SOAX Alternatives: What Actually Replaces It

Most "SOAX alternatives" lists are written by affiliates and they all make the same move: line up eight residential providers, score them on invented criteria, declare a winner. That list is useful only if you already know you need residential IPs.

The more valuable question comes first. Residential and mobile IPs solve exactly one problem, which is looking like a consumer connection to a site that classifies IP address space. They cost what they cost because acquiring consumer IP supply is expensive. If your target does not classify by IP class, you are paying that premium for nothing.

Plenty of SOAX traffic falls into that second bucket. Teams start on residential because the first target they hit blocked them, then route everything through the same pool out of habit, including crawls of sites that never cared. The habit is invisible on a per GB invoice until the invoice gets big.

When people actually audit their own target lists, a meaningful share of requests turn out to be running on residential IPs that the target would have accepted from a datacenter range. That is the part worth moving. The rest genuinely needs what SOAX sells, and you should buy it from a residential provider.

Worth writing down precisely, because the replacement has to match it.

As of 31 August 2026, SOAX's own product menu lists two proxy products: residential and mobile. The URLs soax.com/datacenter-proxies and soax.com/isp-proxies both return 404, so there is no datacenter or ISP tier to fall back on inside the account.

Alongside the proxies sit a managed scraping line (SERP, video and ecommerce data), a headful browser runtime, and per request feature flags such as Web Optimizer (-opt-wb) and MaxIP (-opt-uniqip). Access is a single gateway with the targeting encoded in the username, in the shape package--country-us-sessionid--sessionlength-300.

Billing is by traffic. SOAX's pricing page describes per GB rates that fill monthly buckets in order, the first 5 TB, then the next 10 TB, then everything above 15 TB, with each GB staying in the bucket it landed in rather than being repriced retroactively. Unused traffic rolls over for 60 days on monthly billing and 365 days on annual, and postpaid billing is offered.

Two details in that description decide everything downstream: the unit is a gigabyte, and the targeting reaches a city and an ASN. Any replacement either matches those, or changes the shape of the problem.

This is the section the affiliate lists skip. Sort your own target list into these two columns before you shop.

Moves cleanly to datacenter

• Open data and reference sources. Government portals, filings archives, standards bodies, patent and grant databases, transport and weather feeds, encyclopedic corpora. These serve robots on purpose and rarely look at ASN.
• Sitemaps, feeds and documentation crawls. XML sitemaps, RSS and Atom, JSON endpoints published for integration. High volume, tiny per request cost, almost no bot management.
• Long tail ecommerce and marketplaces. Sites below the tier that pays for a commercial bot manager. Most regional retailers, most B2B catalogs, most classifieds outside the top few.
• Anything byte heavy. PDF harvesting, image metadata, large HTML product pages, bulk JSON dumps. Per GB billing hits these hardest, and this is where the saving is largest.
• Availability and latency monitoring. Checking that your own or a partner's endpoint responds from thirty countries needs a country level exit, not a consumer identity.
• Localisation and pricing QA. Verifying that your site renders the right currency and language per country. Your own infrastructure will not block a datacenter range.
• Internal and partner APIs. Where you need a stable, high concurrency egress rather than a disguise.

Does not move, and you should not try

• Consumer platforms that score IP class on sight. Large social networks, video platforms, dating and messaging apps. Datacenter ASNs are the first filter, not the last.
• Anything account bound. If a login session lives behind the request, a datacenter exit changes the risk profile of the account itself. That is not a cost decision.
• Mobile only surfaces. App APIs and content gated to carrier IP space. That is precisely what SOAX's mobile pool exists for, and datacenter has no equivalent.
• Sneaker, ticketing and drop traffic. These targets have been tuned against datacenter ranges for a decade.
• Hard targets behind a top tier bot manager. Where the defence is calibrated on IP reputation, a datacenter range starts the request at a disadvantage.
• City or ASN level targeting. Datacenter pools are organised by country and subnet, not by neighbourhood or residential ISP. If your query genuinely needs Cleveland rather than the United States, no datacenter provider can serve it.

For the mechanics behind that split rather than the shortlist, comparison of residential vs datacenter vs mobile proxy types works through how each pool is sourced and what that does to detection, and datacenter vs mobile proxies covers the mobile end specifically.

Pool figures are each vendor's own published claim, read on 31 August 2026. Nobody audits these numbers, ourselves included, so treat them as marketing inputs rather than measurements.

Read the last row carefully. We are not on that list as a cheaper SOAX. We are on it because the per thread column changes the arithmetic for one specific slice of the work, and because a lot of readers arriving at a SOAX alternatives page have that slice hiding in their target list.

If your audit says the traffic stays residential, shop inside this group and ignore everything below it.

The straight replacements

Bright Data is the largest network by published pool and the deepest on targeting and managed products. It is also the most complex account you will operate, with the longest learning curve on zones, permissions and compliance review. Pick it when scale and coverage matter more than simplicity.

Oxylabs sits in similar territory with a smaller published pool and a cleaner product surface. Both are enterprise leaning, both negotiate at volume, and both publish more documentation than the rest of the category combined.

