Web data infrastructure is vital to AI systems
AI-driven systems are only as good as their data. Improper infrastructure can leave your data looking dirty, noisy, stale and unbalanced. Then, you’re left with a model that’s dirty, noisy, stale and unbalanced. Perhaps it’s entertaining for a minute. Then reality creeps in. You need your AI systems to perform their tasks correctly.
Whether it’s product listings, financial markets, latest news or social trends, you need a pipeline capable of preparing and delivering that data to your AI model. In this article, we’ll discuss what this entails and different methods of accomplishing it.
Once you’ve finished reading this piece, you should be able to answer the following questions.
- What is in-house web data infrastructure?
- What is managed web data infrastructure?
- What are the costs of each approach?
- When does managed web data infrastructure make sense?
The truth about in-house infrastructure
In-house infrastructure is how most projects begin. Maybe a scraper running on your home internet connection. Eventually, you upload the code to a personal server with scheduled cron jobs. Your app begins to scale and now your AI models need access to fresher, on-demand data. A server that once cost $5 per month is now costing you hundreds. Since you own the infrastructure, you can change it however you’d like. Let’s take an in-depth look at how all this works.
Granular control
In-house infrastructure gives you control over everything. You decide which data sources to use. Your team writes the scrapers. You decide how often to run them. When your project requires highly specialized data, in-house infrastructure is likely right for you.
Your level of control also extends to performance. You can optimize your system for speed, storage or cost depending on the demands of the project. You can experiment with different parsing methods and filters for data quality. You don’t need to wait on a vendor for anything. When your infrastructure is 100% yours and so are the decisions. Your real limitations lie in the resources available to you — development, bandwidth, budget and your imagination.
Hidden costs
When developing this stuff at home or on a local workstation, operational costs are almost free. As you scale, the costs really begin to show. Below are things that most teams and project managers don’t think of immediately when designing their web data infrastructure.
- Proxies and unblocking: The minute you move your scraper to the cloud, it’s now operating with a datacenter IP address. It’s probably going to get blocked. To get around this, you need an unblocking solution.
- Technical debt: When you’ve got total control over your software, you’re responsible for maintaining it. Rather than building new products, developers are often bogged down patching broken scrapers, updating schemas and performing other menial tasks — and you’re paying them for it.
- Scaling costs: As your product scales, it needs more hardware. Cloud hardware isn’t cheap. As time goes on, developers can optimize your code when they’ve got free time but optimizations only go so far. The more your pipeline is used, the more it’s going to cost, period.
- Opportunity cost: This is the invoice for technical debt. Rather than building new products, your development is busy maintaining legacy code and patching breaks in the pipeline. They’re no longer builders, they’re really expensive janitors.
Is managed web data infrastructure the solution?
With control comes stress. Managed web data infrastructure offers relief from that stress. Instead of patching scrapers and scaling servers on your own, you let the provider worry about this.
It’s a really simple idea. Let your builders focus on building. Let someone else take care of the plumbing. You don’t lose all flexibility. Most providers offer various forms of customization. Providers like Bright Data and Firecrawl even offer options with custom schema.
Managed solutions aren’t perfect. You still need to pay the provider. You are limited to their offerings. However, if your developers are always on cleanup duty, managed web data infrastructure could absolutely be the solution you’re looking for.
What is managed web data infrastructure?
With managed web data infrastructure, you know what you’re getting. Somebody else takes care of the backend and plumbing. They keep the data flowing. Your job is now to receive the data and move on from there — maybe some postprocessing or even annotation but some providers offer these services as well. We can think of managed web data infrastructure the way we think of personal cloud storage.
With cloud storage, physical backups aren’t an issue. Sure, you need to pay for the storage. However, nothing in your day-to-day life has the power to destroy it forever. The same can be said for managed web data infrastructure. If your power goes out, no worries. Cloud scrapers are still going to run. Lost your laptop in a freak boating accident? Your crypto is gone but your data pipeline isn’t.
Hidden benefits of managed web data infrastructure
The most obvious benefits of managed web data infrastructure are convenience and stress relief. Many other benefits get overlooked when we think about this one-dimensionally.
Here are some of the most enticing benefits that managed web data infrastructure has to offer.
- Speed to market: Your development team is no longer bogged down with the data pipeline. They have every worker’s dream: Free time. They can use this free time to build new products powered by your AI and data driven backend. What would’ve taken a year might now take just a few weeks.
- Predictable cost: Managed web data infrastructure providers offer predictable pricing plans. If you plan things right, you can calculate your scaling costs with accuracy. Once your growth has plateaued, it’s basically a monthly subscription fee — no surprises.
- Reliability: Nobody needs to troubleshoot broken scrapers. Providers have their own teams whose sole focus is the data pipeline. When schemas are updated, they’ll often send you an email or alert well ahead of time so you can adjust. The data comes when you need it — as predictably as municipal water.
Trade-offs when using managed web data infrastructure
Managed web data infrastructure isn’t free. Instead of getting ambushed by hidden costs, you’re trading for visible ones in the form of fees, contracts and limitations.
- Cost: You’re saving time and labor. However, your predictable costs add up. Unblocking services can cost as much as $0.001 per request. Residential proxies can cost up to $8/GB.
- Vendor dependency: Once you rely on a single provider’s Application Programming Interface (API) or Software Development Kit (SDK), you’re partially dependent. Migration likely requires a rewrite in the codebase.
- Limited flexibility: You’re limited by what your provider offers. If you need something really outside-the-box, you’ll likely still need to build it in-house.
- Trust and dependency: Your uptime is almost guaranteed. Your Service-Level Agreement (SLA) isn’t. When you file a support ticket, you can’t just fire off an email to your development team. You’re dependent on the provider’s support team.
All things considered, generative AI can greatly assist with provider migration. A properly trained generative model can take new input data structures and reformat them to fit your pipeline. If your SLA matches your needs, support will come. Most providers do offer flexibility such as geotargeting and even custom-built scrapers.
How they stack up side by side
| Factor | In-house infrastructure | Managed infrastructure | Example providers |
|---|---|---|---|
| Cost | Starts cheap, scales into hundreds or thousands as usage grows | Predictable pricing (subscriptions/usage-based), but convenience comes at a premium | Bright Data, Snowflake, AWS Data Exchange |
| Maintenance | Your team patches scrapers, updates schemas, handles outages | Provider maintains pipelines, proxies, and updates | Bright Data, Zyte, ScraperAPI |
| Scalability | Requires more hardware, bandwidth, and optimization as you grow | Scales automatically with provider’s backend | Scale AI, Appen, Defined.ai |
| Developer time | High opportunity cost — builders become “expensive janitors” | Freed up to build products and models | Anyverse, Mostly AI |
| Reliability | Fragile: one broken parser can break the chain | High uptime backed by dedicated teams and SLAs | Bright Data, AWS Data Exchange |
| Flexibility | Unlimited customization (within your resources) | Flexible only inside provider’s boundaries | Bright Data, Scale AI |
Conclusion
Web data infrastructure is the lifeblood powering most AI applications. Without reliable web data, AI agents can’t make properly informed decisions. Virtual assistants can’t answer some of your most basic questions. Regardless of how you get it, your AI systems need fresh web data and web data infrastructure does that.
Whether you’re using in-house data pipelines or fully managed web data infrastructure to feed the system, you need some type of infrastructure. If you’re looking for peace of mind, faster development and predictable cost, managed web data infrastructure might just be right for your team.