Category-wise packs with monthly refresh; export as CSV, ISON, or Parquet.
Pick cities/countries and fields; we deliver a tailored extract with OA.
Launch instantly with ready-made scrapers tailored for popular platforms. Extract clean, structured data without building from scratch.
Access real-time, structured data through scalable REST APIs. Integrate seamlessly into your workflows for faster insights and automation.
Download sample datasets with product titles, price, stock, and reviews data. Explore Q4-ready insights to test, analyze, and power smarter business strategies.
Playbook to win the digital shelf. Learn how brands & retailers can track prices, monitor stock, boost visibility, and drive conversions with actionable data insights.
We deliver innovative solutions, empowering businesses to grow, adapt, and succeed globally.
Collaborating with industry leaders to provide reliable, scalable, and cutting-edge solutions.
Find clear, concise answers to all your questions about our services, solutions, and business support.
Our talented, dedicated team members bring expertise and innovation to deliver quality work.
Creating working prototypes to validate ideas and accelerate overall business innovation quickly.
Connect to explore services, request demos, or discuss opportunities for business growth.
GeoIp2\Model\City Object ( [raw:protected] => Array ( [city] => Array ( [geoname_id] => 4509177 [names] => Array ( [de] => Columbus [en] => Columbus [es] => Columbus [fr] => Columbus [ja] => コロンバス [pt-BR] => Columbus [ru] => Колумбус [zh-CN] => 哥伦布 ) ) [continent] => Array ( [code] => NA [geoname_id] => 6255149 [names] => Array ( [de] => Nordamerika [en] => North America [es] => Norteamérica [fr] => Amérique du Nord [ja] => 北アメリカ [pt-BR] => América do Norte [ru] => Северная Америка [zh-CN] => 北美洲 ) ) [country] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [location] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [postal] => Array ( [code] => 43215 ) [registered_country] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [subdivisions] => Array ( [0] => Array ( [geoname_id] => 5165418 [iso_code] => OH [names] => Array ( [de] => Ohio [en] => Ohio [es] => Ohio [fr] => Ohio [ja] => オハイオ州 [pt-BR] => Ohio [ru] => Огайо [zh-CN] => 俄亥俄州 ) ) ) [traits] => Array ( [ip_address] => 216.73.216.24 [prefix_len] => 22 ) ) [continent:protected] => GeoIp2\Record\Continent Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [code] => NA [geoname_id] => 6255149 [names] => Array ( [de] => Nordamerika [en] => North America [es] => Norteamérica [fr] => Amérique du Nord [ja] => 北アメリカ [pt-BR] => América do Norte [ru] => Северная Америка [zh-CN] => 北美洲 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => code [1] => geonameId [2] => names ) ) [country:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [locales:protected] => Array ( [0] => en ) [maxmind:protected] => GeoIp2\Record\MaxMind Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [validAttributes:protected] => Array ( [0] => queriesRemaining ) ) [registeredCountry:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names [5] => type ) ) [traits:protected] => GeoIp2\Record\Traits Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [ip_address] => 216.73.216.24 [prefix_len] => 22 [network] => 216.73.216.0/22 ) [validAttributes:protected] => Array ( [0] => autonomousSystemNumber [1] => autonomousSystemOrganization [2] => connectionType [3] => domain [4] => ipAddress [5] => isAnonymous [6] => isAnonymousProxy [7] => isAnonymousVpn [8] => isHostingProvider [9] => isLegitimateProxy [10] => isp [11] => isPublicProxy [12] => isResidentialProxy [13] => isSatelliteProvider [14] => isTorExitNode [15] => mobileCountryCode [16] => mobileNetworkCode [17] => network [18] => organization [19] => staticIpScore [20] => userCount [21] => userType ) ) [city:protected] => GeoIp2\Record\City Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 4509177 [names] => Array ( [de] => Columbus [en] => Columbus [es] => Columbus [fr] => Columbus [ja] => コロンバス [pt-BR] => Columbus [ru] => Колумбус [zh-CN] => 哥伦布 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => names ) ) [location:protected] => GeoIp2\Record\Location Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [validAttributes:protected] => Array ( [0] => averageIncome [1] => accuracyRadius [2] => latitude [3] => longitude [4] => metroCode [5] => populationDensity [6] => postalCode [7] => postalConfidence [8] => timeZone ) ) [postal:protected] => GeoIp2\Record\Postal Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [code] => 43215 ) [validAttributes:protected] => Array ( [0] => code [1] => confidence ) ) [subdivisions:protected] => Array ( [0] => GeoIp2\Record\Subdivision Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 5165418 [iso_code] => OH [names] => Array ( [de] => Ohio [en] => Ohio [es] => Ohio [fr] => Ohio [ja] => オハイオ州 [pt-BR] => Ohio [ru] => Огайо [zh-CN] => 俄亥俄州 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isoCode [3] => names ) ) ) )
country : United States
city : Columbus
US
Array ( [as_domain] => amazon.com [as_name] => Amazon.com, Inc. [asn] => AS16509 [continent] => North America [continent_code] => NA [country] => United States [country_code] => US )
In this blog post, we'll dissect the complexities of anti-bot technology for web scraping developers, focus to extract anti-bot landscape and effective strategies. We'll examine the anti-bot distribution curve, derived from an analysis of the top 240,000 websites, illustrating the varying degrees of anti-bot measures employed. Through this lens, we'll elucidate the challenges and consequences developers face in navigating the anti-bot terrain. Moreover, we'll explore innovative solutions the industry has devised to mitigate these challenges.
