[2026] Building an IG Follower Growth Program | Python Automation Tutorial + Full Risk Analysis

Have you ever thought about building your own Instagram automation tool—something that auto-follows, auto-likes, and auto-comments while you sleep? As a developer or tech enthusiast, the idea sounds exciting. But before you start, you need to understand the risks: Instagram keeps strengthening its detection of and penalties for automated behavior, and having your account restricted or disabled is a real outcome. (You may have seen a "ban rate above 95%" figure elsewhere—including in an earlier version of this article. No source for that number exists, so it has been removed; see the next section.)

This article is not an encouragement to violate Instagram's Terms of Service. It is an educational overview of the underlying technology, risk assessment, and legal alternatives. If you still want to try after reading, at least you'll know how to reduce your exposure. But our strong recommendation remains: the technical barriers are too high, the risks too great—a professional service is a smarter choice.

If you want to learn about other growth methods, we recommend reading the 2025 Complete IG Follower Growth Guide first. This article focuses specifically on the development side.

Instagram API Basics

Before writing any code, you need to understand what APIs Instagram provides and what their limitations are.

Official API vs. Unofficial API

So how is the official rate limit actually calculated? (An earlier version of this article said "200 calls/hour," which is wrong.)

Meta's official documentation gives a dynamic formula, not a fixed number:

Calls within 24 hours = 4800 × Number of Impressions

Here, impressions means the number of times your professional account's content entered someone's screen over the past 24 hours—so the more reach your account has, the larger your available API budget. Messaging endpoints carry their own separate limits (the Conversations API, for example, allows 2 calls per second per professional account).

Bottom line:

• If you want to legally access your own account data (post analytics, follower insights) → use the official Graph API
• Comments and messages can in fact be handled programmatically through official endpoints (comment moderation, replying to comments, and sending/receiving messages all have official APIs)—no unofficial package required
• What the official API genuinely does not offer is following and liking → automating those two actions requires an unofficial API (at extremely high risk)

Official Graph API Setup (Legal Method)

Step 1: Create a Facebook Developer Account

1. Log in with your Facebook account at developers.facebook.com
2. Complete developer verification

Step 2: Create an Application

1. Click "Create App"
2. Select type: "Consumer" or "Business"
3. Fill in the app name and contact email

Step 3: Add Instagram Product

1. In the app dashboard, click "Add Product"
2. Select "Instagram Graph API"
3. Complete the setup

Step 4: Get an Access Token

1. Go to "Tools" → "Graph API Explorer"
2. Select your application
3. Choose permissions: instagram_basic, instagram_manage_insights
4. Generate an Access Token

Step 5: Test the API

Use your token to fetch basic account info:

https://graph.instagram.com/me?fields=id,username&access_token=YOUR_ACCESS_TOKEN

Where the official API's boundary actually sits (an earlier version of this article wrongly listed commenting as impossible; that has been corrected):

• ✅ Can do: retrieve post data and follower insights; moderate and reply to comments (POST /<IG_COMMENT_ID>/replies and related endpoints, plus retrieving, hiding, and deleting comments and toggling commenting); publish content; receive mentions; send and receive messages
• ❌ Cannot do: auto-follow and auto-like—neither endpoint exists in the official capability list

In other words, scheduling posts, managing comments, and replying to messages never required an unofficial package. Only people who want to automate following and liking get pushed toward unofficial APIs—and that is precisely the highest-risk part.

Python Environment Setup

If you've decided to try this anyway (at your own risk), here's the basic environment setup.

Install Required Packages

# Install instagrapi (unofficial IG API package—HIGH RISK!)
pip install instagrapi

# Install other tools
pip install requests
pip install python-dotenv

Note: sqlite3 is part of the Python standard library. It does not need to be (and cannot be) installed with pip—just import sqlite3 in your code. An earlier version of this article included pip install sqlite3, which fails with No matching distribution found for sqlite3 and stops readers at the very first step. That line has been removed.

Warning: instagrapi is an unofficial package that uses reverse engineering to bypass Instagram's restrictions. The risk of having your account restricted or disabled is extremely high.

