Dolphin Anty vs GoLogin: Antidetect Browser Compared

Both tools sell the same core promise: every browser profile gets its own device identity and its own storage, so two profiles on one machine read as two computers rather than one computer in two windows. Both are Chromium derivatives. Both let you attach a proxy per profile. If you want the full landscape rather than this pair, we ranked the field in Best Antidetect Browsers: Top 12 Compared, and the signals these tools try to control are broken down in what is browser fingerprinting.

Past that shared surface they were built for different shapes of team.

Dolphin {anty} is a desktop workbench. It assumes an operator sitting at a machine with the app open, working through a list of profiles, and it ships tooling for exactly that: a scenario builder for repeating browser actions across profiles, and a Cookie Robot that visits a list of sites you supply so a fresh profile has some browsing history before you use it (Dolphin's own writeup). The vendor also markets a separate Dolphin Cloud product aimed at Facebook Ads campaign management, which is a different purchase from the browser.

GoLogin is built around reaching a profile from wherever you happen to be. Alongside the Windows, macOS and Linux desktop apps, the vendor advertises a cloud browser, a web version, an Android app, and a Cloud Android product for mobile profiles (gologin.com, checked August 18, 2026). Its paid tiers are metered partly in "profile shares," which is the unit that matters when contractors need access to specific profiles and nothing else.

That is the actual fork in the road. One tool optimizes the operator's desk. The other optimizes distributed access.

One compliance note, stated once and not repeated: many platforms prohibit multi-accounting in their terms of service, and some jurisdictions regulate automated account creation. This article is written for people doing ad verification, localization QA, competitive research, privacy research and legitimate multi-brand account management. Read the terms of the platform you operate on before you build a process on top of either tool.

About the numbers in this article

Everything below that involves a price, a profile cap or a seat allowance was read off the vendors' own pricing pages on August 18, 2026 and is attributed to them. Both vendors change plans, rename tiers and adjust caps frequently, and promotional pricing distorts the annual columns further. Treat every figure here as a snapshot, and confirm current terms on dolphin-anty.com/tarifs and gologin.com/pricing before you budget. We won't restate a vendor's marketing claim as our own finding.

All rows are what the vendors publish, dated August 18, 2026, not our measurements.

Two rows in that table matter more than the rest: the seat row and the access row. They are the two places where these tools genuinely diverge, and both get skipped in the feature grids that dominate search results for this comparison.

Almost every Dolphin Anty vs GoLogin comparison online compares profile allowances. That's the wrong axis for anyone with a team, because profiles are the cheap resource and people are the expensive one.

Dolphin's published plans each include one user and price additional users as an add-on: roughly $10 per user on Base, $20 on Team, $25 on Enterprise, per the pricing page on August 18, 2026. Seats scale linearly and visibly.

GoLogin's tiers all list a single team member and scale access through profile shares instead: 10 on Professional, 100 on Business, 1,000 on Enterprise, 2,000 on Custom.

Work a concrete case. A six-person research team needs roughly 250 profiles, with each analyst touching a subset.

• Dolphin route. Team plan at $159 per month covers up to 300 profiles, plus five extra users at $20 each. Listed total: $259 per month.
• GoLogin route. Business at $49 per month covers 300 profiles and 100 profile shares. Listed total on the monthly column: $49 per month, with access distributed by sharing profiles rather than buying seats.

That's a five-fold gap on published prices, and it exists because the two vendors model teams differently, not because one is generically cheaper. The corollary matters just as much: a single operator running 60 profiles pays a listed $10 on Dolphin Starter against a listed $24 on GoLogin Professional, so the ordering flips for solo work.

One caution before you copy those numbers into a spreadsheet. GoLogin's pricing table presents a monthly figure and an annual figure per tier, and the annual figure reads higher than the monthly one, which is the opposite of the usual convention. We could not resolve which number applies to which billing cadence from the page alone. Confirm at checkout rather than from any comparison article, ours included.

The sharing model also carries a governance cost that a seat model doesn't. A shared profile is access delegated to a person who doesn't appear in a seat list. If your compliance posture requires naming who can open which client account, a per-seat model with an audit trail is worth paying for, and that argument favors Dolphin regardless of the arithmetic above.

This is the difference that changes daily work, and it explains the access row in the table.

