Complete Guide to Outsourcing Web Scraping: Options, Mistakes, and Best Practices

Key Takeaways:

• Businesses benefit from outsourcing web scraping when they struggle with in-house development and maintenance or want to cut costs at scale.
• Outsourcing options include freelance work, talent on demand, DaaS platforms, and fully managed scraping service providers.
• Managed services have the highest satisfaction score and are the best fit for complex, large-scale scraping projects.

Is your current data collection setup slow or constantly breaking? Or maybe you’re ready to grow but don’t know how to scale it up without breaking the bank? You might be ready for outsourcing web scraping.

In this article, we explain what to look for when choosing a provider and how to make sure your scraping solution sets you up for long-term success.

When Outsourcing Web Scraping Makes Sense for Businesses

Building a handful of parsers for an MVP or a small project is easy. This often makes teams think they can handle complex scraping pipelines just as well. However, developing a fully fledged solution your operations or decision-making can rely on requires more skills and resources.

Moreover, keeping your data pipeline running can get increasingly challenging as your needs grow. Here are a few signs it’s time to outsource web scraping:

1. Your internal team struggles to scale up scraping fast enough to support your business needs. Increasing headcount takes time and money to find and onboard the right people. Getting web scraping services can be a more suitable alternative.
2. Your scrapers keep breaking. Recurrent errors lead to costly downtime and slow down your growth, especially when fixes take time or shift your team’s focus away from other priorities.
3. New costs keep coming up that you didn’t plan for. Anti-bot measures evolve, making your team look for new scraping techniques. Your proxies gets blacklisted, and you try out new expensive providers. An outsourced team relies on its experience to solve new challenges faster and spreads its overhead across dozens of clients.
4. You need data from complex sources. Your tried-and-tested solutions can stop working when the anti-bot protections update or your business needs more information.
5. The current data quality doesn’t meet your needs. Your team shouldn’t spend time dealing with duplicates, wrong data types, and pagination issues. The right vendor can develop a scraper that doesn’t return empty fields and build additional validation and data processing layers.

Outsourcing can solve these problems with a solution tailored to your specific use case. Below is a breakdown of what hiring a professional team will mean for each stage of the scraping process compared to in-house data collection.

In-house

Outsourced

Tech team requirements

Engineering team needed

None

Expertise

In-house resources

Expert guidance

Setup

Built by your team

Built by vendor to your needs

Operation

Run by your team

Handled by vendor (even if scrapers were built by your team)

Time to first data

Weeks to months

Days to weeks

Anti-bot handling

Your responsibility

Included

Costs

Hard to predict

Quoted before launch

Infrastructure expenses

Proxies, servers, tooling

Included in scope

Maintenance and QA

Your responsibility

Can be handled by vendor

Scalability

Requires increasing headcount

Scope adjustment

If you’re considering in-house development because you need full control and customization, outsourcing can offer those, too. To sum up, here are the benefits you can get from getting web scraping done for you:

• lower operational costs;
• faster project delivery;
• access to deeper expertise;
• easier scaling;
• reliability.

All scrapers tend to break, but you can count on a professional team to fix the pipeline faster and let your in-house engineers focus on other priorities. To get all the benefits, you need to choose the right cooperation model for your use case.

Outsourcing Web Scraping Tasks: Options

If you’ve decided to get professional services, the market offers different models that vary by scale and your engagement level. Let’s look into the peculiarities of each one and the scenarios where they work best.

1. Freelance web scrapers

There are plenty of scraping specialists on Upwork or Fiverr ready to start working on your project. The main benefits include lower costs, an easy search process, and access to ratings and reviews that help you choose a professional.

However, freelance work is best for small, simple tasks. It can also fit a scenario where you have a scraper built that you plan to operate and maintain in-house. If you need a reliable partner for long-term development, freelancers are not the best option.

2. Talent on demand

This is a hybrid format that allows you to get a dedicated team or a single professional to work on your project alongside your engineers. It works for teams building a data pipeline in-house who need to delegate some of the heavy lifting.