Basic Login Example (For Learning Only)

from instagrapi import Client
import os
from dotenv import load_dotenv

# Load environment variables (.env file—never hardcode credentials in your script)
load_dotenv()

# Initialize client
cl = Client()

try:
    # Log in (using environment variables)
    cl.login(
        username=os.getenv('IG_USERNAME'),
        password=os.getenv('IG_PASSWORD')
    )
    print("Login successful")

    # Get your own account info
    user_info = cl.account_info()
    print(f"Username: {user_info.username}")
    print(f"Followers: {user_info.follower_count}")

except Exception as e:
    print(f"Login failed: {e}")

.env file (add to .gitignore—never upload to GitHub):

IG_USERNAME=your_ig_username
IG_PASSWORD=your_password

Basic Script Examples (For Learning Only—Not Recommended for Real Use)

The following scripts are for educational and research purposes only. Using them in practice will result in account bans.

Script 1: Auto-Follow Target Users

Logic:

1. Get the follower list of a competitor account
2. Auto-follow those users
3. Hope they follow back

from instagrapi import Client
import time
import random

def auto_follow_target_users(cl, target_username, max_follow=50):
    """
    Auto-follows followers of a target account

    Parameters:
    cl: instagrapi Client
    target_username: target account (e.g., a competitor)
    max_follow: maximum number to follow (recommend < 50 to reduce detection)
    """

    try:
        # Get target account's user ID
        target_user_id = cl.user_id_from_username(target_username)

        # Get target account's follower list
        followers = cl.user_followers(target_user_id, amount=max_follow)

        followed_count = 0

        for user_id, user_info in followers.items():
            try:
                # Follow the user
                cl.user_follow(user_id)
                followed_count += 1
                print(f"Followed: {user_info.username}")

                # Random delay (simulate human behavior to reduce detection risk)
                delay = random.randint(30, 90)  # 30–90 seconds
                print(f"Waiting {delay} seconds...")
                time.sleep(delay)

            except Exception as e:
                print(f"Follow failed: {user_info.username}, error: {e}")
                continue

        print(f"Done! Followed {followed_count} users")

    except Exception as e:
        print(f"Script error: {e}")

# Usage example (use at your own risk!)
# auto_follow_target_users(cl, "competitor_account", max_follow=30)

Risks:

• Rapid following in a short period is one of the most easily detected behavioral patterns there is
• Even with random delays, Instagram's AI can still identify the pattern
• Instagram can now identify scripts specifically designed to simulate human behavior

Script 2: Auto-Like and Comment

Logic:

1. Search posts by hashtag
2. Auto-like them
3. Auto-comment with a random pre-set message

from instagrapi import Client
import time
import random

def auto_like_and_comment(cl, hashtag, max_posts=30, comment_list=None):
    """
    Auto-likes posts and optionally leaves comments

    Parameters:
    cl: instagrapi Client
    hashtag: hashtag to search (e.g., "fitness")
    max_posts: maximum posts to interact with
    comment_list: list of pre-set comments to choose from
    """

    if comment_list is None:
        comment_list = [
            "Great post! 👍",
            "Really helpful, thanks for sharing!",
            "Love this content 🔥",
            "Well done!",
            "Keep it up! 💪"
        ]

    try:
        # Search recent posts for the hashtag
        medias = cl.hashtag_medias_recent(hashtag, amount=max_posts)

        interaction_count = 0

        for media in medias:
            try:
                # Like the post
                cl.media_like(media.id)
                print(f"Liked: {media.code}")

                # Randomly decide whether to comment (50% chance)
                if random.random() < 0.5:
                    comment_text = random.choice(comment_list)
                    cl.media_comment(media.id, comment_text)
                    print(f"Commented: {comment_text}")

                interaction_count += 1

                # Random delay (60–120 seconds)
                delay = random.randint(60, 120)
                print(f"Waiting {delay} seconds...")
                time.sleep(delay)

            except Exception as e:
                print(f"Interaction failed: {e}")
                continue

        print(f"Done! Interacted with {interaction_count} posts")

    except Exception as e:
        print(f"Script error: {e}")

# Usage example (use at your own risk!)
# auto_like_and_comment(cl, "fitness", max_posts=20)

Risks:

• Instagram can identify templated comments (e.g., the same "Great post! 👍" repeated)
• Rapid-fire interactions → shadowban
• If comments are reported as spam → comment function blocked on the account

Script 3: Target Audience Analysis

Logic:

1. Analyze a competitor's followers
2. Identify high-engagement users
3. Prioritize following those users

from instagrapi import Client
import sqlite3

def analyze_target_audience(cl, target_username, db_path="audience.db"):
    """
    Analyzes the followers of a target account to find high-engagement users
    """

