Is It Worth Hiring a Data Team or Outsourcing Web Scraping?

How to Track Thousands of Competitor Prices Without Burning Out Your Team

The Price-Tracking Dilemma

In today’s fast-paced markets, staying on top of competitors’ prices is critical for retailers, e-commerce companies, travel firms, and beyond. Prices online can change by the minute for instance, Amazon reportedly makes over 2.5 million price changes per day. For a business with thousands of products, tracking these fluctuations across competitors is a massive undertaking. The question many enterprise decision-makers face is how to get this done without overloading their teams. Do you build an in-house data scraping team to gather and monitor competitor pricing, or do you outsource the job to a specialized web scraping service?

This blog will break down the pros and cons of each approach in clear, non-technical terms. We’ll explore the operational challenges, financial costs, and resource demands of building an internal web data team versus partnering with an external scraping provider. Real-world examples from retail, e-commerce, and travel will illustrate how each option plays out. Our goal is to help you make an informed decision on the best path to collect competitive pricing data without burning out your team or blowing up your budget.

The Challenges of Tracking Thousands of Prices

Monitoring competitor prices at scale isn’t as simple as setting a Google Alert or checking a few websites manually. Companies often start with small internal projects or manual checks, but as the scope grows to hundreds or thousands of products, the effort can quickly snowball into a full-time job. Consider some common hurdles businesses encounter:

• Constant Price Changes: As noted, major online players like Amazon change prices relentlessly (millions of times a day). Even smaller competitors may update prices daily or run flash sales with little notice. Keeping up manually is impractical. If you miss a competitor’s price drop, you could be caught flat-footed in the market.

  
  

• Frequent Website Updates: Websites don’t stay static. A retail competitor might redesign their product pages or tweak their HTML structure, causing any homegrown scraping scripts to break. If your system isn’t flexible or quickly adjustable, you’ll lose data until fixes are made. This means your team must constantly maintain and update any tools built in-house to handle site changes.

  
  

• Anti-Scraping Measures: Many websites deploy defenses against automated data collection – for example, showing CAPTCHA tests, blocking multiple requests, or requiring logins. Gathering data at scale often requires technical workarounds like managing rotating IP addresses (proxies) and using headless browsers (invisible automated browsers) to simulate human browsing. These technical tricks can be complex to implement and maintain. Without specialized expertise, an internal team can struggle with frequent blocks or incomplete data.

  
  

• Data Overload and Quality Control: Tracking thousands of prices means dealing with large volumes of data. An internal process must include quality checks (to remove errors or duplicates) and a pipeline to funnel the data into your databases or pricing systems. If done haphazardly, it’s easy to get overwhelmed or make mistakes that lead to bad data – which in turn can lead to poor decisions.

  
  

• Strain on Your Team: Perhaps the biggest challenge is the human factor. Manually collecting or even semi-automating data for countless products can exhaust your staff. We’ve seen cases where data scientists and analysts end up spending more time maintaining web-scraping scripts than analyzing the data for insights. In other cases, a project that started small grows in scope, and engineers who built a quick solution in their spare time now can’t keep up with the maintenance workload. This kind of continuous firefighting can lead to employee burnout – your team’s energy gets drained by endless data wrangling rather than high-value strategic work.

  
  

These challenges are real, but they can be addressed. The solution boils down to two strategic choices: invest in an in-house data scraping team and infrastructure, or outsource the problem to a professional web scraping service. Let’s examine each path in detail, focusing on what it means for your operations, budget, and people.

Building an In-House Data Web Scraping Team

Many enterprises initially lean toward keeping data collection in-house. It seems straightforward: you have proprietary needs and maybe sensitive data; why not have your own employees build and run the price-tracking system? An in-house approach certainly has its advantages:

• Full Control & Customization: You can tailor every aspect of the data collection to your exact requirements. If you need to capture a very specific piece of information or run the process at certain times, an internal team can tweak the tools on the fly. You’re not sharing infrastructure, so everything can be built around your business needs.

  
  

• Data Security: Keeping the process internal means sensitive competitive data and business intelligence stay within your company’s walls. For industries like finance or healthcare, where privacy is paramount, having an in-house system might feel safer from a governance perspective. There’s no third party handling your data, which mitigates certain security and privacy concerns about outsourcing.