Dolphin binds a profile to the local application. Launching means the desktop app is running on that machine and the profile's data directory is available to it. Cold start is fast because nothing gets fetched. The tradeoff is that the machine becomes part of your infrastructure: back it up, keep it patched, keep it online.

GoLogin hydrates profiles from cloud storage. The SDK's documented flow is that you supply a token and a profile ID, the SDK downloads and prepares that profile, then hands back a WebSocket endpoint to attach to (gologinapp/gologin). First launch on a new machine pays a download cost. In exchange, the same profile opens on a laptop today and a different laptop tomorrow with cookies and storage intact, and the cloud browser removes the local machine from the picture entirely.

Neither model is better in the abstract. Pick against three questions:

1. Do people change machines? Cloud hydration is worth its latency the moment the answer is yes.
2. Is your bandwidth constrained or metered? Repeated hydration of large profiles is a real cost that nobody mentions until the profiles get fat with cache.
3. What happens when a machine dies? A local-first setup needs a backup plan you write yourself.

There's a subtler consequence for automation. Because GoLogin's launch step involves a network fetch, launch latency varies with profile size and connection quality, and it isn't constant across runs. Orchestration code that assumes a fixed startup window will flake intermittently. Read the returned endpoint and wait on readiness instead of sleeping a hardcoded number of seconds.

Both tools converge on the same endpoint: a Chromium instance exposing the Chrome DevTools Protocol, which Puppeteer, Playwright and Selenium all speak. What differs is how you get the WebSocket URL.

Dolphin exposes a local HTTP API on port 3001. You start a profile and read the endpoint out of the response:

# Dolphin {anty}: start a profile for automation, desktop app must be running
curl -s "http://localhost:3001/v1.0/browser_profiles/123456/start?automation=1"

# stop it when the run finishes
curl -s "http://localhost:3001/v1.0/browser_profiles/123456/stop"

GoLogin's first-party SDKs wrap the equivalent step and handle the profile download before returning the endpoint. The Python client follows the same shape as the Node one:

# GoLogin: SDK prepares the cloud profile, then returns a CDP endpoint
from gologin import GoLogin
from selenium import webdriver

gl = GoLogin({"token": "YOUR_DEV_TOKEN", "profile_id": "YOUR_PROFILE_ID"})
debugger_address = gl.start()

options = webdriver.ChromeOptions()
options.add_experimental_option("debuggerAddress", debugger_address)
driver = webdriver.Chrome(options=options)

driver.get("https://www.sparkproxy.io/")
print(driver.title)

driver.quit()
gl.stop()

The practical difference is dependency shape. Dolphin's is a plain HTTP call you can make from any language with zero vendor libraries, which is pleasant in Go, Rust or a shell script, and it means nothing breaks when a vendor SDK falls behind. GoLogin gives you maintained first-party SDKs in Node and Python, which is less code to write if you're already in those languages and a versioned dependency to track if you aren't.

Write your own thin wrapper either way. Both vendors have shipped breaking changes to local API paths, and code that calls start and stop in forty places is code you will rewrite by hand.

Neither browser touches the network layer. The fingerprint claims "Windows 11 desktop in Munich" and the IP claims whatever your proxy exit claims. When those disagree, a profile is more suspicious than a plain Chrome window would have been, because ordinary users don't present a German locale from a Singapore datacenter range.

Three pairing rules apply identically to both tools.

Match the timezone to the exit, not to your desk. Both tools can set a profile timezone. Set it from the exit IP's IANA zone, then confirm that JavaScript agrees by running Intl.DateTimeFormat().resolvedOptions().timeZone in the profile's console. A profile whose JS timezone reports your own city is the single most common self-inflicted failure in this category.

Match locale and language headers to the region. Accept-Language and the client hints Chromium sends are separate from timezone and drift independently. Chrome's User-Agent Client Hints documentation covers what a modern Chromium actually advertises, which is a shorter list than the old User-Agent string implied.

Keep the IP stable for the life of the session. A session that changes exit IP mid-login looks like account compromise to any risk engine. For logged-in profile work you want a sticky session proxy held for the duration, not per-request rotation. Rotation belongs to scraping, not to account work.

