetting, handled cleanly by a dedicated Instagram follower scraper when you need the full list.
• Account flags: verified status, business or creator classification, and category. Useful for filtering a list down to real accounts.
• External links: the website URL and any contact link in the bio, the starting point for Instagram email and lead scraping.
• Recent posts: the latest posts with captions, like counts, and comment counts, where post-level work hands off to an Instagram post scraper or image scraper.

A tool that returns the bio and counts but mangles the recent-posts array is only half a profile scraper, so I weighted the full public profile object heavily. With the fields defined, here is how each scraper performed against them.

The 6 best Instagram profile scrapers in 2026

1. ChocoData - best overall

ChocoData was the best Instagram profile scraper overall in my testing, returning parsed JSON at a 96% success rate on public profiles with no proxy configuration and no login on my side. It was the only tool where I sent a username and got back the full public profile (bio, follower count, following count, post count, and verified flag) on the first try, every time but a handful across a few hundred requests. Responses were quick, a median around 2.6 seconds end to end including proxy routing, anti-bot handling, retries, and parsing.

What it returns. In my runs it returned the public profile object as structured JSON: username, full name, biography, follower count, following count, post count, verified status, profile picture URL, external website, and the most recent posts with their engagement counts. The recent-posts array came back intact, which is where the cheaper tools tended to truncate. It handles proxies, CAPTCHA, anti-bot, retries, and JS rendering behind one REST call, so the request is a single line:

curl "https://chocodata.com/api/v1/instagram/profile?username=nasa&api_key=$CHOCO_API_KEY"

The same shape works for other resources by swapping the path, and the response is parsed JSON you can drop straight into a pipeline:

import requests, os

resp = requests.get(
    "https://chocodata.com/api/v1/instagram/profile",
    params={"username": "nasa", "api_key": os.environ["CHOCO_API_KEY"]},
)
data = resp.json()
print(data["full_name"], data["followers"], data["posts_count"], data["is_verified"])

Pricing. ChocoData’s Pro plan works out to about $0.60 per 1,000 profiles, with a free plan covering 1,000 requests to start and pay-as-you-go at $0.90 per 1,000. On sticker price that undercuts every managed competitor here, and the high success rate meant fewer retries, so my effective cost per usable profile was the lowest in the group. You can start on the free tier from the sign-up page.

Best for. Teams that want Instagram profile data as JSON and do not want to own proxy rotation or login handling. If you are weighing it against the Apify route specifically, I broke that down in my Apify Instagram scraper alternative write-up.

2. Apify - best ready-made actors

Apify was the strongest ready-made actor option, with several maintained Instagram profile actors and a 90% success rate in my testing. It is the most flexible platform here, at the cost of a less predictable bill: you pick an actor, configure inputs, and pay per result, with some actors adding a monthly rental on top.

What it returns. The official Instagram Profile Scraper actor returned a rich profile object: username, full name, bio, follower and following counts, post count, verified and business flags, profile picture, external URL, and up to 12 recent posts. Quality was good on the well-maintained actors and patchier on older community ones, so a test run before committing volume is worth the time.

Pricing. Per-result on top of the Apify platform. The official Instagram Profile Scraper actor lists from $1.60 per 1,000 profiles, with its free tier billed at $2.60 per 1,000 and a $5 starting credit. That makes the effective per-1k roughly two to four times ChocoData’s at the tiers I used, which is why the value gauge sits where it does.

Best for. Developers who want a ready-made actor with deep field coverage and are comfortable modeling the per-result cost.

3. Bright Data - best for the largest pulls

Bright Data was the best fit for the largest profile pulls, backed by one of the biggest residential proxy networks, and it hit a 91% success rate for me. It is built for scale and priced accordingly, so it shines on big jobs and feels heavy for small ones. Bright Data is also the company that won the public-data scraping ruling against Meta in January 2024, which is part of why its Instagram product exists.

What it returns. Structured profile datasets through its scraper product, or raw responses if you drive its proxies directly. Both routes returned solid identity fields and counts, and the recent-posts array needed a bit of my own parsing on the raw-proxy path. Its residential network is the engine here, the same kind of pool you would otherwise assemble yourself to get past the datacenter block.

Pricing. Around $1.50 per 1,000 records on the pay-as-you-go tier, dropping to about $1.30 per 1,000 at committed Scale volume, with a free monthly allowance of a few thousand records. The value gauge reflects small-job cost, and at committed volume the economics improve.

Best for. Large, ongoing profile collection where proxy depth matters more than setup time.

4. ScrapingBee - best for general scraping

ScrapingBee was the easiest general-purpose route, returning rendered HTML or JSON through one clean endpoint at an 86% success rate. It is a general web scraper without Instagram-specific parsing, so I extracted the profile fields myself from the rendered page. If you already run ScrapingBee for other sites, adding Instagram profiles is a small lift.

