The best Browserbase alternative in 2026 for protected web access

This article was originally published on the Zenrows blog. Read the original here: https://www.zenrows.com/blog/best-browserbase-alternative-for-protected-web-access

Architecture overview

Browserbase and Zenrows solve adjacent problems that look identical until you measure them. Browserbase provides managed headless browsers for agents to drive, which is the correct architecture when a workflow requires navigation, clicking, authentication, and session state held across steps. Zenrows provides a data-extraction layer that returns the content of protected pages without operating a browser at all.

The distinction matters because of how each one bills. Browserbase meters browser session time and proxy bandwidth, so a failed session still consumes billable infrastructure. Zenrows meters successful requests. On a workload with a meaningful failure rate against protected targets, that difference compounds into a large cost gap, and our benchmark shows the failure rate on protected sites runs high.

We tested Zenrows Fetch against Browserbase Fetch on four protected targets and one unprotected page. Zenrows returned a 100 percent success rate. Browserbase Fetch returned 62.25 percent across the protected targets. This guide walks through the cost model, the benchmark methodology and results, and the migration path, so you can judge which architecture fits your workload.

What you'll take away

• The cost model behind time-based versus success-based billing

• Benchmark methodology across four anti-bot systems

• A migration path from Browserbase Fetch to Zenrows Fetch

• A decision boundary for when each platform is correct

Benchmark script and CSV results: GitHub

The cost model

Why time-based billing is hard to predict

Browserbase bills browser session time and proxy bandwidth separately. The consequence is that the final bill depends on two variables you have to estimate before you can compute it, average session duration and bandwidth per request. A failed session still runs the browser and still moves bytes, so failures land on the invoice at close to the cost of successes.

Model a concrete workload. 10,000 page attempts per day against a Cloudflare-protected site at a 40 percent failure rate, assuming one minute per extraction, consumes roughly 5,000 browser-hours and about 300 GB of proxy bandwidth per month.

• Browserbase Developer ($20/month) runs an estimated $4,196/month for browser execution and proxy bandwidth alone

• Browserbase Startup ($99/month) runs an estimated $3,499/month, same exclusions

Both figures exclude Fetch, Extract, Search, and model tokens.

Why success-based billing changes the calculation

Zenrows starts the calculation from a different place, the number of successful requests and the configuration each required. A basic request is one credit; JavaScript rendering adds a 5x multiplier, residential proxies add 10x, and both together add 25x, weights that hold on every plan.

Using the 100 percent success rate from our benchmark, all 10,000 daily attempts succeed. Some Cloudflare-protected requests in our test used both JavaScript rendering and premium proxies. If every request required both, the workload consumes 7.5 million credits per month, which runs $622 on the Scale plan. The number is knowable in advance because it derives from successful requests rather than from session durations you estimate.

The extraction architecture

Zenrows Fetch uses mode=auto to adapt request configuration to the target's protection level, activating JavaScript rendering, residential proxies, or both only when a page requires it. This removes two things you would otherwise build and maintain around a browser, the browser lifecycle and the per-request recovery logic when a session fails.

# pip install requests
import os
import requests

API_KEY = os.environ.get("ZENROWS_API_KEY")
TARGET_URL = "https://www.ikea.com/us/en/p/kallax-shelf-unit-white-80275887/"

params = {
    "url": TARGET_URL,
    "apikey": API_KEY,
    "mode": "auto",  # escalates to JS rendering and/or proxies only when the target needs it
}

response = requests.get("https://api.zenrows.com/v1/", params=params)
print(f"status code: {response.status_code}")

Fetch also returns Markdown, plain text, or structured JSON through Extract, selected with response_type, so RAG and LLM pipelines skip manual HTML parsing. For bulk work, Batch processes up to 100,000 URLs per job with queuing, concurrency, retries, and delivery handled internally.

Benchmark methodology

We tested on September 24, 2026, 100 requests per target at 2 req/s, each platform on its default configuration. Zenrows ran mode=auto. Browserbase ran default Fetch, where the proxy network is disabled by default. We kept defaults to evaluate each platform's standard Fetch workflow without browser sessions. The five targets each represented a different protection system.

Target	Protection	Zenrows	Browserbase Fetch	Zenrows avg time	Browserbase avg time
IKEA product page	Cloudflare	100%	100%	1.81s	1.75s
TripAdvisor listings	DataDome	100%	0%	5.63s	0.61s
Walmart product page	PerimeterX	100%	100%	4.52s	1.68s
Amazon product page	Akamai	100%	49%	14.23s	1.36s
Python 3.14.7 docs	none	100%	100%	0.61s	0.52s

Reading the results

Zenrows returned 100 percent across the four protected sites because mode=auto escalated per target. Browserbase Fetch averaged 62.25 percent across the protected targets. The zero on TripAdvisor is the diagnostic result. Default Fetch executes no JavaScript and disables proxies, and DataDome requires browser fingerprinting through the Sessions API, which reintroduces the browser-session management layer that Fetch was supposed to avoid.

The response-time column needs careful reading. Zenrows averaged 5.36s per target against Browserbase's 1.18s. But Browserbase averaged 0.61s on TripAdvisor while returning a 403 on all 100 requests, and Zenrows retrieved the target. A faster response is only meaningful if it returns the requested content, so response time and success rate have to be read as one measurement rather than two separate ones.

Migration path

Migrating a basic Fetch request changes four things, the endpoint, authentication, HTTP method, and parameters. The target URL and your content-handling code stay the same.

Browserbase Fetch is a POST to https://api.browserbase.com/v1/fetch with the URL in the JSON body, returning raw content by default, with Markdown and JSON behind the separate Extract feature. Zenrows is a GET to https://api.zenrows.com/v1/ with config as query parameters, keeping protected retrieval and structured output inside one workflow. The architectural difference is that Browserbase separates access and structure across two features, and Zenrows keeps them in Fetch.

When Browserbase is the correct architecture

Browserbase is the right choice when an agent needs to operate a browser rather than retrieve content.

1. An agent operates inside websites. Browserbase gives it a headless browser it controls through CDP or Playwright, with Stagehand's act, observe, and extract supporting natural-language browser actions.

2. You need session replay and visual debugging. Live View exposes real-time DOM inspection, and Session Inspector exposes network activity and timelines for workflows that are hard to reproduce.

3. Existing Playwright automation with a custom proxy stack. Browserbase moves the browser runtime to managed infrastructure without rewriting the workflow.

4. Chrome extensions or persistent browser state. Browserbase persists cookies, tokens, and local storage across requests and supports uploaded extensions. Zenrows Browser Sessions provides remote Chrome but does not currently support custom extensions.

Decision boundary

The choice reduces to one question: does the workload need to drive a browser, or retrieve data from protected pages? Browser navigation, login and session state across steps, session replay, or Chrome extensions point to Browserbase or Zenrows Browser Sessions. Structured data from protected sites billed per successful request points to Zenrows Fetch. Sign up for Zenrows and test your own targets.

What to build next

• Best Bright Data alternative for the proxy-network comparison

• Best Firecrawl alternative for the AI-native extraction comparison

• Zenrows Fetch documentation for the full parameter reference

• Benchmark repo: GitHub