How often can you run a web scraping crawler?

In theory, a crawler can run as often as you want. In practice, the best frequency depends on how quickly the underlying data changes, the site’s rate limits and anti-bot protections, the complexity of the workflow (logins, location/ZIP, add-to-cart), and the level of QA and reliability you need.

Is hourly crawling always better than daily crawling?

Not always. Higher frequency increases the risk of rate limiting, blocking, and noisy or inconsistent data—especially on complex sites. If your team can’t act on intraday changes, hourly data can add cost without improving decisions.

What’s the most common crawl frequency for competitor pricing?

Many teams use daily crawling for broad coverage and add intraday crawling (for example, 2–6 times per day or hourly) for high-impact competitors, SKUs, or regions where prices change frequently.

How do I choose the right crawl frequency for my use case?

Start with the business decision the data supports (weekly pricing strategy vs. intraday reactions), measure how often the data truly changes, then set a tiered cadence: frequent runs for high-volatility, high-value items and less frequent runs for the long tail.

Can I use different crawl frequencies for different sites or products?

Yes—and it’s usually the best approach. Use tiered frequency: hourly or multiple times per day for high-volatility targets, daily for most competitors, and weekly for low-change sources or long-tail catalogs. You can also increase cadence temporarily during promotions or peak seasons.

What limits how frequently a crawler can run?

The main constraints are site rate limits, anti-bot defenses, page-load complexity (such as heavy JavaScript), workflow requirements (logins, location simulation, add-to-cart), and operational needs like monitoring, retries, and QA to prevent silent data errors.

How does crawl frequency affect data quality?

More frequent crawling increases exposure to transient issues (timeouts, A/B tests, inconsistent pricing displays) and can amplify errors unless you have strong validation. Higher frequency should be paired with automated QA checks, anomaly detection, and reprocessing workflows.

Should crawl frequency increase during promotions or seasonal peaks?

Often, yes. Promotions compress the window where pricing data is valuable. Many teams run a normal cadence (such as daily) and switch to burst mode (multiple times per day or hourly for priority items) during major promo periods.

What’s a good starting frequency if I’m unsure?

A common starting point is daily crawling for full coverage, then adding intraday crawling for a small set of priority competitors or sentinel SKUs. Use a short baseline study (1–2 weeks) to measure true change rates and refine the cadence.

When does it make sense to work with a managed web scraping provider?

If you need high-frequency runs across multiple complex sites with consistent delivery and defensible data quality, a managed provider can handle monitoring, anti-bot adaptation, change management, SLAs, and QA (including anomaly detection and auditing) so you can rely on the data operationally.

Web Scraping Cadence 101: What Determines How Frequently You Can Crawl a Website?

Raquell Silva
9 hours ago
6 min read

Web scraping dashboard illustration with crawler, servers, and timing elements showing crawl frequency decisions.

What Is the Frequency We Can Run the Crawler?

Crawler frequency (how often we collect the same data from the same sources) is one of the first decisions that determines cost, feasibility, and data quality in a web scraping program.

In theory, you can run a crawler as often as you want. In practice, the “right” frequency is a balance between:

How fast the underlying data changes: price, inventory, availability, promotions, fees
How much risk you can tolerate: missing changes vs. getting blocked or rate-limited
How much complexity exists in the collection workflow: logins, location/ZIP, add-to-cart logic, anti-bot defenses, variant logic
How reliable you need the output to be: SLAs, QA requirements, anomaly detection, auditability
How much you’re willing to invest: infrastructure, maintenance, monitoring, change management

Infographic titled ‘Deciding Crawler Frequency’ showing five stacked sections. The sections explain: 1) how fast the underlying data changes (price, inventory, availability, promotions, fees); 2) how much risk you can tolerate (missing changes versus being blocked or rate-limited); 3) how much complexity exists in the collection workflow (logins, location or ZIP, add-to-cart logic, anti-bot defenses, variants); 4) how reliable the output needs to be (SLAs, QA, anomaly detection, auditability); and 5) how much you’re willing to invest (infrastructure, maintenance, monitoring, change management). A footer notes these factors help balance freshness, stability, and cost.