  
  

• Institutional Knowledge & Skill Growth: Over time, your team can develop deep expertise in web scraping and data engineering. The skills they build could become an internal asset, benefiting other projects. You’re essentially investing in the technical growth of your staff, which can pay off if data collection is core to your competitive strategy.

However, these benefits come with significant costs and challenges. Before committing to building an internal web scraping capability.

"In-house web scraping sounds appealing at first—but the reality is it gets complicated fast. Websites can block crawlers, structures vary widely, and maintaining your own tools, servers, and databases is no small task.

At Ficstar, we’ve configured thousands of websites across platforms. We handle everything from crawling to data delivery—often within days—not weeks. That saves our clients time, cost, and a whole lot of headaches."

— Scott Vahey, Director of Technology at Ficstar

Consider the following cons:

• High Upfront Investment: Setting up an in-house web scraping operation is not cheap. You’ll likely need to hire specialized talent, such as data engineers or developers familiar with web scraping techniques or divert existing engineering resources to the project. The hiring process itself takes time and money, and salaries for experienced data professionals are substantial (often in the high five to six figures annually per person). Beyond people, you need infrastructure: servers or cloud computing resources to run the scrapers, tools for parsing data, storage for the large datasets, etc. All this requires a significant upfront budget outlay before you even see results.

  
  

• Ongoing Maintenance & Upkeep: Web scraping is not a “set and forget” operation. Websites change, as we noted, and anti-bot measures are always evolving. An internal team must continuously maintain and update your scraping tools and scripts to keep data flowing. That means fixing things when a site redesigns its layout, adjusting to new blocking tactics, updating software libraries, and so on. This maintenance is a never-ending effort and can consume considerable team bandwidth. If your web scraping infrastructure started as a quick proof of concept, it may not scale easily engineers might spend more time debugging and patching than innovating.

  
  

• Scalability Limits: What if your data needs double or triple in short order say you expand your product catalog or enter a new market with new competitors to track? Scaling an in-house solution isn’t just flipping a switch. You might need to add more servers, optimize code, or hire additional staff to handle the increased load. Rapid scaling can be challenging and expensive when you’re doing it alone. Companies often find their in-house systems work for smaller scopes but start to lag or crash when the volume ramps up.

  
  

• Diversion from Core Business: Every hour your IT team or data scientists spend on scraping competitor prices is an hour they aren’t spending on your core business initiatives. For many companies, web data collection is a means to an end, not a core competency. If you’re a retailer, your core might be merchandising and marketing – not running a mini web-scraping tech operation. Building an in-house team can inadvertently pull focus away from strategic projects. As one analysis noted, it diverts resources in terms of both money and attention, which can be costly in opportunity terms.

  
  

• Risk of Team Burnout: Maintaining a large-scale data operation in-house can be intense. If the team is small, they may end up on call to fix scrapers whenever they break, including late nights or weekends if your business demands continuous data. Over time, this firefighting mode can hurt morale and retention. It’s worth asking: do you want your talented analysts or engineers spending their days (and nights) wrestling with scraping tools and proxy servers? For most organizations, that kind of grind leads to burnout, which is exactly what we want to avoid.

  
  

It’s not that an in-house team can’t work, many big enterprises eventually build robust data engineering teams. But the true cost can be much higher than it appears at first glance. In fact, industry experts have noted that the total cost of hiring and maintaining a data team is often prohibitive for smaller companies and a major investment even for large ones. Unless your business has unique needs that absolutely require a custom-built solution (and the deep pockets to fund it), it’s worth carefully considering if the benefits outweigh these challenges.

Outsourcing Web Scraping to a Service Provider

The alternative is to outsource your web scraping and price tracking to a specialized service provider. There are companies (like Ficstar, among others) whose core business is exactly this: collecting and delivering web data at scale. Outsourcing can sound risky at first after all, you’re entrusting an external firm with a task that influences your pricing strategy. But for many enterprises, the advantages of outsourcing outweigh the downsides. Here’s why outsourcing is an attractive option:

• Lower Upfront and Ongoing Costs: Perhaps the biggest draw is cost-effectiveness. Outsourcing eliminates the heavy upfront investments in development, infrastructure, and hiring. A good web scraping service will already have the servers, software, and experienced staff in place. Typically, you’ll pay a predictable subscription or per-data fee. While it might seem like an added expense, compare it to the salary of even one full-time engineer plus hardware/cloud costs, outsourcing often comes out significantly cheaper, especially for sporadic or fluctuating needs. You also save on ongoing maintenance costs; the provider handles updates and fixes as part of their service.