Decodo is the closest in feel to SOAX for a mid sized team: comparable targeting depth, self serve throughout, and a scraping API sitting alongside the proxies rather than replacing them.

IPRoyal is the one to look at if your usage is lumpy. Its residential traffic is sold without an expiry date, which suits a team that buys a block, burns half of it on a project, and comes back three months later. Under a bucketed rollover model that gap costs you.

Webshare covers residential, static residential and datacenter, with ZIP level targeting on the residential product. Its mobile coverage is the thinnest of the group, so a SOAX user leaning on 4G and 5G exits should test carefully rather than assume parity.

What to check before you sign

Pool size is the least useful number on any of these pages. Four things matter more.

1. Concurrency policy on your plan. SOAX advertises unlimited concurrent sessions on both residential and mobile. Not every alternative does at every tier, and a concurrency cap silently reshapes your crawl.
2. Session length semantics. SOAX lets you set a custom session length on residential and holds a mobile IP up to 60 minutes. If a competitor's sticky session means ten minutes, your checkout flow breaks in a way that looks like a bug in your own code.
3. How unused traffic behaves. Expiry, rollover window, and whether overage is billed or throttled.
4. Whether the ASN or ISP filter you rely on exists at all. If you filter to a specific residential ISP today, confirm the replacement exposes that dimension before you migrate anything.

For the workloads in the first column of the audit, the whole cost structure changes.

SparkProxy sells rotating datacenter proxies: 1M+ IPs across 80+ countries, a separate USA pool of 50,000+ addresses, unlimited bandwidth, and billing by concurrent thread on a flat monthly plan. There is one host and three ports, and nothing else to configure.

# Rotating exit, fresh IP per request
curl -s --proxy http://USER:PASS@gateway.sparkproxy.io:11000 \
     https://www.sparkproxy.io/ip

# Sticky session, same exit held across the session
curl -s --proxy http://USER-session-acct42:PASS@gateway.sparkproxy.io:11002 \
     https://www.sparkproxy.io/ip

# SOCKS5 with remote DNS resolution
curl -s --proxy socks5h://USER:PASS@gateway.sparkproxy.io:13000 \
     https://www.sparkproxy.io/ip

Country selection rides in the username, the same pattern SOAX uses.

curl -s --proxy http://USER-country-de:PASS@gateway.sparkproxy.io:11000 \
     https://www.sparkproxy.io/ip

SOCKS5 here is TCP only, so UDP based traffic has no path through it. Authentication is IP whitelist or user and password, both on every plan. Plans run 100, 250, 500 and 1000 concurrent threads, and the trial is 24 hours at 250 threads with no card, which is enough to measure a real block rate rather than a demo page.

Two limits, stated plainly:

• There is no residential or mobile product to buy. If your audit put most requests in the second column, we do not have the thing you need, and the section above names five companies that do.
• Targeting stops at the country. No city, no ASN, no carrier. That is a property of datacenter address space, not a roadmap gap.

There is one partial bridge, and it belongs in the docs rather than in a sales pitch. The SparkProxy Scraping API accepts a premium_proxy parameter that routes an individual request through a residential exit, priced at 10 credits without JavaScript rendering and 25 with it, against 1 credit for a plain fetch. That is a per request escape hatch for the handful of URLs in a crawl that need one, not a residential plan wearing a different name.

import requests

# Plain datacenter fetch: 1 credit
r = requests.get(
    "https://scrape.sparkproxy.io/api/v1",
    headers={"X-API-Key": "YOUR_API_KEY"},
    params={"url": "https://www.sparkproxy.io/", "render_js": "false"},
)

# Same call, residential exit, for the URLs that need it: 10 credits
r = requests.get(
    "https://scrape.sparkproxy.io/api/v1",
    headers={"X-API-Key": "YOUR_API_KEY"},
    params={
        "url": "https://www.sparkproxy.io/",
        "render_js": "false",
        "premium_proxy": "true",
    },
)

If your crawl is a long list of simple pages, batch mode collapses the credit cost further, since a whole comma separated batch costs 1 credit when render_js is false.

import requests

urls = [
    "https://www.sparkproxy.io/",
    "https://www.sparkproxy.io/blog/",
    "https://www.sparkproxy.io/docs/scraping-api/",
]

r = requests.get(
    "https://scrape.sparkproxy.io/api/v1",
    headers={"X-API-Key": "YOUR_API_KEY"},
    params={"url": ",".join(urls), "render_js": "false"},
)
for item in r.json()["results"]:
    print(item["url"], item["httpStatus"], item["success"])

Here is the calculation nobody puts in a comparison article, and it decides whether any of this is worth your time.

Under per GB billing your cost scales with page weight. Under per thread billing it does not. So the first thing to measure is not your request count, it is your average bytes per request.

Take the per GB rate from your own current invoice, not from any vendor's marketing page, and multiply. Then set that against a flat monthly thread plan whose bandwidth column reads unlimited.

