--- title: Tabstack vs. Browserbase | Tabstack description: Tabstack is a web intelligence API. Browserbase is browser infrastructure. How they differ, when to use each, and how they work together in the same agent stack. --- Browserbase is browser infrastructure. Tabstack is a web intelligence API. They’re different layers of the stack. For many agent architectures, they’re not alternatives at all. Here’s the clearest way to understand the difference: Browserbase gives your agent a managed headless browser in the cloud. You write the code that tells it what to do, you write the LLM calls that extract structure from what it finds, you manage the sessions. Tabstack takes a URL and returns structured data - the extraction, reasoning, and research happen inside the API call before the response comes back. --- ## The core difference **Browserbase** is the AWS for headless browsers. It handles scale, concurrency, stealth, anti-detection, session persistence, and CAPTCHA solving. You bring your automation framework (Playwright, Puppeteer, Stagehand) and your intelligence layer (your LLM calls, your parsing logic, your schema validation). Browserbase owns the infrastructure; you own everything above the fetch. **Tabstack** handles the intelligence layer. Call `/extract/json` with a schema, get back exactly that JSON. Call `/research` with a question, get back cited answers from multiple sources. No browser to manage, no LLM calls to write, no parsing to maintain. The work happens inside the call. --- ## Intelligence: inside vs. outside the call This is the architectural distinction that matters. With Browserbase, every piece of intelligence - extraction, schema validation, LLM reasoning, structured output - is your code. You own and maintain it. When the page changes, your selectors break. When the LLM prompt produces inconsistent output, you debug it. With Tabstack, extraction, transformation, and research happen before the response returns. Your agent gets structured, validated output. When a page changes, Tabstack adapts - you didn’t write selectors, so nothing breaks. --- ## Stealth and anti-detection Browserbase wins here clearly. Browserbase has first-class stealth infrastructure: managed residential proxies, CAPTCHA solving, stealth mode, and session persistence. It’s purpose-built for production agents that face bot protection at scale. Tabstack documents robots.txt and User-Agent behavior. Anti-detection capabilities are not documented publicly - this is a real gap for production use cases that require operating on heavily protected sites. --- ## Scale and concurrency Browserbase is built for massive concurrency, with plan-tiered concurrent session limits. Session management, region routing, and autoscaling are all handled. Tabstack is a stateless per-call API. It scales horizontally with your request volume, but there are no managed browser sessions to configure - and no need for them if your use case is structured extraction from specific URLs. --- ## Feature comparison | Feature | Tabstack | Browserbase | | ---------------------------------- | ---------------------- | ------------------------------ | | Schema-driven JSON extraction | Yes - core product | No - raw browser only | | AI transformation inside call | Yes - `/generate/json` | No | | Autonomous research with citations | Yes - `/research` | No | | Managed browser sessions | No - stateless API | Yes - core product | | Stealth / anti-detection | Not documented | Yes - first-class | | CAPTCHA solving | Not documented | Yes | | Residential proxy network | Not documented | Yes | | Session recording and replay | No | Yes | | Playwright / Puppeteer compatible | No - own API | Yes - drop-in | | Concurrent browser fleet | N/A | Yes - plan-tiered concurrency | | Browser automation | Yes - `/automate` | Yes - core | | robots.txt compliance | Yes - by design | Configurable by implementation | | TypeScript SDK | Yes | Yes + Stagehand | | Python SDK | Yes | Yes | | Open source | No | Stagehand only | --- ## Who each is right for **Use Tabstack when:** - You need structured, schema-enforced JSON from specific pages without writing extraction code - Your agent needs to make one call and get processed data back, not manage browser state - Extraction, transformation, and multi-source research are the jobs to be done - Your agent architecture already handles orchestration and you need the web intelligence layer - TCO matters - fewer components to build, maintain, and debug **Use Browserbase when:** - Your agent needs raw managed browser sessions with maximum concurrency at scale - You’re hitting anti-bot systems, CAPTCHAs, or login-walled sites in production - Session recording, replay, and observability are required for debugging - You’re using Playwright or Stagehand and want a cloud infrastructure upgrade - SOC-2 or HIPAA compliance documentation is a procurement requirement - You want full control over the intelligence stack - your LLM, your prompts, your logic --- ## Honest gaps **Tabstack limitations vs. Browserbase:** Anti-detection and CAPTCHA handling not documented. No persistent browser sessions. No session recording or observability. No formal SOC-2 certification. **Browserbase limitations vs. Tabstack:** No structured extraction - raw browser content only. All LLM work is externalized. No `/research` endpoint. Higher operational complexity - you own the intelligence layer. --- ## The complementary architecture Many teams won’t choose between these. A complex agent might run on Browserbase infrastructure for browser session management, and call Tabstack’s `/extract/json` or `/research` for specific pages where structured, reliable output matters more than raw browser access. Browserbase for browser sessions. Tabstack for the intelligence call inside those sessions. --- ## Try Tabstack Get an API key at [console.tabstack.ai](https://console.tabstack.ai). ``` import Tabstack, { APIError } from '@tabstack/sdk' const client = new Tabstack({ apiKey: process.env.TABSTACK_API_KEY }) try { // Schema-enforced extraction -- no browser session to manage const result = await client.extract.json({ url: 'https://target.com/data', json_schema: { /* your schema */ } }) } catch (err) { if (err instanceof APIError) { console.error(err.status, err.message) } else { throw err } } ``` [Full documentation](https://docs.tabstack.ai)