  
  

• Access to Expertise and Advanced Tools: Web scraping at scale is this industry’s bread and butter. Outsourcing means you get a team of specialists who have likely seen and solved every scraping challenge out there – from dealing with tricky CAPTCHA roadblocks to parsing dynamic JavaScript-loaded content. They also maintain large pools of proxy IPs and headless browsers so you don’t have to worry about the technical nitty-gritty. This technical expertise means higher success rates and more robust data collection. Essentially, you’re hiring a ready-made elite data team (for a fraction of the cost of hiring internally). 

  
  

• Scalability and Flexibility: Data needs aren’t static – you might need to ramp up during a holiday season or pause certain projects at times. Outsourcing offers far greater flexibility in this regard. Need to track double the number of products next month? A large service provider can scale up the crawling infrastructure quickly to meet your demand. Conversely, if you scale down, you’re not stuck with idle staff or servers – you can adjust your contract. This elasticity is hard to achieve with an in-house setup without over-provisioning (which costs money). Providers often serve multiple clients on robust platforms, so they can accommodate spikes in workload more easily. In short, you get on-demand scalability without long-term capital commitments.

  
  

• Speed to Implementation: Getting started with an outsourcing partner can be much faster than building from scratch. Providers often have existing templates and systems for common use cases (like retail price monitoring). Once you define what data you need, they can onboard you and begin delivery quickly – sometimes within days or weeks. In contrast, hiring and training an internal team, then developing a solution, could take months before you see reliable data.

  
  

• Operational “Peace of Mind”: When you outsource to a reputable service, you shift a lot of operational burden off your plate. The provider is responsible for dealing with site changes, broken scrapers, IP bans, and all those hassles. Your team can focus on analyzing the data and making decisions, rather than on the mechanics of data gathering. As one web data provider put it, they bring in the expertise and relieve businesses from the burden of developing and constantly fixing these capabilities internally. This can significantly reduce stress on your organization. No more panicked mid-week scrambles because a website tweak stopped the data flow – the service team handles it behind the scenes.

  
  

Of course, outsourcing isn’t a magic bullet without any considerations. Here are a few potential downsides or risks to weigh:

• Less Direct Control: When an external party is collecting data for you, you have to relinquish some control. You might not be able to dictate every minor detail of how the data is gathered. If you have very unique requirements, you’ll need to ensure the vendor can accommodate them. Good providers will offer customization, but it may not be as infinite as having your own team at the keyboard. Mitigate this by setting clear requirements and maintaining open communication channels with the provider. Many enterprise-focused scraping companies assign account managers or support teams to work closely with clients, which helps maintain a sense of control and responsiveness.

  
  

• Data Security and Compliance: You are trusting an outside firm with your competitive intel and possibly with access to some of your systems (for delivery or integration). It’s important to choose a provider with strong security practices. Ensure they comply with data protection regulations and handle the data ethically and legally. Reputable providers will emphasize compliance – for example, they’ll respect robots.txt rules, manage request rates to avoid disrupting target sites, and avoid scraping personal data. Always vet the provider’s security standards and perhaps avoid sending highly sensitive internal data their way if not necessary. In many cases, the data being scraped (competitor prices on public websites) is not confidential, so the risk is relatively low, but due diligence is still key.

  
  

• Dependency on a Third Party: Outsourcing means you are to some extent dependent on the service provider’s stability and performance. If they have an outage or issues, it could impact your data deliveries. To mitigate this, pick a well-established provider with a reliable track record, and consider negotiating service-level agreements (SLAs) that include uptime and data quality guarantees. Diversifying (using multiple data providers or having a small in-house capability as backup) is another strategy some enterprises use, though it adds cost. Generally, leading providers know their reputation hinges on reliability – often more so than an internal ad-hoc team might.

  
  

For most organizations whose primary business is not data collection itself, the outsourcing route is highly advantageous. It allows you to leverage state-of-the-art data gathering techniques and expert personnel without having to build or manage those resources yourself. In other words, you get to focus on using the pricing data to make decisions (your actual job), rather than on the laborious process of obtaining that data.