It’s also a good starting point for your potential transition from internal development to outsourcing. Look for a company that offers on-demand talent, end-to-end services, and maintenance. If you’re satisfied with their work, you’ll be able to easily scale up cooperation without switching vendors.

3. Data-as-a-service providers

DaaS companies own proprietary scraping infrastructure and deliver pre-made or custom datasets on demand. In many cases, you can choose the delivery method and schedule.

This option works when you need to inform decisions once a quarter. If the data is the core of your product or business, you’ll require closer integration.

4. Managed web scraping services

This is the best way to outsource web scraping production for companies that need end-to-end services. A provider handles scraper development, proxy management, anti-bot challenges, data integration, pipeline monitoring, and continuous maintenance. You can use the data without dealing with technical challenges.

If you want to compare specific big industry names, read our article on the 10 Best Web Scraping Services.

According to a Deloitte study, managed services are growing in popularity compared to other outsourcing models. The satisfaction score of managed outsourcing has reached 88%. On the other hand, only 71% of surveyed organizations that don’t adopt managed services say outsourcing meets or exceeds their satisfaction levels.

Choosing the Right Web Scraping Outsourcing Provider

Getting end-to-end services means the quality of your entire pipeline depends on a single vendor. This makes it essential to find a reliable company that can handle your specific use case. To compare outsourcing web scraping tasks options, use the checklist below.

• Transparent tech stack that fits your needs. The vendor needs to be proficient in the tools necessary to access complex targets and connect the data to your system.
• Proven relevant experience. Look for case studies that include similar sources or industries and feature quantifiable outcomes.
• Niche or local coverage. Make sure the vendor understands the markets and regions you work in.
• Ability to handle your scale. If you need a complex solution fast, a freelancer probably won’t deliver.
• Flexible approach. It’s easier to work with a provider that adapts the solution and engagement model to your needs.
• All-in-one development. If you need scrapers and the data analysis tools built, it’s better to get them from a single provider.
• Authentic reviews. Check third-party review platforms where people with verified accounts share their feedback.
• Sample dataset available. It’s best to see what the output will look like before committing.
• Monitoring and maintenance options. Scrapers break more often than other software, so you need to plan for updates.

For more details on selecting the right provider, read our article —> How to Choose Data Scraping Vendor.

Web Scraping Outsourcing Cost

Your project cost depends on the number of sources you need to cover, their complexity, data processing requirements, and many other factors. Below are some of the elements you have to pay for no matter who does the development:

• Proxy infrastructure;
• Engineer time;
• Cloud computing;
• Browser licensing;
• CAPTCHA solving.

If you scrape in-house, it adds more expense categories. Below is a list of things you save on when opting for outsourcing:

• Learning curve. Providers gain expertise with day-to-day practice that your internal team lacks.
• Recruitment and onboarding when you launch and when it’s time to scale up.
• Opportunity costs. You don’t have to divert your team’s resources.
• Time before launch. Faster development cuts your time to market.
• Downtime. Professionals fix pipelines faster.

When scraping is not your specialty, it can be hard to predict the costs of a long-term project. Vendors provide a transparent, structured quote upfront, allowing you to plan your budget and adjust scraper characteristics accordingly.

Common Mistakes of Web Scraping Outsourcing

Despite all the benefits of outsourcing, challenges can arise. For smooth cooperation, we’ve prepared a list of common mistakes to avoid when hiring a vendor.

1. Getting the cheapest option

Cheap services are usually offered by teams who are starting out and still learning the peculiarities of scraping. The output can seem satisfactory at first. However, these providers often lack the ability to give advice and prevent future issues. To avoid risk, look for a more experienced vendor.

2. Opting for a pre-made solution

This is another option that seems cheaper when you start but leads to additional expenses in the long run. If you need more than occasional data file delivery, sooner or later you’ll run out of the customization options of an off-the-shelf tool. Get custom development unless you’re ready to build transformation layers and adapt your own systems and workflow to a subscription-based SaaS.

3. Getting into a vendor lock-in

Some providers build solutions that make it hard to switch to their competitors. For example, Apify offers custom development within their scraping platform. Migration is possible, but it will take additional time and effort.

