Best Datacenter Proxies Ranked by Price and Performance

Scraping at volume with unpredictable page weight or block rate? Buy a flat plan priced by concurrency. SparkProxy sells that shape: 100 threads for $75/mo on Starter up to 1000 threads for $440/mo on Plus, unlimited bandwidth on all of them, 1M+ datacenter IPs across 80+ countries.

Running something small, or just proving a target is scrapeable? Webshare's per-proxy pricing is the cheapest real entry point here by an order of magnitude, and their free tier is 10 proxies with no card.

Need addresses that stay yours because you are logging in, allowlisting, or holding accounts? Rotation is the wrong product. Buy dedicated IPs from IPRoyal, Oxylabs or Bright Data, and read our breakdown of shared vs dedicated datacenter proxies first.

Nobody here is a strict upgrade over the others. The sections below say where each one loses.

Six things decide whether a datacenter proxy plan is good value. Not one of them is the headline price.

1. Billing unit. Per IP per month, per gigabyte, or per concurrent thread. This choice alone can swing your bill 10x on the same workload.
2. What the entry price buys. A $2.99 entry is meaningless if the traffic allowance runs out in two days.
3. Bandwidth terms. Metered, throttled past a line, or genuinely uncapped. Throttled and cut off are very different outcomes at 3am.
4. Concurrency ceiling. Requests in flight determine throughput, not the size of the pool.
5. Authentication. IP whitelist, user:pass, or both. Whitelist-only breaks on dynamic egress IPs, which describes most cloud runners.
6. A trial you can point at your own targets. A demo dashboard proves nothing.

What this ranking ignores: vendor uptime, latency and success-rate panels. Every provider here prints a number in the high nineties, and none of those came from an independent audit, ours included.

Datacenter proxies are sold three ways, and each model is flat along one axis and brutal along another.

Per IP per month. You rent addresses. Webshare, Bright Data, Oxylabs and Decodo all offer this, and IPRoyal sells a term variant. Your bill is driven entirely by how many addresses you hold, so it does not move when pages get heavier or request counts rise. It moves hard when your target tightens its per-IP tolerance, because your only lever is buying more addresses.

Per gigabyte. Oxylabs, Bright Data and Decodo publish per-GB datacenter tiers. Your bill tracks bytes, so it is flat when a target rate-limits aggressively and painful when pages are heavy. It carries a detail people miss: failed requests bill too. A 20% block rate is a 20% price increase on per-GB and costs nothing extra on the other two models.

Per concurrent thread, unmetered. SparkProxy sells this: a fixed number of simultaneous connections against a rotating pool, flat monthly, no traffic meter. The bill moves only with how much parallelism you buy. The trade-off is a floor, and $75/mo is a lot for 20,000 requests a month.

The buying rule falls straight out of that. Find the axis of your workload that is least predictable, then pick the model that is flat along it. Cannot predict page weight? Not per-GB. Cannot predict how hard a target will clamp down per IP? Not per-IP. Our longer treatment of datacenter proxy pricing models has the arithmetic.

All competitor rows below are that vendor's own published material, read September 2026. Verify before you buy.

Webshare

The cheapest usable datacenter proxies here, and it is not close. Published tiers run from $0.0299 per proxy at 100 proxies down to $0.0179 at 60,000, plus 10 free with no card. That free tier is the best way to learn whether a target blocks datacenter ASNs at all, before you spend anything anywhere.

Buy it when you are prototyping or need a lot of static addresses cheaply. Skip it when traffic allowance is your binding constraint. Their pricing page shows a range of "250GB - Unlimited" rather than one figure, so check what lands on the tier you build in the configurator. At these prices the traffic terms are the real product.

IPRoyal

Sells addresses, not rotation. Their datacenter page lists $1.57 per proxy on 30 days, $1.48 on 60 and $1.39 on 90, across 60+ locations with state and city targeting and both HTTP(S) and SOCKS5. Each proxy includes 100 GB of traffic per monthly cycle, pooled across your order, which welds bandwidth headroom to address count.

Buy it when you need exclusive, stable IPs for a fixed term. Skip it when your workload is data-heavy and identity-light, because you end up buying addresses you do not need to get traffic you do.

Oxylabs

The most flexible catalogue here. They publish all three shapes: dedicated per IP from $1.20, shared per IP from $0.70, shared per GB from $0.44, with a stated 1.9M+ dedicated pool across 188 countries and a 5 free IP trial on the shared product.

Buy it when you need enterprise procurement, wide coverage, or want to compare two billing models under one account. Skip it when you are small. The sample configurations on their own page ($2.25/IP dedicated, $1.20/IP shared, $0.59/GB) sit well above the headline "from" rates.

Bright Data

The largest published footprint of the six, over 1,300,000 IPs across 98 countries. Shared IPs from $1.40 at 10 units down to $0.90 at 1,000, dedicated from $2.20 down to $1.30, or bandwidth at $0.60/GB pay as you go, falling to $0.42/GB on a 5 TB commitment.

Buy it when you need scale, compliance documentation, and a vendor your legal team has heard of. Skip it when you want a simple bill. The platform is deep, and depth means configuration surface somebody on your team has to own.

Decodo

Sits between the budget and enterprise tiers. Published datacenter rates run $0.45 to $0.60 per GB, $0.02 to $0.035 per IP, dedicated from $1.15 per IP, on a stated 500K+ pool with 100K+ rotating and static shared addresses. An unlimited traffic option exists on some plans, unusual at this price point.

Buy it when you want per-GB economics without a sales call. Skip it when you need to evaluate seriously first: the trial is 3 days and 100 MB, roughly 700 average HTML pages, enough to confirm connectivity and nowhere near enough to measure a block rate.