The pattern that falls out is consistent. At 15 KB per request and modest volume, per GB billing is already cheap, and switching anything is a waste of an afternoon. At 400 KB and a million requests a month, the traffic line alone is the size of a mid tier thread plan, before the residential premium is counted at all. The heavier and higher volume the crawl, the more the unit of billing dominates every other consideration.

Measure it directly rather than guessing.

import requests, statistics

sample = ["https://www.sparkproxy.io/", "https://www.sparkproxy.io/blog/"]  # use YOUR real targets
proxy = "http://USER:PASS@gateway.sparkproxy.io:11000"
sizes = []

for url in sample:
    r = requests.get(url, proxies={"http": proxy, "https": proxy}, timeout=30)
    sizes.append(len(r.content))

avg_kb = statistics.mean(sizes) / 1024
print(f"avg {avg_kb:.1f} KB, {avg_kb * 1_000_000 / 1_048_576:.0f} GB per million requests")

One caveat that cuts against us: bandwidth is not the only cost line. A datacenter pool that gets blocked forces retries, and a retry is a request you paid for twice in wall clock time. Unlimited bandwidth is a saving only on targets that answer. That is what the next section is for. For the full breakdown of how thread based plans are structured, understanding datacenter proxy pricing models covers it, and shared vs dedicated datacenter proxies explains why a shared rotating pool behaves differently from a rented static set.

Do not migrate on an article's say so, ours included. Take 200 real URLs from your live crawl, spread across every distinct target domain, and run them twice.

1. Baseline. Run the 200 through your current SOAX residential setup. Record HTTP status, response bytes and wall clock per request.
2. Candidate. Run the identical list through a datacenter trial. Same headers, same concurrency, same order.
3. Score per domain, never in aggregate. An overall block rate hides everything useful. You want the domains sitting at or near 0% blocked on datacenter, which are the ones that move, and the domains above roughly 10%, which stay residential.
4. Watch for soft blocks. A 200 response can still be a consent wall, a challenge page or a stripped result set. Compare response length against the baseline, not just the status code.
5. Re-run an hour later. Bot defences are stateful. A clean first pass sometimes turns into a challenge on the second.

import requests
from collections import Counter
from urllib.parse import urlparse

PROXY = "http://USER:PASS@gateway.sparkproxy.io:11000"
blocked = Counter()
total = Counter()

for url in open("targets.txt").read().split():
    host = urlparse(url).netloc
    total[host] += 1
    try:
        r = requests.get(url, proxies={"http": PROXY, "https": PROXY}, timeout=20)
        # soft-block heuristic: unexpected status, or a suspiciously small body
        if r.status_code in (401, 403, 407, 429, 503) or len(r.content) < 2000:
            blocked[host] += 1
    except requests.RequestException:
        blocked[host] += 1

for host, n in total.items():
    print(f"{host:40s} {blocked[host] / n:6.1%} blocked  ({blocked[host]}/{n})")

Everything printed at 0.0% is a line item you are currently overpaying for. Everything above 10% is a target that earns its residential IPs. Split the crawl on that boundary and route each domain accordingly, which is a normal architecture rather than a compromise.

One more thing to weigh, and it is not a feature comparison.

On 31 August 2026, netnut.io served a seizure notice from the United States Federal Bureau of Investigation at both the root domain and its residential proxies page. NetNut appears on most SOAX alternatives lists published before that date. We will not characterise the situation beyond what the page itself shows, and nothing here is a claim about anyone's conduct.

The practical point is architectural. Residential proxy supply is sourced from consumer devices through SDK partnerships and reward apps, a longer and more exposed chain than renting rack space. Whichever provider you pick, assume any single vendor can become unreachable on short notice, and build so that costs you an hour rather than a week:

• Keep the proxy endpoint in configuration, never in code. One environment variable, one place to change it.
• Hold a second provider's credentials, even unused. A dormant account with a small balance is cheap insurance.
• Do not let credential syntax leak into your crawler. SOAX encodes targeting in the username and so do we, so wrap that in a function that builds the string and a switch becomes a single edit.
• Store enough of a run's state that a mid crawl provider swap resumes instead of restarting.

If a portion of your traffic is moving, this is the mapping. SOAX puts targeting in the username against a single gateway, and so does SparkProxy, so most of the work is mechanical.

Two migration mistakes worth naming. Do not carry your SOAX concurrency assumptions across unchanged: when threads are the billed resource, an over provisioned worker pool is money on the floor and an under provisioned one is a queue. And do not change sticky sessions and rotation on the same day. Change one, measure across a full crawl cycle, then change the other, or you will not know which edit moved the block rate.

The version of this that survives contact with your invoice is almost never "replace SOAX". It is closer to "measure the crawl, move the part that never needed residential IPs, keep paying for residential on the part that does". Less satisfying than a ranked list, and it is the answer that shows up on the bill.

To measure the datacenter half properly, the 24-hour trial gives you 250 threads with no card, which is enough to run the block rate script above against your own target list. If your targets come back blocked, you will have learned that for free, and you should stay on residential.