Below is a practical way to think about frequency. What makes it easy, what makes it hard, and how to decide.

What “Frequency” Really Means

When teams say “run it hourly,” they usually mean a bundle of requirements:

Refresh cadence: every X minutes/hours/days
Coverage: how many sites, SKUs, locations, and variants per run
Latency: how soon after a change you need it reflected (near-real-time vs next-day)
Reliability: how often the job can fail without business impact
Validation: how strict QA must be (field-level validation, reconciliation, sampling, anomaly rules)
Delivery: how quickly the data must land in your environment (API, S3, database, BI tool)

A “daily run” that collects list prices for 50 SKUs from one site is not comparable to a “daily run” that collects ZIP-level, add-to-cart, fees-included pricing across 30 competitors and thousands of SKUs.

The Core Tradeoff: Freshness vs Stability

As you increase frequency, you also increase:

Request volume (more pages, more sessions, more retries)

Blocking pressure (rate limits, bot defenses, CAPTCHAs, fingerprinting)

Site-change exposure (more opportunities to hit UI experiments, A/B tests, layout changes)

QA workload (more data to validate, more anomalies to triage)

Operational load (monitoring, alerting, incident response, reprocessing)

So the real question becomes:

What frequency produces business value without creating operational chaos?

How to Decide Frequency: A Simple Decision Framework

1) Start with the business use case (what decisions depend on this data?)

Common enterprise pricing use cases map to different cadences:

Price indexing / weekly strategy → weekly or 2–3x/week

Promo tracking / competitive response → daily (sometimes 2x/day)

Dynamic pricing categories (tickets, travel, some marketplaces) → hourly to near-real-time

MAP compliance / audit trails → daily or weekly (depending on enforcement needs)

Assortment + availability monitoring → daily (or more during peak seasons)

If your pricing decisions update weekly, collecting hourly often just creates noise and cost.

Read: The Hidden Cost of Web Scraping: What You Don’t Know Beyond the Basic Cost

2) Measure how often the target data actually changes

Before committing to “hourly,” do a short baseline:

Sample the same set of items multiple times per day for 1–2 weeks
Quantify:
- % of items with price changes per day
- average magnitude of change
- time-of-day clustering (do changes happen at 12am, 6am, random?)
- promo windows (weekends, holidays, flash sales)

If only 3–5% of SKUs change daily, you might do daily for full coverage and hourly for a small “sentinel set.”

3) Factor in source constraints (the website decides what’s realistic)

Some sites are “friendly” to stable crawling. Others are hostile:

heavy JavaScript rendering
frequent UI changes

geo-based pricing requiring ZIP/location simulation

add-to-cart required to see true price (fees, shipping, discounts)

aggressive bot defenses and session fingerprinting

Frequency should reflect not just your desire for freshness, but the operational reality of each source.

4) Use tiered frequency (not one cadence for everything)

Most enterprise programs end up with a hybrid model:

Tier A (high volatility / high value): hourly or 2–4x/day
Tier B (medium volatility): daily
Tier C (low volatility / long tail): weekly
Event-based bursts: increase cadence during major promos, seasonal peaks, or competitor campaigns

This is how you control cost while still capturing competitive movement.

Frequency by Complexity Level (With Your Examples)

Simple

At this level, the task involves scraping a single well-known website, such as Amazon, for a modest selection of up to 50 products. It’s a straightforward undertaking often executed using manual scraping techniques or readily available tools.

Typical viable frequency: daily → multiple times per day

Why it works: limited scope, fewer failure modes, simpler QA, manageable monitoring

What usually breaks first at higher frequency: IP rate limits, dynamic page elements, inconsistent pricing display vs checkout

Standard

The complexity escalates as the scope widens to encompass up to 100 products across an average of 10 websites. Typically, these projects can be efficiently managed with the aid of web scraping software or by enlisting the services of a freelance web scraper.

