ZenRows Alternatives: Compare by Credit Mix, Not Price

Four reasons come up repeatedly, and only one of them is answered by changing vendor.

The credit balance drains faster than the request count. This is the big one. A credit is not a request. If your client sets js_render=true as a habit, every request costs five times a plain fetch, and if it also carries premium_proxy=true the multiplier is twenty five. Teams read that as expensive pricing. It is usually an expensive default.

Concurrency, not volume, is what throttles the pipeline. ZenRows publishes a concurrency ceiling per plan tier: 5 on Free, 20 on Build, 50 on Launch, 100 on Growth, 200 on Scale, and 400 to 1,000 or more on Enterprise (ZenRows Fetch docs, read 31 August 2026). A team that only needs a modest credit allowance but wants 80 workers running is buying a plan for the concurrency line, not the credit line, and that feels like paying for the wrong thing.

The product moved and the buyer did not. ZenRows has been repositioning around agent and AI workloads. The API formerly called Universal Scraper API is now Fetch, joined by Extract, Batch, Browser Sessions, Search and Crawl, and the standalone proxy product is labelled "Residential Proxies (Legacy)" in their own documentation navigation. If you originally bought raw residential proxies with an API bolted on, the centre of gravity has moved away from you.

You never needed the managed layer. Some workloads are a plain HTTP GET against a JSON endpoint that has no bot protection at all. Paying any scraping API for those is paying for insurance you are not claiming. Our own breakdown of web scraping API vs self-managed proxies covers where the line sits.

Shortlisting is impossible until you name the line item you are replacing. ZenRows is a platform, not a single endpoint.

ZenRows describes the premium pool as more than 55 million residential IPs across 190 or more countries with 99.9% uptime, and states that every plan shares one credit balance across the primitives, that failed attempts are not charged, and that 404 and 410 responses count as usable results (zenrows.com/pricing, read 31 August 2026). Those are real strengths. A shared balance across six primitives is genuinely convenient, and per-success billing is the correct model for this category.

The important row is the last one. If raw residential proxies are what you buy, note the word "Legacy" and plan accordingly. We are not a candidate there either, and this article says so twice more.

Here is the fact that reframes the whole comparison. The major scraping APIs have converged on the same four-rung price structure, to the same weights.

ZenRows weights are from their Fetch documentation, read 31 August 2026. SparkProxy weights are from our own scraping API docs. Both sides avoid charging for failures: ZenRows bills only successful requests, and SparkProxy deducts up front then refunds automatically on a 500 or a 530.

Read the table again. The base ladder is identical. The add-on packaging differs, and that difference cuts both ways. ZenRows folds geo targeting and JS instructions into the premium tier, while SparkProxy charges 5 credits each on top of a cheaper base. For a workload that needs geo targeting on every single request, the ZenRows packaging is the better shape. For a workload that needs a browser but no geo and no premium exit, the SparkProxy 5-credit rung is the better shape. Neither is a discount. They are different bundles.

The practical consequence is blunt. If two vendors charge the same credits for the same configuration, a vendor switch cannot fix a credit-mix problem. It can only change the price per credit, which in this category moves within a narrow band. Fixing the mix moves your bill by an order of magnitude.

Take 100,000 requests a month and price three mixes on the shared ladder above.

Same request count, same price list, a ten times spread. The plan tier you need is decided by a property of your own code, not by the vendor's price page.

So find your mix. This script replays a sample of your real target URLs against the cheapest rung and reports what fraction actually needs an upgrade.

import concurrent.futures as cf
import requests

API = "https://scrape.sparkproxy.io/api/v1"
KEY = "YOUR_API_KEY"

def cheapest_rung(url):
    """Try the 1-credit rung first. Report whether it was enough."""
    r = requests.get(
        API,
        headers={"X-API-Key": KEY},
        params={
            "url": url,
            "render_js": "false",          # 1 credit
            "json_response": "true",       # returns status_code and credits_used
            "transparent_status_code": "true",
        },
        timeout=90,
    )
    body = r.json()
    status = body.get("status_code")
    words = (body.get("meta") or {}).get("wordCount", 0)
    # A soft block is a 200 with almost no content. Count words, not statuses.
    ok = status == 200 and words > 120
    return url, ok, status, words, body.get("credits_used")

with open("targets.txt") as fh:
    urls = [line.strip() for line in fh if line.strip()]

with cf.ThreadPoolExecutor(max_workers=8) as pool:
    rows = list(pool.map(cheapest_rung, urls))

passed = sum(1 for _, ok, *_ in rows if ok)
print(f"{passed}/{len(rows)} URLs served fully on the 1-credit rung")
for url, ok, status, words, credits in rows:
    if not ok:
        print(f"  needs upgrade: {url}  status={status} words={words}")