Proxy type follows the target. Consumer platforms that score IP reputation want residential exits; internal tools, staging environments and price-monitoring targets are usually fine on datacenter, and the tradeoffs are laid out in residential vs datacenter proxies. GoLogin advertises bundled residential and mobile proxies, which is genuinely convenient for small setups. Dolphin's pricing page doesn't present bundled proxies, so you bring your own. Bundled convenience costs you the ability to compare exit quality independently, which is why teams past a certain size end up bringing their own regardless of what ships in the box.

An antidetect browser is a device-identity tool. Three problems sit outside its scope entirely.

Behavior. Fingerprints are static; behavior isn't. A profile that logs in at machine speed at 03:00 every day and clicks the same pixel coordinates is identifiable regardless of how clean its canvas hash is. No profile manager fixes that.

Account history. A profile with no history is a new account, and new accounts get scrutiny. Cookie Robot addresses the first hour of that problem, not the first month of it.

Data collection at scale. This is the big one, and it's where most teams overbuy. If your goal is pulling public pages rather than operating logged-in accounts, running fifty browser profiles is an expensive way to make HTTP requests. Every profile costs RAM, a seat, a proxy session and an operator's attention.

For collection work, an API call is the cheaper path. SparkProxy's Scraping API handles rendering, proxy selection and retries behind one request:

curl -X POST "https://scrape.sparkproxy.io/api/v1" \
  -H "X-API-Key: sk-xxxxxxxxxxxxxxxx" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://example.com/pricing",
    "render_js": true,
    "format": "md",
    "stealth": true
  }'

Or from Python, with a geolocated request for regional pricing checks:

import requests

resp = requests.post(
    "https://scrape.sparkproxy.io/api/v1",
    headers={"X-API-Key": "sk-xxxxxxxxxxxxxxxx"},
    json={
        "url": "https://example.com/de/pricing",
        "render_js": True,
        "format": "json",
        "premium_proxy": True,
        "country": "de",
    },
    timeout=90,
)

data = resp.json()
print(data["status_code"], data["credits_used"], data["meta"]["title"])

The honest split: browser profiles for anything requiring a logged-in identity, an API for anything that's really just fetching pages. Teams that draw that line early spend less on both. Ad verification is the clearest example of a workload that splits down the middle, and we covered how that plays out in how brands use proxies for ad verification.

Both vendors publish a free tier, so run this before spending anything. Five profiles on Dolphin and three on GoLogin is enough.

1. Create one profile per tool on the same proxy exit. Same country, ideally the same endpoint. This is your controlled variable.
2. Verify the timezone agrees with the exit. Open the console in each profile and run Intl.DateTimeFormat().resolvedOptions().timeZone. If it reports your own city, you configured the profile wrong. Fix it now, because you'll make the same mistake at scale.
3. Check locale consistency. Compare navigator.language and navigator.languages against the exit region in both profiles.
4. Time a cold start on a second machine. Install GoLogin on a different computer and open the same profile. Record the wait. That number is your daily tax if your team moves between machines.
5. Attach an automation script to each. Use the two snippets above. What you're measuring isn't whether it works, it's how much code you had to write around each vendor's launch step.
6. Restart both apps and reopen every profile. Confirm cookies and logged-in state survived. Persistence bugs surface on restart, not on first run.
7. Close and reopen one profile eight times in a row. Look for fingerprint drift between launches. A profile whose values change run to run is worse than useless for account work.

Steps 4 and 7 are the ones that decide it. Everything before them, both tools will pass.

Choose Dolphin {anty} if: you're a solo operator or a small desk-bound team; the listed $10 Starter tier covers your profile count and the seat add-on math still works at your headcount; you want warmup and repeatable scenarios in the product rather than in your own scripts; you prefer a plain HTTP local API with no vendor SDK in your dependency tree; you need per-seat accountability over who can open what.

Choose GoLogin if: people need the same profiles from more than one machine, from a browser tab, or from a phone; you have contractors who should see a handful of shared profiles and nothing else; you want first-party Node or Python SDKs rather than community clients; bundled proxies remove a purchasing step you'd rather not take yet; the share-based model prices your team shape better than per-seat does.

Choose neither if the work is fetching public pages. Run it through an API and keep browser profiles for identities that genuinely need to stay logged in.

If your headcount is uncertain, price both against your worst case rather than your current one. Seat-based costs grow with hiring and share-based costs don't, which means the cheaper tool today is frequently not the cheaper tool in nine months.