$8K–$40K typical projects
Direct answer
I take on web scraping and data extraction projects for $8K–$40K: the low end covers a reliable scraper for a handful of structured sources with clean output, the high end covers large-scale pipelines across many sites with anti-bot handling, scheduling, monitoring, and delivery into your database or warehouse. Most engagements run 3–8 weeks. I bring 7+ years of production Python delivery and Top Rated status on Upwork with $100K+ earned and verified client reviews — which matters in scraping more than most niches, because the market is full of scripts that work for a week and die silently.
The hard part of scraping was never getting the first page of data — it's still getting correct data in month six, after the target site redesigned twice and added bot detection. This service delivers extraction pipelines built like production software: monitored, validated, and honest about legal boundaries. Buyers succeed here when they treat scraped data as a supply chain, not a one-time download.
You need an MVP or v2 shipped on budget with someone who makes architecture decisions and owns delivery end-to-end.
You need a senior IC to augment the team, rescue a codebase, or lead mobile + AI integration without months of hiring.
You need a reliable senior subcontractor for client projects — clear communication, store-ready quality, white-label friendly.
30 minutes — goals, stack, timeline, budget range.
Fixed milestones, clear deliverables, start date.
Weekly demos, async Slack updates, production standards.
Store launch, documentation, knowledge transfer.
An e-commerce operator needs daily pricing and availability from a set of competitor sites. I build per-site extractors with schema validation, a scheduler with retries and change alerts, and delivery into their database or a spreadsheet their team already uses. When a site redesigns, monitoring flags the break within hours instead of poisoning weeks of decisions with stale data.
A team needs a large, clean corpus — listings, reviews, filings, articles — to power a model or analytics product. I assess which sources are legally and technically viable, build a polite, rate-limited crawler, deduplicate and normalize the output, and document provenance per record. They get a defensible dataset with known coverage, not a folder of HTML soup.
A company runs a pile of scripts written by past contractors; every month another one silently breaks. I consolidate them into one framework with shared session handling, per-source health checks, and structured logs, then kill the sources that were never worth their maintenance cost. Ongoing breakage goes from a recurring emergency to a tractable, monitored queue.
Week one is feasibility and honesty: I probe each target source for structure, rendering requirements, anti-bot posture, and terms-of-service risk, then tell you which sources are cheap, which are expensive, and which I won't touch. This is also where we check for official APIs and licensed data feeds — if the data can be bought or requested for less than scraping costs, I'll say so and shrink the project.
Weeks two through four, I build extractors source by source, each with a validation schema — required fields, plausible ranges, record counts — so bad data fails loudly instead of flowing downstream. The final phase is the operational wrapper most scraping projects never get: scheduling, retries with backoff, proxy management where legitimate, alerting when a source's yield drops, and delivery into your systems. You end up owning a pipeline with health dashboards, not a script with my name in a comment.
Source difficulty dominates. A server-rendered site with stable markup costs a fraction of a JavaScript-heavy application behind aggressive bot detection, where each page needs browser automation, fingerprint management, and constant upkeep. Source count is second — but not linearly, because ten sources rarely share markup, so each is its own small integration.
Third is freshness and scale: a weekly pull of thousands of records is cheap to run; hourly monitoring of millions of pages needs distributed scheduling, proxy budgets, and storage design. Fourth, data messiness: extraction is half the work; normalizing prices, dates, addresses, and duplicate entities across sources is the other half, and buyers consistently underestimate it. A practical note — ongoing maintenance is a real cost, typically a few hours a month per volatile source. I quote it explicitly, because a scraping bid without a maintenance line item is just deferring the price.
Scraping sits in a legal gray zone that varies by jurisdiction, data type, and method, and any vendor who waves that away is a liability. My working rules: public data only — I don't scrape behind logins in violation of terms, don't circumvent access controls, and don't collect personal data that would create GDPR or CCPA exposure for you. I respect robots.txt as a strong signal, rate-limit to avoid harming target sites, and prefer official APIs when they exist.
I'll flag when your use case needs a lawyer rather than an engineer — reselling scraped content wholesale, for instance, carries copyright risk that no technical cleverness fixes. This isn't just ethics; it's risk management for your business. Projects built on quietly abusive scraping get cease-and-desists, IP bans, and sudden data outages at the worst moment. A pipeline designed to be polite is also, not coincidentally, the one that keeps working.