Read the output in three buckets:

• Above roughly 90% passing. Your default should be render_js=false. Upgrade the named exceptions per domain, not globally.
• Between 20% and 90%. Split by domain. One config per domain group beats one config for the whole crawler, every time.
• Below roughly 20%. The targets genuinely fight back. Budget for the 25-credit rung and stop optimising the ladder.

The word count check matters more than it looks. A soft block returns HTTP 200 with a short interstitial, so a script that counts status codes will report a clean sweep and you will size your plan against a fantasy. json_response=true hands you meta.wordCount without parsing anything yourself.

Rates are omitted deliberately. Several of these vendors reprice quarterly and some publish rates only behind a configurator. Read the vendor's own page before you buy, and where the unit column says "see vendor page" it is because a guess would be wrong within a month.

Every scraping API has two meters and buyers only shop the first one. Credits cap what you may spend. Concurrency caps how fast you may spend it.

ZenRows documents its ceilings per tier and, more usefully, documents a behaviour almost nobody accounts for: cancelling a request on the client side does not free the slot. The server keeps processing, the slot stays occupied for up to three minutes, and the symptom is a wave of 429s that looks like target-side rate limiting but is self-inflicted (ZenRows Fetch docs, read 31 August 2026). If your HTTP client has an aggressive timeout and retries on it, you are burning slots faster than you are consuming them.

SparkProxy surfaces the same class of limit in the error body rather than only in headers:

{
  "error": "Concurrency limit reached",
  "active": 3,
  "limit": 3,
  "retry_after_seconds": 5
}

Either way the fix is the same, and it lives on your side of the wire. Read the limit, respect retry_after_seconds, and back off with jitter rather than a fixed sleep. Our guide to retry and backoff strategies for web scraping has the implementation, including why a naive fixed retry turns one 429 into a thundering herd.

One more shared trap before you migrate: both APIs cap response size. ZenRows returns 413 Content Too Large with no partial data when a page exceeds the limit. The mitigation is identical everywhere, which is to stop asking for whole pages. Use format=md to strip the page to readable text, or extract_rules to pull only the fields you want, and the size problem disappears along with a chunk of your parsing code.

Being direct about this, because a comparison page that pretends to be neutral is worse than one that declares its position.

Where we are a real candidate. Replacing ZenRows Fetch for HTML, Markdown, MDX, JSON, screenshot or PDF output. Our API sits at https://scrape.sparkproxy.io/api/v1, authenticates with an X-API-Key header, and runs the same credit ladder. Signup gives 1,000 free API credits with no card, which is enough to run the credit-mix script above against a few hundred real URLs before you commit to anything.

import requests

r = requests.get(
    "https://scrape.sparkproxy.io/api/v1",
    headers={"X-API-Key": "YOUR_API_KEY"},
    params={
        "url": "https://example.com/pricing",
        "render_js": "true",       # 5 credits
        "wait_for": "#price-table",
        "country_code": "DE",      # +5 credits, ISO 3166-1 alpha-2
        "format": "md",            # clean Markdown instead of raw HTML
    },
    timeout=120,
)
print(r.text)

Where we are not a candidate at all. We sell datacenter proxies as our proxy product. We do not sell residential, ISP or mobile proxy plans. If you are replacing the ZenRows residential proxy product with raw residential exits, we are not on your list, and no amount of framing changes that. We also have no equivalent of Browser Sessions: there is no remote browser you connect Playwright to over CDP. And we have no auto-parse mode, so nothing here substitutes for ZenRows autoparse or extract=auto. Our extract_rules expects you to write CSS selectors.

Where the proxy side matters. If your credit-mix test shows most targets pass on the plain rung, the cheapest move is to stop buying per-request scraping for those domains entirely and run them through flat-rate datacenter proxies with your own HTTP client. Different product, different meter, covered in datacenter proxy pricing models. Ours run on gateway.sparkproxy.io: port 11000 for rotating HTTP and HTTPS, 11002 for sticky sessions, 13000 for SOCKS5, which is TCP only, so if you need UDP forwarding we are not it. There is a 24-hour trial on the proxy side, separate from the API credits.