    # Create SQLite database
    conn = sqlite3.connect(db_path)
    cursor = conn.cursor()

    # Create table
    cursor.execute('''
    CREATE TABLE IF NOT EXISTS target_users (
        user_id TEXT PRIMARY KEY,
        username TEXT,
        follower_count INTEGER,
        following_count INTEGER,
        engagement_rate REAL,
        is_private INTEGER,
        is_verified INTEGER,
        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
    )
    ''')

    try:
        target_user_id = cl.user_id_from_username(target_username)
        followers = cl.user_followers(target_user_id, amount=100)

        for user_id, user_info in followers.items():
            try:
                user_detail = cl.user_info(user_id)

                # Simplified engagement rate: followers/following ratio
                if user_detail.following_count > 0:
                    engagement_rate = user_detail.follower_count / user_detail.following_count
                else:
                    engagement_rate = 0

                cursor.execute('''
                INSERT OR REPLACE INTO target_users
                (user_id, username, follower_count, following_count, engagement_rate, is_private, is_verified)
                VALUES (?, ?, ?, ?, ?, ?, ?)
                ''', (
                    str(user_id),
                    user_detail.username,
                    user_detail.follower_count,
                    user_detail.following_count,
                    engagement_rate,
                    1 if user_detail.is_private else 0,
                    1 if user_detail.is_verified else 0
                ))

                conn.commit()
                print(f"Analyzed: {user_detail.username}, engagement rate: {engagement_rate:.2f}")

            except Exception as e:
                print(f"Analysis failed: {e}")
                continue

        # Query high-engagement users (rate > 1, public accounts)
        cursor.execute('''
        SELECT username, follower_count, engagement_rate
        FROM target_users
        WHERE engagement_rate > 1 AND is_private = 0
        ORDER BY engagement_rate DESC
        LIMIT 20
        ''')

        high_engagement_users = cursor.fetchall()

        print("\nTop 20 High-Engagement Users:")
        for username, followers, rate in high_engagement_users:
            print(f"  - {username}: {followers} followers, rate {rate:.2f}")

    except Exception as e:
        print(f"Script error: {e}")

    finally:
        conn.close()

# Usage example (use at your own risk!)
# analyze_target_audience(cl, "competitor_account")

This script carries relatively lower risk (analysis only, no automated actions), but still violates Instagram's Terms of Service (uses unofficial API).

Database Design and Operational Logging

To track operation history and avoid repeating the same actions, you need a database.

SQLite Database Design

import sqlite3
from datetime import datetime

def init_database(db_path="ig_automation.db"):
    """Initialize the database"""

    conn = sqlite3.connect(db_path)
    cursor = conn.cursor()

    # Follow history table
    cursor.execute('''
    CREATE TABLE IF NOT EXISTS follow_history (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        user_id TEXT NOT NULL,
        username TEXT,
        action TEXT,  -- 'follow' or 'unfollow'
        timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
        success INTEGER  -- 1=success, 0=failed
    )
    ''')

    # Like history table
    cursor.execute('''
    CREATE TABLE IF NOT EXISTS like_history (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        media_id TEXT NOT NULL,
        media_code TEXT,
        timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
        success INTEGER
    )
    ''')

    # Comment history table
    cursor.execute('''
    CREATE TABLE IF NOT EXISTS comment_history (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        media_id TEXT NOT NULL,
        comment_text TEXT,
        timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
        success INTEGER
    )
    ''')

    # Daily stats table (track daily totals to stay within IG limits)
    cursor.execute('''
    CREATE TABLE IF NOT EXISTS daily_stats (
        date TEXT PRIMARY KEY,
        follow_count INTEGER DEFAULT 0,
        like_count INTEGER DEFAULT 0,
        comment_count INTEGER DEFAULT 0
    )
    ''')

    conn.commit()
    conn.close()

    print("Database initialized")

init_database()

Benefits:

• Prevents re-following or re-liking the same user/post
• Tracks daily action counts to stay within safe limits
• Helps analyze which actions are effective (follow-back rate, engagement rate)

Risk Mitigation and Safety Settings

Even with all safety measures in place, you cannot 100% prevent account bans—but you can reduce the probability.

Rate Limiting

First, to be clear: Instagram has never published action limits.

An earlier version of this article carried a threshold table ("20–30 follows per hour, 150–200 per day"). It has been removed. Instagram's Help Center has never published the thresholds that trigger an action block, third-party sources contradict each other wildly (for the same action, some say 200 per day, others say 20–30 per hour), and publishing precise thresholds implies "stay under this number and you're safe"—which is the most dangerous kind of misdirection.

