Residential vs Datacenter vs Mobile Proxies · 2026 Guide
Three networks, three very different cost and trust profiles. A field guide to picking the right one for scraping, automation, ad verification and account work — with the numbers and trade-offs we actually see in production.
Pick the wrong proxy type and nothing else in your stack matters. The cleanest scraper in the world will burn through a million datacenter IPs trying to fetch an Instagram profile; an expensive mobile pool is a waste of money on a public price page that any datacenter IP could pull in 40 milliseconds.
This guide is the answer we give customers who arrive asking "which one do I buy?" — written from the perspective of running [ProxyGen](/) on top of all three networks, watching what works and what doesn't across thousands of real workloads.
## The short version
If you only read one paragraph: **datacenter** for cheap public data, **residential** for general-purpose scraping and geo-locked content, **mobile** for the hardest targets — social platforms, ad verification, and anything that fingerprints carrier ASNs. Most production scrapers end up using two of the three, routed by target domain.
The rest of this article is the reasoning behind that summary, the numbers we measure in production, and the questions you should ask before committing budget to a network.
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## How each network is actually built
You can't reason about success rates without understanding where the IPs come from. The three networks have fundamentally different supply chains, and that supply chain is exactly what anti-bot systems are scoring.
### Datacenter proxies
Datacenter IPs are allocated to cloud providers and hosting companies — AWS, Hetzner, OVH, DigitalOcean, hundreds of smaller VPS shops. They live in the same /24 blocks as the API servers and web hosts they originate from.
For a target site, the giveaway is the **ASN (Autonomous System Number)**. Any half-serious anti-bot stack maintains a list of hosting ASNs and treats traffic from them with extra suspicion. That doesn't mean *blocked* — plenty of legitimate traffic comes from datacenter ranges, including server-to-server API calls, monitoring tools, and corporate VPNs — but it does mean the bar for "looks like a bot" is much lower.
**Typical price in 2026:** `$0.50–$2.00 per IP/month` for shared, `$3–$8` for dedicated. Bandwidth is essentially free.
**Typical use cases that survive:**
- Public product catalogues with no JavaScript challenge - SEO rank tracking on Google-friendly targets - Sports odds and crypto price feeds - Internal monitoring and uptime checks - Any target that returns the same data to logged-out users without a CAPTCHA
### Residential proxies
Residential IPs are allocated by **ISPs** (Comcast, Vodafone, Türk Telekom, Telmex…) to home subscribers. A residential proxy network buys or rents access to those IPs — usually through SDK-bundled apps where end users opt into shared bandwidth in exchange for a free tier of something else.
To an anti-bot system, a request from a residential IP looks like a person on their home Wi-Fi. The ASN is consumer-grade. The IP has a history of normal browsing patterns. The geographic distribution matches population centres rather than datacenter clusters.
**Typical price in 2026:** `$1–$6 per GB` of bandwidth depending on volume. No per-IP charge — you pay for the bytes that travel through the network.
**Typical use cases:**
- E-commerce price intelligence (Amazon, eBay, Mercado Libre, Trendyol) - Travel and hospitality scraping (hotel rates, flight prices) - SERP scraping at scale - Anything geo-locked: regional pricing, country-specific catalogues, localised search results - Account creation and management on low-to-medium-trust platforms
### Mobile proxies
Mobile IPs come from cellular carriers — 4G LTE and 5G connections allocated to phone subscribers. The defining property is **carrier-grade NAT (CGNAT)**: a single public IP is shared by thousands of real mobile devices at once.
That shared-IP property is what makes mobile proxies so hard to block. Banning a mobile IP doesn't ban one bot — it bans every Verizon, Vodafone, or Turkcell subscriber routed through that gateway. Anti-bot systems know this and apply extra leniency to mobile carrier ASNs as a result. It's not that mobile traffic is *trusted*, it's that the blast radius of blocking it is unacceptable.
**Typical price in 2026:** `$8–$25 per GB`, the most expensive of the three by a wide margin. Some providers also rent dedicated SIMs by the day.