Operational, Financial, and Resource Considerations

Ultimately, the decision between in-house and outsourcing comes down to what makes sense for your operations, finances, and team resources. Let’s summarize the key considerations across these dimensions:

Operational Impact:

• In-House: You manage the entire operation. This gives you fine-grained control, but also means handling all the headaches, site changes, broken scrapers, scaling server loads, etc. If your industry has very custom needs, in-house might integrate better with your workflows. But be realistic about the ongoing operational effort. Do you have a plan for 24/7 monitoring? Backup systems? Those will be on you.

  
  

• Outsourced: Much of the operation is handled by the provider. They typically ensure the data pipeline runs smoothly and resolve issues proactively (often before you even notice them). Your operational involvement is more about vendor management – setting requirements, reviewing data quality, and coordinating changes when your needs shift. If web scraping is not a core competency you want to develop, outsourcing removes a major operational burden from your plate.

Financial Considerations:

• In-House: There’s a significant fixed cost investment upfront, and ongoing variable costs for maintenance. Salaries, benefits, training, infrastructure, and possibly software licenses all add up. As one source put it, the total cost can be outright prohibitive for many businesses. If budgets are tight or unpredictable, this route can be risky – you don’t want a half-built data project because funding was insufficient. However, if you already have a large IT budget and staff with available time, you might repurpose some existing resources (though be cautious of stretching your team too thin).

  
  

• Outsourced: Typically involves a predictable recurring cost (monthly or usage-based fee). This can often be treated as an operating expense. It scales with your needs – if you need more data, costs will rise but ideally in proportion to the value you gain. In many cases, outsourcing is more cost-effective, especially at scale, because you’re sharing the provider’s infrastructure and efficiency across clients. You pay for what you need, when you need it, rather than investing in capacity you might not use all the time. From a budgeting standpoint, it can be easier to justify a subscription fee tied to clear deliverables (data delivered) versus the nebulous ROI of an internal team that might take months to fully ramp up.

  
  

Resource and Talent Factors:

• In-House:  You’ll need to recruit, train, and retain a team with the right skill set. This might include web developers, data engineers, or data scientists familiar with web technologies. The talent market for these skills is competitive. Once hired, keeping them motivated on web scraping tasks (which can be repetitive or frustrating due to constant website defenses) might be challenging. There is also the risk that if a key team member leaves, your project could be stalled – all the knowledge about those custom scripts can walk out the door with an employee. On the flip side, building an internal team means those people can potentially take on other data projects as well, providing flexibility if your priorities change (they’re not tied only to price tracking).

  
  

• Outsourced:  You’re tapping into an existing talent pool – essentially “renting” the expertise of a full team that the provider has assembled. You don’t have to worry about hiring or turnover in that team; the provider handles that. Your internal staff can be smaller, focusing on core analysis rather than the data gathering grunt work. This can relieve your analysts and managers from a lot of extra hours. As one case in point, businesses have found that by outsourcing, their internal experts can spend time deriving insights from data instead of wrangling data extraction tools, leading to better morale and productivity. The trade-off is that you won’t have that scraping expertise in-house; if someday you decide to bring it in-house, you’d be starting from scratch on the talent front.

  
  

Speed and Time-to-Value:

• In-House: Be prepared for a potentially slow ramp-up. Even after hiring, building robust scrapers and pipelines can take significant development and testing time. It might be months before you have a reliable stream of competitor data coming in, and during those months you’re flying partially blind. If speed is crucial – say you need a solution live before your next big pricing season – this is a serious consideration.

  
  

• Outsourced: As mentioned, you can usually onboard faster. Providers often have pre-built capabilities for common needs. The time from kickoff to receiving data could be very short, meaning you start getting ROI faster. This can be a decisive factor if your competitors are already using advanced pricing tools and you need to catch up quickly.