In practice these limits shift with account age, account history, and whether you've recently been reported; the same numbers produce completely different outcomes on different accounts. The consequences aren't fixed either: you may temporarily lose the ability to follow, like, or comment, or the account may be restricted or disabled outright.

The code below demonstrates the practice of setting and logging your own ceiling. Choose conservative numbers yourself—do not treat any figure as an officially safe threshold.

Implementing rate limiting:

import sqlite3
from datetime import date

def check_daily_limit(db_path, action_type, limit):
    """Check whether the daily limit has been reached"""

    conn = sqlite3.connect(db_path)
    cursor = conn.cursor()

    today = str(date.today())

    cursor.execute(f'''
    SELECT {action_type}_count FROM daily_stats WHERE date = ?
    ''', (today,))

    result = cursor.fetchone()

    if result is None:
        cursor.execute('''
        INSERT INTO daily_stats (date, follow_count, like_count, comment_count)
        VALUES (?, 0, 0, 0)
        ''', (today,))
        conn.commit()
        current_count = 0
    else:
        current_count = result[0]

    conn.close()

    if current_count >= limit:
        print(f"Daily {action_type} limit reached ({limit}), stopping")
        return False
    else:
        return True

# Usage
if check_daily_limit("ig_automation.db", "follow", 150):
    # Execute follow action
    pass
else:
    print("Daily follow limit reached")

Simulating Human Behavior

How Instagram's AI detects bots:

1. Action patterns: Perfectly regular actions (e.g., one follow every exactly 30 seconds) → 100% bot
2. Operating hours: 24-hour non-stop activity → not a real person
3. Action sequence: Following accounts in user ID order → obviously a script
4. Mouse movement/click patterns: No cursor movement, direct API calls → bot

Human-simulation techniques (still cannot guarantee 100% evasion):

import random
import time
from datetime import datetime

def human_like_delay(min_sec=30, max_sec=120):
    """Simulate random human-like delays"""
    delay = random.uniform(min_sec, max_sec)
    print(f"Waiting {delay:.1f} seconds...")
    time.sleep(delay)

def is_active_hours():
    """Only operate during 'human active hours' (8 AM to 11 PM)"""
    current_hour = datetime.now().hour
    return 8 <= current_hour <= 23

def random_break():
    """Take random breaks (simulate humans needing rest)"""
    if random.random() < 0.1:  # 10% chance of taking a break
        break_time = random.randint(300, 900)  # 5–15 minutes
        print(f"Random break for {break_time//60} minutes...")
        time.sleep(break_time)

def safe_follow(cl, user_id):
    """Safe follow action with all protections applied"""

    if not is_active_hours():
        print("Outside active hours, stopping")
        return False

    if not check_daily_limit("ig_automation.db", "follow", 150):
        return False

    random_break()

    try:
        cl.user_follow(user_id)
        print("Follow successful")
        human_like_delay(30, 120)
        return True
    except Exception as e:
        print(f"Follow failed: {e}")
        return False

Even with all of this, Instagram can still detect bots—and its detection keeps being updated.

Legal vs. Prohibited: What You Can and Cannot Do

What Is Legal (Using the Official API)

What Is Prohibited (Violates Terms of Service—Will Lead to Account Ban)

Two distinctions worth making (an earlier version of this article collapsed them, which is misleading):

• Comments: Blasting canned comments through an unofficial package without authorization is prohibited. Moderating and replying to comments through the official Instagram API is a supported, compliant feature.
• Messages (DMs): Unauthorized automated DMs are prohibited. But Meta does provide official Instagram messaging endpoints (Send API, Private Replies API) and even documents their rate limits—so programmatic messaging has a legitimate path. The difference is whether you're on the officially authorized one.

The "risk level" column above is a qualitative judgment, not a probability. Nobody who attaches percentages to these behaviors (this article's earlier version included) can produce a source for them.

Build It Yourself vs. Professional Service: Cost-Benefit Analysis

Let the numbers speak. Here's why we don't recommend building it yourself.