With the advent of the AI revolution and new scraping technologies such as 'site unblockers' and 'AI scrapers,' an automation-first approach has become paramount. Today, automation, AI, and APIs have transitioned from last-resort tools to first-resort solutions. This shift is attributed to their efficacy in addressing the trade-offs between cost, speed, and success inherent in crawling infrastructure. By leveraging these advancements, developers can enhance their scraping capabilities and effectively navigate the dynamic anti-bot landscape.
Experienced web scraping developers are acutely aware of the perpetual trade-off inherent in their craft, whether consciously acknowledged or intuitively sensed. While numerous websites operate on identical platforms like job boards and ecommerce sites, the level of anti-bot protection varies significantly. From minimal to highly intricate measures, this diversity poses a challenge in the web scraping landscape. Effectively navigating this anti-bot landscape requires strategic deployment of web crawling services and scraping techniques. Developers must adeptly extract insights from the anti-bot landscape strategies to inform their scraping code and optimize web scraping services for success amidst varying levels of anti-bot protection..
In late 2023, Actowiz Solutions conducted an analysis of the leading 240,000 websites utilized on the platform, categorizing the complexity of anti-bot technology into five distinct groups.
Tiers 1-3 = 84%
Tier 4 = 8.2%
Tier 5 = 1.3%
In the dataset extracted from Actowiz API, the analysis reveals a categorization of 240,000 websites into five tiers based on the complexity of their crawling, ranging from tier 5, denoting the most intricate, to tier 1, representing the simplest. This segmentation underscores the diverse web accessibility landscape, necessitating a tailored approach for effective cost management. To navigate this terrain, developers must allocate considerable time and resources to address the unique challenges posed by each website.
At the heart of effective web scraping is the development of custom spider code. This requires meticulous attention to the intricacies of individual websites. Moreover, maintaining a diverse fleet of browsers hosted on servers is crucial to accommodate varying site requirements. Integrating platform-as-a-service technologies or similar solutions further enhances adaptability and scalability. Additionally, comprehensive monitoring systems, dashboards, and alerts are indispensable for ensuring the smooth operation of the tech stack and the timely detection of issues.
By reframing web scraping as a distribution challenge, developers can better understand the nuanced decisions and trade-offs involved. This perspective shift broadens their understanding, as the overarching dilemma lies in balancing project costs with speed and scalability, influenced by the broad spectrum of website complexities.
Each project entails a unique set of considerations, demanding careful evaluation of the cost implications against performance expectations. This evaluation extends beyond the immediate development phase, as ongoing maintenance and potential disruptions further impact the overall cost-effectiveness of the endeavor.
Ultimately, the success of web scraping initiatives hinges on the ability to strike a harmonious balance between cost optimization, speed of execution, and scalability. Developers must remain vigilant, continuously reassessing strategies to adapt to evolving website landscapes and maximize project outcomes. In this dynamic environment, the savvy developer leverages insights from the anti-bot landscape to inform strategic decisions, ensuring efficient resource allocation and sustained project success.
Investing significant time and resources in a one-size-fits-all solution may lead to unnecessary expenses, especially when scraping numerous pages. While it offers instant unblocking for most websites, it's costly and lacks scalability. This trade-off prioritizes speed and success over cost-effectiveness.