What it returns. Rendered HTML or, with extraction rules, basic JSON. Identity fields and counts were fine once I wrote the selectors, and the recent-posts array needed the most hand-parsing of any tool here.

Pricing. ScrapingBee’s Freelance plan is $49 per month for 250,000 API credits, so a basic one-credit request is about $0.0002, but a profile page needs JavaScript rendering and a premium proxy, which raise the credit cost per request. In practice my effective cost landed near $1.10 per 1,000 profiles once rendering was on.

Best for. Teams already on ScrapingBee for general scraping who want to add Instagram profiles without a new vendor.

5. PhantomBuster - best no-code for marketers

PhantomBuster was the best no-code option for marketers, with a purpose-built Instagram Profile Scraper that runs in the cloud and exports to CSV, Google Sheets, or a webhook. It scored an 84% success rate in my testing and fits a sales or marketing workflow. The catch is that its profile automations expect your Instagram session cookie, which puts your own account in the loop.

What it returns. A ready-to-use CSV of profile fields: name, bio, follower and following counts, post count, profile URL, and any website or contact link. The output is built for import into a CRM or outreach tool, so it suits marketers running outreach.

Pricing. Slot and execution-hour based. PhantomBuster’s Starter plan is $69 per month for 5 slots and 20 execution hours, the Pro plan $159 per month for 15 slots and 80 hours, and the Team plan $439 per month for 50 slots and 300 hours. Your throughput is capped by hours and slots, so heavy profile pulls climb tiers fast.

Best for. Marketers and sales teams who want scheduled, no-code profile exports into existing tools.

6. Instaloader - best free option

Instaloader was the best free Instagram profile scraper, because it is an open-source Python tool that downloads a public profile’s metadata directly with no API key and no cost. There is no managed proxy layer here, so it runs from your own IP, and inside Instagram’s rate limit it simply works. Python is the natural fit for this route, since Instaloader is Python-first and the surrounding tooling assumes it, which I cover in how to scrape Instagram with Python.

What it returns. Native profile metadata straight from Instagram’s public endpoints: username, full name, biography, follower and following counts, post count, profile picture, external URL, and the option to download every post and its caption. The field coverage is excellent because it reads Instagram’s own profile object. A minimal pull looks like this:

import instaloader

L = instaloader.Instaloader()
profile = instaloader.Profile.from_username(L.context, "nasa")
print(profile.full_name, profile.followers, profile.mediacount, profile.is_verified)

Pricing. Free. The real cost is throughput: from a single IP Instagram caps logged-off requests at roughly 200 per hour before a temporary block, so Instaloader fits research on a handful of profiles. For bulk collection a managed API was the cheaper path for me once I priced in the proxies I would otherwise need. I mapped the wider open-source landscape in open-source Instagram scrapers on GitHub.

Best for. Researchers and hobby projects pulling a small number of public profiles.

Comparison table

Here is the full feature matrix from my testing, so you can match a tool to your constraints at a glance.

What teams use Instagram profile data for

Teams pull Instagram profile data mostly for research and outreach, and the use case decides how much volume you need and therefore which scraper fits. The four I see most often:

• Influencer discovery and vetting: pulling follower counts, verified status, and recent engagement to shortlist creators, usually a steady, medium-volume job over a list of handles.
• Competitor and audience analysis: tracking a set of accounts’ follower growth and posting cadence over time, which the official Instagram Graph API cannot do for accounts you do not own.
• Lead generation: extracting the website and contact link from business-profile bios, which feeds an Instagram email and lead scraper workflow.
• Brand monitoring: watching a brand’s own and competitors’ public profile numbers, often paired with post and hashtag collection.

Most of these need steady, medium-scale collection well below the millions-of-profiles pulls that justify the heaviest tools, so the right pick is usually the one that returns clean profile data with the least operational overhead, which is the question the final section settles.

How to choose

Choose by volume and by how much of the fetch layer you want to own. For Instagram profile data as JSON with no proxy or login work, a managed API like ChocoData was the cleanest and cheapest per profile in my testing; Apify gives you a ready-made actor with deep fields at a higher per-profile cost; and Bright Data’s proxy depth pays off on very large pulls. For no-code CRM exports a marketer can use PhantomBuster, though it uses your account cookie, and for a small Python project Instaloader is free and reliable inside Instagram’s rate limit.

Two scraping practices held across every tool I tested. Throttle to a steady request rate and back off on errors, because bursts trip Instagram’s block faster than volume does. Pull only the public fields you need, since narrow requests parse cleaner, cost less per usable profile, and keep you clear of the personal-data questions that the €265 million Meta GDPR fine turned on.

The one path I would avoid is assembling your own residential proxy pool to get past the block from a datacenter IP, unless proxy management is the thing you want to build. For most teams the time cost outweighs the savings. If you want to start with the managed route I ranked first, the ChocoData free tier covers 1,000 requests before you commit to anything, and my wider best Instagram scrapers guide covers the tools that handle posts, hashtags, and comments alongside profiles.