Full Cost Comparison

Actual cost calculation:

Cost of building it yourself = time cost + maintenance cost (the loss from a banned account cannot be quantified and is counted separately)

Time cost = 50 hours × your hourly rate (NT$300 in this example) = NT$15,000
Risk cost = losses if account is banned (followers gone, brand damaged) — cannot be quantified, so it is not included in the total
Maintenance cost = 5 hours/month × NT$300 × 12 months = NT$18,000

Total cost = NT$33,000+ (the figure moves with your own hourly rate)

---

Professional service cost = priced per quantity on the official site, depending on how much you order, with no build or maintenance hours on your side

Savings = (build yourself − professional service) / build yourself
          (the denominator is your own build cost; plug in the site's current unit price for your own number)

Conclusion: From a cost-benefit standpoint, a professional service wins decisively.

Legal Alternatives: Automation Tools That Don't Require Code

If you only want to automate legitimate functions (like scheduling posts or analyzing data), here are legal tools that can do it.

Recommended Legal Automation Tools

1. Later (Post Scheduling)

Features:

• Schedule posts (images, Reels, Stories)
• Best time to post suggestions
• Hashtag recommendations
• Analytics

Price: No free plan—only a 14-day free trial. Current plans (annual billing, 25% off included): Starter US$18.75/month, Growth US$37.50/month, Scale US$82.50/month

An earlier version of this article said "free, up to 30 posts/month." That was wrong: "schedule up to 30 posts per profile" is the ceiling on the paid Starter plan, not a free allowance.

Compliance: ✅ Official Instagram partner

Rating: ⭐⭐⭐⭐⭐

2. Buffer (Multi-Platform Management)

Features:

• Schedule posts across IG, Facebook, Twitter, LinkedIn
• Team collaboration
• Analytics

Price: Priced per channel. Free forever (up to 3 channels); Essentials US$5/month per channel ($60 billed yearly); Team US$10/month per channel ($120 billed yearly)

An earlier version of this article said "from $6/month" without noting the per-channel pricing. Corrected.

Compliance: ✅ Scheduling is a use the platform permits (we could not verify a Meta official-partner status)

Rating: ⭐⭐⭐⭐

3. Hootsuite (Enterprise-Grade)

Features:

• Manage multiple accounts
• Social listening (monitor brand mentions)
• Advanced analytics

Price: Standard from US$99/month (Professional US$199, up to Advanced at US$399), billed per user per month on an annual plan

Compliance: ✅ Scheduling is a use the platform permits (we could not verify a Meta official-partner status)

Rating: ⭐⭐⭐⭐ (best for enterprises)

4. Zapier / IFTTT (Workflow Automation)

Features:

• Cross-platform automation (e.g., sync Facebook posts to Instagram)
• Automated responses in specific scenarios
• Data integration

Price:

• Zapier: has a Free plan (US$0/month, free forever); paid Professional from US$19.99/month, Team from US$69/month
• IFTTT: the free plan allows only 2 Applets; Pro US$2.99/month, Pro+ US$8.99/month

An earlier version of this article simply said "IFTTT free," which implies the free tier is unrestricted. The actual limit is now stated.

Compliance: ✅ Uses official APIs

Rating: ⭐⭐⭐⭐

Build It Yourself vs. Legal Tools vs. Professional Service

5 Reasons You Shouldn't Build Your Own IG Follower Growth Program

Reason 1: Extremely High Account Risk

Instagram's detection keeps getting stronger:

• Can identify the call patterns of unofficial APIs
• Can identify scripts designed to simulate human behavior
• Can track IP addresses, device fingerprints, and behavioral patterns

No set of precautions can guarantee you won't be detected—and once a penalty lands (feature restrictions, suspension, termination), all you can do is appeal to the Help Center, and that outcome is not in your hands.

Reason 2: Staggering Time Investment (50–100 Hours)

Building a fully functional Instagram automation program requires:

• Learning Python basics: 10 hours
• Learning API usage: 10 hours
• Learning databases: 5 hours
• Developing the scripts: 20 hours
• Testing and adjustments: 10 hours
• Adapting to Instagram updates: 5+ hours per month

Total: 50–100 hours — roughly NT$15,000–NT$30,000+ in time cost at an assumed NT$300/hour (the figure moves with your own rate)

Reason 3: Low Follower Quality

Even if you successfully follow 1,000 users:

• Follow-back rate is low: most of the strangers you follow will not follow you back
• Engagement is minimal: accounts that follow back out of reciprocity rarely interact with your content
• Many of them unfollow soon after, once they realize the follow was automated