Typical viable frequency: daily (sometimes 2x/day)

Why: more sources = more variability; maintaining stability becomes the main job

What drives the decision: change rate across competitors, importance of same-day response, and how often sites change layouts

Complex

Involving data collection on hundreds of products from numerous intricate websites, complexity intensifies further at this level. The frequency of data collection also becomes a pivotal consideration. It is advisable to engage a professional web scraping company for such projects. A professional web scraping service provider is recommended for this complexity level.

Typical viable frequency: daily for full coverage + intraday for priority subsets

Why: at scale, the constraint becomes operations—monitoring, automated retries, regression tests, anomaly detection, reprocessing, and change management

Common strategy:

full refresh daily
“high-signal” SKUs or key competitors 2–6x/day
automated alerts when price drops exceed thresholds (so you don’t need everything hourly)

Very Complex

Flight Hub page flights data collection complex

Reserved for expansive endeavors, this level targets large-scale websites with thousands of products or items. Think of sectors with dynamic pricing, like airlines or hotels, not limited to retail. The challenge here transcends sheer volume and extends to the intricate logic required for matching products or items, such as distinct hotel room types or variations in competitor products. To ensure data quality and precision, opting for an enterprise-level web scraping company is highly recommended for organizations operating at this level.

Typical viable frequency: hourly to near-real-time for parts of the system—but rarely for everything

Why: the bottleneck is not just crawling—it's matching and normalization (room types, fare classes, bundles, seat sections/rows, variants), plus strict QA and auditability

Common strategy:

run frequent “delta” collections (capture what changed)
run deeper “full reconciliations” daily/weekly
maintain strong identity resolution (so changes don’t get misattributed)

Practical Rules of Thumb (What Enterprises Do in Real Life)

Use “Sentinel SKUs” to justify higher frequency

Pick a small, representative set of items that are:

high revenue / high sensitivity
often discounted
key competitive benchmarks

If those are volatile intraday, increase cadence there first.

Don’t increase frequency until you can detect bad data automatically

More runs = more opportunities for silent errors (wrong price, wrong variant, missing fees). Higher frequency demands:

automated field validation (ranges, formats, required fields)
anomaly detection (spikes/drops, unexpected null rates)
sampling and human QA review
audit logs and reproducible runs

Align frequency to actionability

If your team cannot react intraday, hourly data becomes an expensive dashboard.

Plan for burst capacity

Even if “normal” is daily, you want the ability to temporarily go 4x/day during:

Black Friday / Cyber Week
competitor promo launches
high-stakes seasonal periods

Example Frequency Recommendations by Industry

Automotive tires (SKU + ZIP-level + fees/shipping): Daily for full catalog; 2–6x/day for key competitors and top SKUs in priority ZIPs.

QSR / retail menus (regional + channel differences): Daily, with extra runs during known promo windows and menu rollouts.

Ticketing / resale (event + section/row + dynamic pricing): Hourly (or more) for high-demand events, but often daily for the long tail.

Where Ficstar Fits

Why Frequency is an Operations Problem

At higher complexity, frequency isn’t limited by “can we scrape it once?” It’s limited by whether you can run it reliably, repeatedly, and defensibly:

monitoring + alerting
resilient retries and fallback logic
proactive change detection (site layouts, flows, anti-bot changes)
QA sampling and anomaly workflows
SLAs and consistent delivery formats
schema stability and product matching governance

That’s why many teams move from tools/freelancers to a managed partner when they need intraday refresh, multi-site scale, and consistent quality.

Contact Ficstar's data expert!

FAQ

Can we run the crawler hourly?

Often yes—for a subset. For “everything across every source,” hourly can become expensive and unstable unless the program has mature monitoring, QA, and change management.

What’s the most common frequency for competitor pricing?

For enterprise programs: daily for broad coverage, plus intraday for priority competitors/SKUs.

Should we scrape more often during promotions?

Yes—promotions compress the window where data is valuable. Many teams run “burst mode” schedules during peak periods.