# Rotating datacenter exit, new IP per connection
curl -x http://USER:PASS@gateway.sparkproxy.io:11000 https://example.com/api/items

# Sticky session, same exit held across a multi-step flow
curl -x http://USER:PASS@gateway.sparkproxy.io:11002 https://example.com/cart

This is the part of a migration that eats a week if you do it by guesswork. ZenRows uses one parameter dialect. SparkProxy and ScrapingBee use a different one that happens to be close to each other. Most names map cleanly.

Now the four that will bite you, because the name survives the port and the behaviour does not.

Trap 1: the render default flips

ZenRows js_render defaults to false. SparkProxy render_js defaults to true, and so does ScrapingBee's. A naive port that copies the URL across and drops the parameter because "it was false anyway" moves every request from the 1-credit rung to the 5-credit rung. Five times the bill, no code change, no error, no warning. Set it explicitly on every call. Always.

Trap 2: wait changes units

ZenRows wait is milliseconds. SparkProxy wait is seconds, capped at 30. Porting wait=3000 verbatim asks for 3,000 seconds and gets rejected against the cap. The value you want is 3.

Trap 3: json_response means two different things

On ZenRows, json_response captures the page's own network traffic, the XHR and Fetch calls. On SparkProxy and ScrapingBee, json_response wraps the API response in a JSON envelope carrying job_id, status_code, duration_ms and credits_used. Identical name, unrelated function. If you were using it to intercept a hidden endpoint, the SparkProxy answer is to call that endpoint directly, which is faster and cheaper anyway. The method is written up in how to scrape hidden JSON API endpoints.

Trap 4: session_id is not a session on our side

ZenRows session_id holds the same exit IP for up to 10 minutes. SparkProxy session_id is a label attached to the browser profile in per-job logs, and it explicitly does not persist cookies or pin an IP across requests. If your flow depends on IP stickiness, the SparkProxy answer is the sticky proxy port 11002 fed in through own_proxy, or injecting the session cookie directly with the cookies parameter. Do not assume the name carried its meaning across.

One further difference that is not quite a trap: ZenRows block_resources is enabled by default, SparkProxy's is false by default. Turn it on after you port, or your rendered pages will pull images and fonts nobody is reading.

The credit ladder is identical across vendors. Batch billing is not, and for high-volume, low-protection targets it is the largest single difference on this page.

ZenRows Batch bills at the same per-request rate as Fetch, per their pricing page read 31 August 2026. Reasonable and predictable: a thousand URLs costs a thousand requests' worth of credits.

SparkProxy batch mode works differently. Pass comma-separated URLs in a single url parameter with render_js=false, and the whole batch costs 1 credit regardless of URL count.

import requests

urls = [
    "https://example.com/p/1",
    "https://example.com/p/2",
    "https://example.com/p/3",
]

r = requests.get(
    "https://scrape.sparkproxy.io/api/v1",
    headers={"X-API-Key": "YOUR_API_KEY"},
    params={
        "url": ",".join(urls),
        "render_js": "false",   # required for batch mode
    },
    timeout=180,
)
data = r.json()
print("credits:", data["credits_used"])   # 1, for the whole batch
for row in data["results"]:
    print(row["url"], row["httpStatus"], row["success"])

Weigh the constraint honestly: batch mode only works without a browser. It buys you nothing on targets that need rendering, which is most of the hard ones. But if your credit-mix test came back above 90% passing on the plain rung, this is where the real saving lives, and it is a structural difference that no amount of per-credit discounting from another vendor can match.

For long jobs that do need a browser, the async path is callback_url. You get a 202 immediately with a job ID, and the full result JSON is POSTed to your endpoint once Chromium finishes.