Very few genuinely useful followers survive, and their quality is poor

Reason 4: Legal Risk

• Violates Section 4.2 of the Instagram Terms of Use; the platform can terminate service accordingly
• Commercial use may expose you to civil disputes (for example, misrepresenting your service to clients)
• The CFAA is often cited, but read it narrowly: Van Buren (2021) narrowed its scope, and hiQ v. LinkedIn (2022) found only, at the preliminary injunction stage, that hiQ had raised serious questions about whether the CFAA reaches the scraping of public data — not a final holding that violating terms of use can never create CFAA liability; and it is US federal law with limited applicability to readers in Taiwan

What happens immediately is not legal liability; it is the loss of the account itself

Reason 5: Extremely Poor Return on Investment

Building it yourself:
- Time cost: NT$15,000–NT$30,000 (50–100 hours at an assumed NT$300/hour)
- Risk cost: the loss from a banned account cannot be quantified
- Follower quality: terrible
- Total cost: NT$33,000+ (NT$15,000 of build time plus NT$18,000 of maintenance over a year)

Professional service:
- Money cost: priced per quantity on the official site, depending on how much you order
- Risk: you never hand over your password, but no service can guarantee the platform won't take action
- Follower quality: — (no quality metric is published; we don't publish unsourced self-ratings)
- Total cost: same — it depends on how much you order

ROI difference: building it yourself costs you 50–100 hours up front and a year of maintenance; buying the service costs you none of those hours

To gain 2,000 followers, building it yourself means 50–100 hours of work plus a year of maintenance (about NT$33,000+ at an assumed NT$300/hour), and you carry the ban risk yourself; the service is priced per quantity with zero build or maintenance hours — plug in the site's current unit price to see the difference for yourself.

Final Recommendation: 3 Paths for 3 Types of People

Type 1: Pure Technical Researcher

Goal: Understand IG API mechanics, develop technical skills

Recommendations:

• ✅ Read this article, understand the technical principles
• ✅ Use the official Graph API for legal data analysis
• ❌ Don't test illegal scripts on real accounts
• ✅ OK to research on test accounts (at your own risk)

Type 2: General User Who Wants More Followers

Goal: Grow Instagram followers

Recommendations:

• ❌ Don't build it yourself (risk too high)
• ✅ Use free organic methods: see Free Instagram Growth Guide
• ✅ Or choose a professional service with good reviews from real users
• ✅ Or review all your options: Complete Instagram Growth Methods Guide

Your time is valuable—don't waste it on high-risk, low-reward technical development.

Type 3: Business or Brand

Goal: Build a long-term Instagram presence

Recommendations:

• ❌ Absolutely do not use automation tools (brand damage risk is too severe)
• ✅ Use legal tools: Later, Buffer, Hootsuite for scheduling and analytics
• ✅ Focus on content quality: see IG Growth Techniques
• ✅ Small paid investment for cold-start acceleration via a reputable professional service

Further Reading

Want to learn about other Instagram growth methods?

• Complete guide: 2025 Complete IG Follower Growth Guide — covers every method
• Free techniques: Free Instagram Growth Guide — 15 zero-cost tips
• Plugin reviews: IG Follower Plugin Recommendations — risk analysis
• Full methods breakdown: Complete Instagram Growth Methods Guide — cost-benefit analysis
• Advanced strategies: IG Growth Techniques — 10 advanced secrets
• Beginner guide: How to Gain Instagram Followers — 10-step complete tutorial

This guide covers the how-to side of Instagram growth. If what you need is a faster start on the numbers, Lion Fans offers Instagram follower, like and view services — see plans and pricing.

References

This article draws from:

• Instagram Terms of Use — Section 4.2 "How You Can't Use Instagram" and Section 6 on account termination and appeals (help.instagram.com/581066165581870, archived copy dated 2026-07-21)
• Meta official Rate Limits documentation (developers.facebook.com/docs/graph-api/overview/rate-limiting)
• Meta official Instagram Platform comment moderation documentation (developers.facebook.com/docs/instagram-platform/comment-moderation)
• Python sqlite3 standard library documentation (docs.python.org/3/library/sqlite3.html)
• instagrapi package page on PyPI (actively maintained; check the PyPI page for the current release — it was 2.18.18, published 2026-08-26, when we checked)
• Official pricing pages for each tool (Later, Buffer, Hootsuite, Zapier, IFTTT), accessed July 2026

Update Log

• 2026-07-23: Re-verified against official API documentation and the current terms, correcting the technical specifications, rate limits, available endpoints and legal notes in this article. Tool pricing now follows each vendor's official pricing page, and figures we could not source were removed.
• 2025-10-18: First published.