**Typical use cases:**
- Social media: Instagram, TikTok, X, Reddit at any scale beyond personal - App store scraping (Google Play, App Store) - Ad verification on mobile-first platforms - Account management on platforms with aggressive device fingerprinting - Sneaker drops, ticket releases, and other high-friction commerce
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## The numbers we actually see
Here is what a typical month of production traffic looks like on each network, averaged across customers in the same vertical. *These are real success rates measured at the application layer (HTTP 200 + expected payload), not just TCP handshake success.*
### Public e-commerce (logged-out product pages)
| Network | Success rate | Cost per 1M requests | Latency (p50) | | --- | --- | --- | --- | | Datacenter | 91% | $1.20 | 80 ms | | Residential | 98% | $14 | 240 ms | | Mobile | 99% | $65 | 380 ms |
For this workload datacenter is the right answer — that 7-point success rate gap costs you 10× less per million requests. Residential only wins when the target starts geo-blocking or asking for JavaScript-rendered prices.
### Search engine SERPs
| Network | Success rate | Notes | | --- | --- | --- | | Datacenter | 60–75% | Heavy CAPTCHA below 1 req/s per IP | | Residential | 96% | Stable at 5 req/s/identity with session stickiness | | Mobile | 97% | Marginal gain over residential, not worth the cost |
### Instagram / TikTok account work
| Network | Success rate | Notes | | --- | --- | --- | | Datacenter | < 5% | Instant block, often permanent on the account | | Residential | 60–80% | Works for read; account actions trigger checkpoints | | Mobile | 95%+ | The only network we recommend for any account-touching workload |
The pattern is consistent: **the harder the target, the more the curve flattens between datacenter and residential, and the more mobile becomes irreplaceable.**
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## What anti-bot systems are actually checking
Picking the right network is necessary but not sufficient. Modern detection stacks layer multiple signals on top of IP reputation, and a clean IP fired through a careless client will still get blocked.
### IP reputation and ASN
The cheapest signal: lookup the IP's ASN, cross-reference with a list of known hosting providers, residential ISPs, and mobile carriers. Add fraud-score databases (IPQualityScore, MaxMind minFraud, IPHub) that flag IPs seen on VPNs or open proxy lists.
> *Residential and mobile IPs that have leaked onto public proxy lists are essentially "burned" — they look residential by ASN but score as proxy on every reputation database. A serious provider rotates these out aggressively.*
### TLS fingerprinting
The handshake your HTTP client sends is a fingerprint. `curl`, `requests`, `httpx` and `axios` each have a distinct cipher-suite ordering, extension list, and ALPN negotiation that is **trivial to detect** — even when the IP is a perfect residential one.
Tools like `curl-impersonate` and `tls-client` produce handshakes that match real Chrome or Safari builds. If you're scraping anything harder than a public RSS feed, you need one of these.
### HTTP/2 frame ordering
HTTP/2 sends settings and headers in a specific frame order that varies by browser. Anti-bot systems hash that order. A "Chrome 121" user-agent paired with HTTP/2 frames in the order Go's stdlib produces is an instant flag.
### Behavioural signals
For browser-rendered targets the detection moves up the stack: mouse movement, scroll patterns, key timings, canvas fingerprints, WebGL parameters, audio context hashes. The proxy IP is just one input among dozens.
### What this means for network choice
A perfect mobile IP can't save a Python script firing `requests` with the default urllib3 TLS stack. Conversely, a properly impersonated Chrome 121 fingerprint fired through a datacenter IP will still get blocked on Instagram because **the ASN itself disqualifies it**.
Network choice and client fingerprinting are two separate axes. You need both right for hard targets.
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## Static ISP — the fourth option
Worth a mention: **static ISP proxies** (sometimes called "static residential") are a hybrid. The IPs are allocated to a residential ISP — so they pass ASN-based checks — but they live on a datacenter machine that holds them long-term. You get residential reputation with datacenter uptime and speed.
The trade-off:
- **Pro:** Long-lived (weeks/months on the same IP), fast (datacenter network speeds), residential ASN - **Con:** Smaller pools (thousands, not millions), more expensive than rotating residential, more likely to get individually burned because they don't rotate
[Static ISP proxies](/static-isp) are the right answer for **account farming, marketplace seller accounts, and ad verification** where you want a stable identity over many days rather than a fresh IP per request.
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## Sticky sessions vs rotation
Independent of the network choice, you also need to pick a session strategy.
### Rotation per request
Every HTTP request gets a fresh IP. Maximum diversity, but **suspicious on its own** — a real user doesn't change IP between loading a product page and loading its images. Useful for stateless workloads: SERP scraping, public price fetching, dictionary lookups.
### Sticky sessions
The same upstream IP is held for a window (1–120 minutes typically) keyed on a session ID. The proxy gateway routes all traffic for that session through the same exit IP.