A capability worth knowing about, because it changes the shortlist for anyone mid-contract. SparkProxy and ScrapingBee both expose an own_proxy parameter. ZenRows Fetch has no equivalent.

import requests

r = requests.get(
    "https://scrape.sparkproxy.io/api/v1",
    headers={"X-API-Key": "YOUR_API_KEY"},
    params={
        "url": "https://example.com",
        "render_js": "true",
        # Your existing residential vendor, our browser and stealth layer
        "own_proxy": "http://user:pass@your-residential-vendor.example:8080",
    },
    timeout=120,
)

This decouples two purchases that vendors normally sell as one. You keep a residential contract you have already paid for and rent only the browser, fingerprinting and retry layer on top. It also gives you a migration path that does not require cancelling anything: point the new API at your existing exits, compare success rates against the incumbent on identical IPs, and you have isolated exactly one variable. Accepted formats are ip:port, ip:port:user:pass, http://user:pass@host:port and socks5://host:port, with SOCKS5 requiring render_js=true.

Genuinely, several groups should close this tab.

You draw on more than two primitives from one balance. If Fetch, Extract, Batch and Browser Sessions all bill from the same credit pool and you use three of them, unbundling across three vendors costs more in integration and reconciliation than you will save on credits. Nobody else on the list above sells that combination under one balance.

You depend on Browser Sessions. A real remote browser you drive with Puppeteer or Playwright, staying logged in across a long job, is a different product from a scraping API. Replacing it means Browserless, Steel or Bright Data's Scraping Browser, not a URL-in-HTML-out endpoint. Do not move a working session-based flow onto a stateless API and expect it to survive.

mode=auto is doing real work for you. Adaptive Stealth Mode starts on the cheapest configuration and escalates only when needed, and ZenRows bills only the configuration that succeeded. If you run hundreds of heterogeneous domains and nobody on the team has time to tune per-domain configs, that automation is worth paying for, and hand-tuning would cost more engineer hours than it saves in credits.

You need auto-extraction without writing selectors. autoparse and Extract remove parser maintenance entirely on supported sites. For a small team scraping common e-commerce or listing sites, that is a genuine labour saving a raw HTML API does not replicate.

Your targets are genuinely hostile. A 55 million IP residential pool across 190 or more countries is a large asset. If you sit on the 25-credit rung because you have to, a cheaper vendor with a smaller premium pool is not cheaper, it is just a different failure rate.

Order matters more than vendor choice.

1. Run the credit-mix script on your real target list, not a sample of easy URLs. The output is your migration plan and your plan-tier decision, in that order.
2. Fix your defaults before you switch vendors. If the test says 70% of your traffic passes on the plain rung, setting js_render=false explicitly on ZenRows recovers most of the money you were hoping to save by leaving. Do that first. If the bill is acceptable afterwards, you have finished.
3. Port one domain group, not a percentage of traffic. A percentage split across all targets gives you a blended success rate that hides which domain broke.
4. Hold every other variable constant. Swap the API while keeping your parser, your concurrency and your retry policy identical. Change two things and a regression is unattributable.
5. Diff the four traps line by line. Render default, wait units, json_response semantics, session_id semantics. Grep your codebase for each name.
6. Compare on identical exits using own_proxy. Point the new API at the same IPs the incumbent used. Whatever difference remains is the browser and stealth layer, which is the thing you are actually buying.
7. Instrument content length, not status codes. Carry the wordCount check from the test into production monitoring. Migrations that "worked" and then quietly degraded were counting 200s.
8. Keep the old account alive for one full billing cycle. A rollback that costs one month beats a rollback that costs a re-integration.

• Your credit balance drains faster than expected: do not switch yet. Fix the render default and re-measure. If your mix is 70% plain and you were paying the browser rung on all of it, you just cut your bill by more than any vendor switch would.
• You want the cheapest plain-HTTP rung at volume: SparkProxy, where a whole comma-separated batch costs 1 credit without a browser, or ScrapingBee for the closest thing to a drop-in dialect.
• You want managed unblocking at enterprise scale: ScraperAPI, Zyte API or Bright Data Web Unlocker. Per-request billing, deep unblocking, no bandwidth exposure.
• You want Markdown for an LLM pipeline: Firecrawl if crawling and discovery are part of the job, or format=md on any HTML API if you already hold the URLs.
• You want finished records rather than responses: Apify or Zyte automatic extraction. Do not buy a raw HTML API and then write the parser you were trying to avoid.
• You are mid-contract with a residential vendor: pick an API with own_proxy so you keep the exits and replace only the browser layer.
• You use three or more ZenRows primitives, or Browser Sessions: stay. Unbundling costs more than it saves.

The honest finish for a page like this one: for a large share of readers the correct action is not to switch vendors at all. It is to open the client, find the line that sets the render flag, and measure what it has been costing you.