  
  

Example

Retailer Scenario:

Imagine a large online retailer with 50,000 SKUs (products) that wants to monitor prices at 5 major competitors daily. An in-house team would need to build scrapers for each competitor site (which might each have different site structures, categories, etc.), run them every day, handle login or anti-bot measures if required, then integrate that data into the retailer’s pricing system for analysis. This is doable, but consider that each competitor site could take significant engineering effort to scrape correctly. If two of those sites change their layout in the same week, the team scrambles to fix scripts instead of analyzing why competitor prices changed. Over a year, the internal team may find themselves perpetually playing catch-up, possibly missing critical pricing moves by competitors during downtime. Now consider outsourcing: the retailer contracts a web scraping service. The service already has experience scraping similar retail sites and can adapt quickly. If a site changes, they likely detect it and deploy a fix before the retailer even notices a gap. The data feeds arrive on schedule each day in the format needed, and the retailer’s pricing analysts can trust that the grunt work is handled. The analysts can focus on strategizing responses to price changes (like adjusting their own promotions or alerting category managers), rather than troubleshooting data gaps. In this scenario, outsourcing not only prevents team burnout but arguably leads to better competitive response because the retailer is consistently informed.

Travel Industry Scenario:

Consider a travel aggregation company that needs airfare and hotel price data from hundreds of sources (airlines, hotel chains, booking sites). Prices in travel are incredibly dynamic – airlines change fares multiple times a day, and hotel rates fluctuate with demand. An in-house approach here would mean building a complex system that navigates different booking websites (some may not even be easily scrapable without headless browser automation due to heavy JavaScript). The company would need a team on standby 24/7 – because travel pricing doesn’t sleep – to ensure data is fresh. The complexity is high: dealing with captchas, rotating proxy IPs to avoid IP blocking, parsing data that might be loaded asynchronously, etc. This could quickly overwhelm a small data team. By outsourcing to a firm specializing in travel data collection, the aggregator can offload those complexities. The provider likely has a cloud infrastructure to run browsers that simulate user searches on these sites, has a bank of IP addresses globally to distribute requests, and knows the tricks to avoid captchas or can solve them efficiently. They deliver continuously updated price feeds to the aggregator, who can then focus on displaying deals or calculating insights (like “prices are trending up for summer travel”). The internal team is freed from low-level technical battles and can concentrate on partnerships and product development. In an industry as time-sensitive as travel, the reliability and focus that outsourcing brings can be a game-changer.

Finding the Right Balance

Every business is unique, and the decision to build an in-house data team or outsource web scraping should align with your strategic priorities, budget, and capacity. For some large enterprises with deep pockets and data at the core of their operations, investing in an in-house web scraping team could make sense – it offers maximum control and can be integrated tightly with internal systems. However, as we’ve outlined, that route requires a significant, ongoing commitment in money, time, and talent. Many companies underestimate these demands and find themselves facing stalled projects or burnt-out teams.

Outsourcing, on the other hand, has emerged as a practical solution for many mid-size and large businesses to get the data they need without the heavy lifting. It turns a complex technical challenge into a service that can be purchased – much like cloud hosting replaced the need for every company to maintain its own servers. By leveraging a specialized web scraping provider, you tap into economies of scale and expert knowledge that would be costly to replicate internally. Your organization can stay focused on its core mission (be it selling products, delivering services, or innovating in your domain), while still reaping the benefits of timely, high-quality competitor price data.

In deciding which path to take, ask yourself:

• Is having a bespoke, internally-controlled data system a competitive differentiator for us, or can we rely on a third party?

• Do we have the appetite to invest heavily in the people and tech needed long-term, or would we rather treat this as an operational expense?

• How urgent is our need for data, and can we afford the time to build in-house?

• Are our internal teams at risk of burnout if we add this responsibility to their plate?

  
  

For many enterprise decision-makers, the answer becomes clear that outsourcing web scraping is not about giving up control, it’s about gaining efficiency and reliability. It’s a way to track thousands of competitor prices, even in real-time, without exhausting your team’s bandwidth. The right data partner will work as an extension of your team, handling the dirty work of data collection while you concentrate on strategy and execution.

In summary, hiring a data team vs. outsourcing web scraping is a classic build-vs-buy decision. Consider the full spectrum of costs and benefits discussed above. If you choose to build internally, go in with eyes open and ensure leadership is committed to supporting the effort continuously. If you choose to outsource, do your due diligence in selecting a trustworthy provider and set up a strong collaboration framework. Either way, by making an informed choice, you’ll position your company to harness competitor pricing data effectively – giving you the insights to stay competitive, all while keeping your team sane and focused. In the end, the goal is the same: enable your organization to make smarter pricing decisions without burning out your team in the process.