Sticky is the right default for anything resembling a user journey: login, browse, add to cart, checkout. Anti-bot systems expect IP continuity within a session and **flag mid-session IP changes as fraud**.
The practical compromise: stick a session for the lifetime of the user-flow you're simulating (5–30 minutes), then rotate the whole identity — IP, cookies, fingerprint, the lot.
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## A simple decision tree
When customers ask us "which one should I buy", we walk them through this:
1. **Is the target a logged-out public page with no JavaScript challenge?** → Start with [datacenter](/datacenter). Cheapest, fastest, works fine. 2. **Does the target geo-locate, render prices in JS, or run a visible bot-protection vendor (Cloudflare Turnstile, Akamai Bot Manager, PerimeterX/HUMAN, DataDome)?** → [Residential](/residential-proxy) with sticky sessions, real browser headers, and TLS impersonation. 3. **Is the target a social platform, an app store, or anything where you log into an account?** → [Mobile](/mobile-proxy). The cost premium is real but the success rate gap is bigger. 4. **Do you need to hold a single residential-looking identity for days or weeks?** → [Static ISP](/static-isp). Account farming, seller accounts, ad verification.
Most production setups end up routing per-domain: datacenter for the easy 70% of targets, residential for the medium 25%, mobile for the hard 5% that actually need it. The waste in production scraping pipelines almost always comes from **using a single network for everything** — usually paying residential rates for traffic that would have succeeded on datacenter.
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## A note on cost modelling
Cost-per-GB and cost-per-IP look incomparable on first glance, but at the workload level they collapse to the same number: **cost per successful request**.
``` cost_per_success = (gb_used × $/GB + ips_used × $/IP) / successful_requests ```
A datacenter pool at $1/IP/month with a 60% success rate on your target is more expensive than a residential pool at $3/GB with a 98% success rate — once you factor in the retries, the latency cost of retries, and the engineering time spent tuning around the failures. We've seen customers cut their proxy bill by 40% by migrating *up* the price ladder, because the lower-tier network was burning through retries.
Always benchmark on your real targets before committing to a network. We give every account a small trial balance specifically so the first decision can be data-driven rather than guesswork.
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## What ProxyGen does differently
A quick note on how our [shared bandwidth pool](/pricing) interacts with this guide. Most providers force you to pre-commit to a network — 50 GB residential or 10 GB mobile, no swapping. We don't. Every dollar of credit lands in a single pool and draws against residential, mobile, ISP, or datacenter as your traffic demands.
The practical effect: you can follow the decision tree above without budget guilt. Routing 5% of your traffic to mobile because that's what the target needs doesn't require buying a separate mobile plan you might not finish. The pool just settles.
## Country coverage
For workloads that need a specific geography, jump straight to the country page — every Tier 1 market has its own breakdown of pool size, real ISP samples, latency profile and a country-specific FAQ:
- **Americas** — [United States](/residential-proxy/united-states), [Canada](/residential-proxy/canada), [Brazil](/residential-proxy/brazil), [Mexico](/residential-proxy/mexico) - **Europe** — [United Kingdom](/residential-proxy/united-kingdom), [Germany](/residential-proxy/germany), [France](/residential-proxy/france), [Italy](/residential-proxy/italy), [Spain](/residential-proxy/spain), [Netherlands](/residential-proxy/netherlands), [Poland](/residential-proxy/poland) - **Asia & Pacific** — [Turkey](/residential-proxy/turkey), [Japan](/residential-proxy/japan), [India](/residential-proxy/india), [South Korea](/residential-proxy/south-korea), [Indonesia](/residential-proxy/indonesia), [Australia](/residential-proxy/australia)
Each country also has a mobile variant at `/mobile-proxy/<slug>` with carrier-specific use cases. Browse the full list at [Proxy Locations](/proxy-locations).
If you want to dig deeper into specific platforms — Instagram, Amazon, Google SERPs — head to our [Scraping](/scraping) section or the targeted guides on the [blog](/blog).
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## Further reading
- [Surviving 2026 Anti-Bot: Fingerprinting Beyond IP](/blog/anti-bot-2026-fingerprinting) — the client-side half of this problem. - [How to Scrape Amazon Reliably in 2026](/blog/scrape-amazon-2026) — a worked example of routing residential to a single hard target. - [Rotating Residential vs Mobile Proxies in 2026](/blog/rotating-residential-vs-mobile-2026) — the deeper comparison of the two trickiest networks.