SparkProxy

The only provider here selling neither bytes nor addresses. You buy concurrency: Starter $75/mo for 100 threads, Core $140 for 250, Boost $240 for 500, Plus $440 for 1000, all unlimited bandwidth, 5 to 25 whitelist slots by plan, 30 day validity. The pool is 1M+ datacenter IPs across 80+ countries including 50,000+ US addresses, at gateway.sparkproxy.io on port 11000 for HTTP/HTTPS, 11002 for sticky sessions, 13000 for SOCKS5.

Buy it when your traffic volume or page weight is unpredictable and you want a bill that does not care. Skip it when you are under roughly 50,000 requests a month, where $75 buys more headroom than you can use, or when you need a specific address to stay yours.

Here is the part comparison tables skip. One concrete workload, priced through every model.

The workload. One million plain HTML requests over a month. Average response 150 KB, so roughly 150 GB of transfer. The target soft-blocks an address after about 500 requests a day, which at 33,333 requests a day means roughly 70 addresses in circulation. Those are model assumptions chosen to be plausible, not measurements from a test we ran. Your numbers will differ. The point is the shape, not the total.

Five of the eight land between $68 and $100. On the median workload the vendor barely matters. What matters is what happens when the workload changes shape, and the model decides that entirely:

• Pages get heavier. Swap 150 KB HTML for a 2 MB JavaScript-rendered page and the same million requests becomes about 2 TB. Per-GB goes from roughly $90 to roughly $1,200. Per-IP and per-thread do not move.
• The target clamps down. If per-IP tolerance drops from 500 requests a day to 50, you need 700 addresses instead of 70. Per-IP goes up tenfold. Per-GB and per-thread do not move.
• Your block rate rises. A 25% failure rate means 250,000 extra requests. Per-GB bills every one, including the ones that returned a block page. Per-IP and per-thread cost nothing extra, you just finish later.

Two of those three are things your target does to you, not things you control. Buy the model that is flat along whichever one you cannot forecast, and treat per-unit price as a tiebreaker between models already in the right shape. Sizing the concurrency side is its own exercise, covered in concurrent connections in proxies.

Datacenter proxies terminate in commercial hosting networks. That is the definition, and also the ceiling on how different two vendors can be. Everyone here exits from ranges registered to hosting companies, and most anti-bot systems classify traffic by datacenter ASN long before latency matters. If your target blocks datacenter ranges outright, no vendor on this page fixes that and you are shopping in the wrong category: read residential vs datacenter proxies next.

Within the category, three things genuinely differ.

Subnet diversity. A pool of 100,000 addresses sitting inside a handful of /16s behaves like a much smaller pool once a target blocks by prefix. Vendors publish IP counts, not subnet counts, and the subnet count is exactly the number that would let you evaluate this. Our guide on clean datacenter subnets explains how to check for yourself.

Abuse management. Shared pools degrade when other customers hammer the same targets you do. That is invisible from the outside, and it is the strongest argument for trialling on your own targets rather than trusting somebody else's benchmark.

Throughput ceilings. SparkProxy publishes a per-plan speed cap in its fair usage policy: 25 Mbps on Starter, 50 on Core, 100 on Boost, 150 on Plus, 200 on Pro, 250 on Pro+, up to 1 Gbps on custom arrangements. That is a ceiling, not a promised rate. Most vendors publish no cap at all, which is not the same as not having one.

Latency between reputable datacenter providers, measured from the same region to the same target, sits inside the noise of the target's own response times. A vendor leaning hard on a millisecond figure usually had nothing more interesting to say.

That last row needs a caveat. A managed API is not automatically cheaper, it moves the work. Plain fetches cost 1 credit, JavaScript rendering 5, screenshots or PDFs 10, so a million rendered pages is 5,000,000 credits, the $249 Pro tier. Weigh it with our scraping API vs self-managed proxies breakdown, not on sticker price.

Do not buy on a comparison table, this one included. Every vendor here has a free tier, a trial, or an entry price under $5, so run three measurements against your real targets first.

Step one: confirm the category works. Point ten free proxies at your target and record the status codes. If datacenter IPs are blocked outright, stop and go read about residential options.

curl -x http://USER:PASS@gateway.sparkproxy.io:11000 https://api.ipify.org

Step two: measure bytes per record. This is the input to every per-GB decision and almost nobody measures it. Pull 200 pages, sum the response sizes, divide by the usable records you extracted. Above roughly 500 KB per record, per-GB pricing will hurt and you should be shopping per-IP or per-thread.

import requests

PROXY = "http://USER:PASS@gateway.sparkproxy.io:11000"
proxies = {"http": PROXY, "https": PROXY}

total_bytes, ok = 0, 0
for url in target_urls[:200]:
    try:
        r = requests.get(url, proxies=proxies, timeout=20)
        total_bytes += len(r.content)
        if r.status_code == 200:
            ok += 1
    except requests.RequestException:
        pass

print(f"success rate: {ok / 200:.1%}")
print(f"avg bytes/response: {total_bytes / 200:,.0f}")

Step three: find the per-IP ceiling. Hold one address and keep requesting until the status codes change. That count decides how many addresses a per-IP plan needs: the difference between an $84 bill and an $840 one. Our proxy testing guide has the full procedure.

To skip the proxy layer entirely, one call against the managed API answers the reachability question:

curl -G "https://scrape.sparkproxy.io/api/v1" \
  -H "X-API-Key: YOUR_API_KEY" \
  --data-urlencode "url=https://example.com/product/123" \
  --data-urlencode "render_js=false" \
  --data-urlencode "country_code=US"

render_js=false drops the cost from 5 credits to 1, and the response carries X-Credits-Used and X-Duration-Ms headers so you can cost the job before you scale it.

Three measurements, two hours, and you will know which of the six is the right shape for your workload. That beats any ranking, this one included.