4. Getting no IP rights

When you’re paying for custom development to outsource web scraping, it makes sense to own the resulting code. Make sure your contract details the rights of each party and specifies that your business logic and collected data can’t be reused or resold.

5. Skipping maintenance clauses

Scrapers aren’t something you can build once and keep using forever. Make sure the vendor you choose for development is ready to maintain the pipeline in the long run. Opt for companies that have been on the market for years and look into their maintenance packages before you commit.

6. Not assessing enough options

Custom development prices are too project-specific to publish, but quotes are usually free. Don’t hesitate to contact providers and see what they can offer in your specific case. You can get a custom quote from DataOx here.

What Is the Best Way to Outsource Web Scraping Production

The key is finding a model that works for your particular project. Let’s sum up the best practices to follow when selecting an outsourcing vendor and a collaboration approach.

1. Choose an engagement model that fits your use case

Freelance work and pre-built tools can work for small, one-off scraping projects like taking a snapshot of the market before opening a new store. When data is core to your business, go for managed services.

2. Keep your future goals in mind

Even if you don’t need a large-scale collection now, the scope can change. Leave room to scale up and add complex features. The best option is to find a provider with a diverse portfolio.

3. Plan for the whole pipeline, not just data extraction

You can find a vendor that can handle scraping, validation, data transformation, and integration into your existing system. End-to-end development delivers seamless pipelines and frees your team to work on other goals.

4. Pick experienced professionals

Check the portfolio to see the challenges they’ve already overcome and the outcomes their clients received. Bonus points if the vendor has public case studies in your particular industry.

Case Studies for Outsourcing Web Scraping from DataOx Expertise

DataOx provides fully managed web scraping services with custom scope. We have completed more than 300 projects across industries and achieved a 94% customer satisfaction score. Below are three examples of how our scraping services helped customers overcome their business challenges.

Case Study #1: Legal Data Processing

DataOx has prepared a data foundation for a legal tech startup that is now part of one of the industry leaders in the U.S.

Challenge

A legal tech project needed structured data to train an advanced AI engine. The training required the statutes and case law from all 50 U.S. states. The data was scattered across multiple sources and lacked consistent structure and machine readability.

Solution

DataOx built comprehensive infrastructure that included data collection, transformation, and delivery to the startup’s training engine. We worked with the client for 3 years, maintaining scrapers and ensuring data freshness.

Results

DataOx covered law from all 50 U.S. states, expanding beyond statutes to additional legal information sources. Consistent data structure and reliable feed powered innovative legal research and AI training.

Read more about this project in our legal tech case study.

Case Study #2: Global Organic Platform

DataOx has developed web scraping and real-time data processing infrastructure for a B2B marketplace connecting organic product suppliers with their clients.

Challenge

An organic ingredients provider struggled to check vendor certification data across dozens of regulatory websites. Manual data collection from different bodies was creating operational bottlenecks.

Solution

DataOx built a platform that pulls data from multiple global and regional certification systems, updating 500,000+ unique records monthly. It allows businesses to search for the right provider and use advanced supplier/buyer matching algorithms.

Results

• USDA, EU Organic, and international standards covered.
• Vendor search time reduced from several days to 15-30 minutes.
• Client companies close deals 3-5x faster.

Read more about this project in our organic trade case study.

Case Study #3: AI Recruitment Platform

DataOx has built an AI-powered application tracking system that helps small and mid-sized businesses automate recruitment.

Challenge

A recruitment automation company needed to develop and scale AI-powered tools, including their customizable interview guide generator.

Solution

DataOx developed a custom SaaS and built data integrations connecting popular platforms like Indeed, LinkedIn, and ZipRecruiter. We created an advanced onboarding system and have been maintaining the solutions for more than 8+ years.

Results

• 900K+ candidates in the system.
• 680K candidates acquired through platform integrations.
• 1K new client companies added annually.
• 120K assessments completed in the last year.

Read more about this project in our AI recruitment case study.

DataOx has been building data solutions across industries since 2015, but most of our projects stay private under NDAs. Contact us to discuss your needs, and we’ll help you find the best way to outsource web scraping production at any scale.