Ask three questions. First: 'How will I know when a scraper breaks?' The only good answer is automated monitoring — record counts, schema validation, freshness checks — with alerting. Silent failure is the defining disease of this niche; scripts keep exiting zero while returning garbage. Second: 'What happens when the site redesigns?' You want to hear about resilient selectors, fast repair workflow, and an explicit maintenance arrangement, not 'it should be fine.'
Third: 'Which of my target sources would you refuse or deprioritize, and why?' A senior vendor pushes back on at least some of your list — for legal exposure, brittleness, or poor cost-per-value — because a vendor who accepts every source uncritically is quoting a fantasy. Also look at their output format thinking: deliverables should be validated, deduplicated records in your database or warehouse, with provenance. Raw HTML dumps or loose CSVs mean the hard half of the work is being left to you.
Check for an API first — a surprising number of scraping requests I receive are for data available through an official API, a licensed feed, or a bulk export that costs less than one week of engineering. Check data brokers and existing datasets second; if a vendor already sells the corpus, buying usually beats building unless your needs are unusual.
Don't scrape to power a core product feature from a single hostile source — if one company's website is your entire supply chain and they don't want you there, your product has a kill switch someone else controls. And don't commission a pipeline for a one-time analysis; a single manual extraction pass is far cheaper than automation you'll never run twice. Scraping earns its cost when the data is public, recurring, multi-source, and unavailable any other way — that's the profile where this service pays for itself quickly.
You approve milestones and a price before any build starts — no open-ended hourly surprises.
You see running software each week, not status reports, so you can course-correct early.
The person who scopes the work is the person who builds it — no junior layers, no agency markup.
Top Rated on Upwork with public client reviews and $100K+ earned, plus contributions to Expensify. Check the receipts before you commit.
Chat-Based Financial Collaboration
Contributed 50+ merged pull requests to New Expensify — the production React Native app used by millions for chat-based expense management — working through Expensify's fully public open-source process alongside their core engineers, and recognized with a Guest Engineer invitation to their Curaçao offsite.
Food Truck Discovery Platform
Production food-truck discovery platform for US cities — customer and vendor React Native apps on iOS and Android, plus customer and vendor web, with AI features. Helps people find trucks nearby while vendors manage schedules, orders, and events.
Rewards & Engagement Platform
End-to-end React Native app for the US market with gamified rewards, real-time engagement, and secure payment flows — built from product scoping to App Store launch.
With a senior engineer, $8K–$40K. A monitored scraper covering a few structured sources with clean, validated output lands at $8K–$15K. Multi-source pipelines with browser automation, anti-bot handling, scheduling, and warehouse delivery run $20K–$40K. Expect an explicit ongoing maintenance cost too — typically a few hours per month per volatile source — because sites change and any quote pretending otherwise is hiding the real price.
Scraping publicly accessible data is generally defensible in many jurisdictions, but it depends on what you collect, how, and what you do with it. Risk rises sharply with personal data (GDPR/CCPA exposure), scraping behind logins against terms of service, circumventing technical blocks, or republishing copyrighted content. I build within conservative boundaries — public data, polite rates, no personal-data harvesting — and I'll tell you when your use case needs legal review rather than engineering.
A pipeline over a few well-behaved sources takes 3–4 weeks including monitoring and delivery into your systems. Difficult JavaScript-heavy or bot-protected sources add one to two weeks each. The first data usually flows within days — the remaining weeks buy the part that matters: validation, alerting, retries, and normalization that keep the data trustworthy in month six, which is where cheap scraping projects quietly fail.
Projects typically fall in the $8K–$40K range depending on scope, integrations, and timeline. I provide a fixed-scope proposal after a 30-minute scoping call.
MVPs often ship in 8–12 weeks. Production systems with AI backends or RAG may run 12–20 weeks. Rescue and audit engagements can start within days.
Yes. I work with founders, CTOs, product teams, and agencies worldwide — US, UK, EU, and APAC time zones with async updates and weekly demos.
Yes. I specialize in React Native + Python (FastAPI) + AI (RAG, agents, OpenAI/Claude) under one senior owner — fewer handoffs, faster shipping.
Book a free 30-minute scoping call on this site, hire through Upwork, or email dhairyasenjaliya@gmail.com with your brief and timeline.
30-minute scoping call · Clear milestones · Senior engineer ownership