Developing a system that prioritizes cost efficiency over perfect success rates can be effective when minimal time constraints allow for ongoing adjustments. While cheaper than sledgehammer and AI solutions, it may lead to occasional data gaps and slower crawling speeds. This trade-off prioritizes cost savings over immediate results and flawless performance.
Developing sophisticated systems with cascading layers of proxy types, browsers, and infrastructure elements, including generative AI for crawler creation, promises accelerated development but has significant drawbacks. While such systems provide instant unblocking for many websites, the investment in time, money, and specialized expertise is substantial. These multi-vendor systems are intricate and fragile, demanding continuous maintenance and upkeep.
At first glance, these solutions may appear intelligent and efficient. However, they introduce a new layer of complexity. The focus shifts from individual website scraping to managing a vast and intricate scraping infrastructure. This transition necessitates skilled developers to balance and maintain proprietary systems composed of multiple tools, vendors, and internal code bases. Consequently, any time saved in building and maintaining the actual crawler is counterbalanced by the high total cost of ownership for the entire scraping system.
Despite their potential to streamline development, these optimized solutions often face an uphill battle in justifying their high costs and maintenance demands. They may offer speed and efficiency in data extraction but impose significant overhead in terms of system ownership. Moreover, they perpetuate the challenges inherent in the anti-bot landscape, necessitating constant adaptation to evolving measures.
Ultimately, the responsibility lies with developers to carefully weigh the benefits and drawbacks of such optimized solutions within the context of their specific scraping needs and organizational resources. While they may offer advantages in certain scenarios, the trade-offs in terms of cost, complexity, and maintenance should be thoroughly evaluated to ensure long-term viability and return on investment in crawling infrastructure. Your informed decisions are key to the success of these solutions.
Utilizing AI-powered solutions can dramatically accelerate the process of creating web scraping code, spanning from spider and crawler creation to selector formulation. By leveraging large language models (LLMs), these solutions automate tasks such as generating selectors and converting JSON into scraping configurations, thereby boosting productivity across diverse domains during development. However, due to the prohibitive cost and limitations of LLMs for precise data extraction, such as SKUs or prices, their usage is typically restricted to expediting selector coding. Despite the advantages, the trade-off lies in the necessity for recurrent selector adjustments, as they are prone to break over time, necessitating periodic fixes. This approach intersects with keywords like crawling infrastructure, scrape anti-bot landscape, strategies, web crawling services, web scraping code, and web scraping services.
Regardless of the system implemented, one critical limitation persists: the reliance on human intervention to address, circumvent, and resolve website bans individually. The scalability and pace of operations are primarily tethered to human resources, surpassing all other factors besides budget considerations.
This constraint may be acceptable depending on the business objectives and project requirements. For instance, prioritizing speed might justify a thirtyfold increase in expenditure per request, mainly if the scope involves crawling a few websites with limited page counts, say, 10,000 pages.
Conversely, in scenarios where data extraction occurs from a single expansive website with millions of pages every quarter, the imperative shifts to optimizing requests for cost-efficiency per query.
However, challenges arise when extracting data swiftly and successfully from various websites while maintaining low overall costs and avoiding protracted system development endeavors. A viable solution must possess several key capabilities:
Dynamic analysis of a website's anti-bot technology with minimal human intervention.
Automated allocation of resources necessary to circumvent bans, tailored to each website's complexity and resistance level.
Continuous monitoring and self-adjustment mechanisms over time to ensure sustained operation.
Access to requisite crawling infrastructure, including proxies, browsers, stealth technologies, and cookie management tools.
Integration with scraping frameworks like Scrapy through an API for enhanced customization and control.
Adaptive pricing models that account for the unique cost structures of individual websites.
The absence of these capabilities condemns a website unblocking system to the perennial trade-off between cost, speed, and success, impeding the ability to scale web data collection effectively. Organizations must address these challenges to avoid being burdened with substantial upfront efforts to unblock spiders, followed by ongoing monitoring and maintenance to preserve their functionality and efficiency.
Actowiz API embodies the principles outlined above, boasting a design that seamlessly aligns with the needs of web data extraction. With its fully hosted infrastructure, Actowiz API simplifies the unblocking process for most websites through a straightforward API call. Users can seamlessly integrate it into their optimized systems or outsource all requests to focus on leveraging the returned data efficiently.
While Actowiz API isn't a universal solution, it significantly enhances automation, leading to substantial productivity gains. Advantages include the benefits of an optimized solution without compromising on trade-offs, resulting in a lower total cost of ownership.
However, automated systems could be more flawless, necessitating domain experts for operation and system adjustments. Trust in Actowiz Solutions is imperative.
To explore Actowiz API's robust ban handling capabilities further, reach out to us for additional information and insights. We're eager to provide you with comprehensive details about our innovative solutions. You can also reach us for all your mobile app scraping, data collection, web scraping service, and instant data scraper requirements.
✨ "1000+ Projects Delivered Globally"
⭐ "Rated 4.9/5 on Google & G2"
🔒 "Your data is secure with us. NDA available."
💬 "Average Response Time: Under 12 hours"
Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.
Find Insights Use AI to connect data points and uncover market changes. Meanwhile.
Move Forward Predict demand, price shifts, and future opportunities across geographies.
Industry:
Coffee / Beverage / D2C
Result
2x Faster
Smarter product targeting
“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”
Operations Manager, Beanly Coffee
✓ Competitive insights from multiple platforms
Real Estate
Real-time RERA insights for 20+ states
“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”
Data Analyst, Aditya Birla Group
✓ Boosted data acquisition speed by 3×
Organic Grocery / FMCG
Improved
competitive benchmarking
“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”
Product Manager, 24Mantra Organic
✓ Real-time SKU-level tracking
Quick Commerce
Inventory Decisions
“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”
Aarav Shah, Senior Data Analyst, Mensa Brands
✓ 28% product availability accuracy
✓ Reduced OOS by 34% in 3 weeks
3x Faster
improvement in operational efficiency
“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”
Business Development Lead,Organic Tattva
✓ Weekly competitor pricing feeds
Beverage / D2C
Faster
Trend Detection
“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”
Marketing Director, Sleepyowl Coffee
Boosted marketing responsiveness
Enhanced
stock tracking across SKUs
“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”
Growth Analyst, TheBakersDozen.in
✓ Improved rank visibility of top products
Real results from real businesses using Actowiz Solutions
In Stock₹524
Price Drop + 12 minin 6 hrs across Lel.6
Price Drop −12 thr
Improved inventoryvisibility & planning
Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.
✔ Scraped Data: Price Insights Top-selling SKUs
"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"
✔ Scraped Data, SKU availability, delivery time
With hourly price monitoring, we aligned promotions with competitors, drove 17%
Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place
Discover how Scraping Consumer Preferences on Dan Murphy’s Australia reveals 5-year trends (2020–2025) across 50,000+ vodka and whiskey listings for data-driven insights.
Discover how Web Scraping Whole Foods Promotions and Discounts Data helps retailers optimize pricing strategies and gain competitive insights in grocery markets.
Track how prices of sweets, snacks, and groceries surged across Amazon Fresh, BigBasket, and JioMart during Diwali & Navratri in India with Actowiz festive price insights.
Scrape USA E-Commerce Platforms for Inventory Monitoring to uncover 5-year stock trends, product availability, and supply chain efficiency insights.
Discover how Scraping APIs for Grocery Store Price Matching helps track and compare prices across Walmart, Kroger, Aldi, and Target for 10,000+ products efficiently.
Learn how to Scrape The Whisky Exchange UK Discount Data to monitor 95% of real-time whiskey deals, track price changes, and maximize savings efficiently.
Discover how AI-Powered Real Estate Data Extraction from NoBroker tracks property trends, pricing, and market dynamics for data-driven investment decisions.
Discover how Automated Data Extraction from Sainsbury’s for Stock Monitoring enhanced product availability, reduced stockouts, and optimized supply chain efficiency.
Score big this Navratri 2025! Discover the top 5 brands offering the biggest clothing discounts and grab stylish festive outfits at unbeatable prices.
Discover the top 10 most ordered grocery items during Navratri 2025. Explore popular festive essentials for fasting, cooking, and celebrations.
Explore how Scraping Online Liquor Stores for Competitor Price Intelligence helps monitor competitor pricing, optimize margins, and gain actionable market insights.
This research report explores real-time price monitoring of Amazon and Walmart using web scraping techniques to analyze trends, pricing strategies, and market dynamics.
Benefit from the ease of collaboration with Actowiz Solutions, as our team is aligned with your preferred time zone, ensuring smooth communication and timely delivery.
Our team focuses on clear, transparent communication to ensure that every project is aligned with your goals and that you’re always informed of progress.
Actowiz Solutions adheres to the highest global standards of development, delivering exceptional solutions that consistently exceed industry expectations