--- title: Tabstack vs. Firecrawl | Tabstack description: Firecrawl is built for site-wide crawling and content ingestion. Tabstack is built for structured extraction and agent intelligence calls. How they compare and when to use each. --- Both are web data APIs. The difference is what you get back and what you pay to get it. Firecrawl turns pages and websites into markdown. It’s fast, it crawls entire sites, and its markdown output is genuinely good for LLM ingestion. If you need to ingest 10,000 pages of documentation into a vector store, Firecrawl’s `/crawl` endpoint is the right tool. Tabstack is built for a different job: getting structured, schema-enforced data from specific pages, with AI transformation and autonomous research built in. One product, intelligence inside the call. Here’s how they actually compare. --- ## Structured extraction Both products support schema-driven extraction from a known URL. **Tabstack** `/extract/json`: define a JSON schema, pass a URL, get back exactly that structure. Works on any page including JS-heavy SPAs with `effort: 'max'`. Included in the base product. **Firecrawl** `/scrape`: accepts a JSON Schema and optional prompt for single-page extraction. Use `/agent` for autonomous multi-page discovery without knowing URLs upfront. Firecrawl uses tiered, credit-based pricing; check firecrawl.dev/pricing for current plan details. Where they diverge: Tabstack bundles an AI transformation endpoint (`/generate/json`) — fetch a URL, apply custom AI instructions, return structured output in one call. Firecrawl doesn’t have an equivalent. If transformation is part of the extraction job, that’s a separate LLM step you own. --- ## Autonomous research Firecrawl’s `/agent` handles autonomous web discovery — you describe what you want, and it searches across the web to find and extract it, without needing to know URLs upfront. It’s not a `/research` equivalent. Tabstack’s `/research` takes a question, autonomously searches the web, reads multiple sources, synthesizes findings, and returns a cited answer, all in a single API call. No orchestration code. No source selection logic. No citation pipeline to build. Firecrawl’s `/agent` is scoped to extraction: the output is structured data from whatever it finds. Tabstack’s `/research` is scoped to synthesis: the output is a cited answer derived from multiple sources. If autonomous, cited research is a use case in your agent, this is the clearest functional gap between the products. --- ## Site-wide crawling Firecrawl wins here, clearly. Firecrawl’s `/crawl` recursively follows links across entire sites: thousands of pages, sitemap traversal, pagination handling. It’s purpose-built for bulk content ingestion. Tabstack is a per-URL API. There’s no site-wide crawl, no sitemap traversal, no recursive link following. You know the pages you want; Tabstack extracts them. This is a real limitation for bulk RAG pipeline ingestion. --- ## What you’re actually paying for | | Tabstack | Firecrawl | | --------------------- | --------------------------- | ------------------------------------------------------------------------------------------- | | Structured extraction | Included, schema-first | Available via `/scrape` (schema-driven, single pages) or `/agent` for autonomous multi-page | | Pricing model | Credit-based, tiered plans | Credit-based, tiered plans | | AI transformation | Included (`/generate/json`) | Not available - requires a separate LLM call | | Autonomous research | Included (`/research`) | Partial - `/agent` for autonomous extraction; no citation synthesis | | Site-wide crawling | Not available | Core strength | | Pricing transparency | Published tiers | Published tiers | --- ## Feature comparison | Feature | Tabstack | Firecrawl | | ---------------------------------- | ---------------------------- | ------------------------------------------------------------------- | | Schema-driven JSON extraction | Yes — core product, included | Yes — `/scrape`, separate billing | | AI transformation inside call | Yes — `/generate/json` | No — separate LLM call required | | Autonomous research with citations | Yes — `/research` | Partial — `/agent` for autonomous extraction, no citation synthesis | | Site-wide crawling | No — per-URL only | Yes — core strength | | Sitemap and URL discovery | No | Yes — `/map` endpoint | | Browser automation | Yes — `/automate` | Yes — `/interact` | | Clean markdown output | Yes — `/extract/markdown` | Yes — optimized for LLMs | | Self-hostable | No | Yes — AGPL-3.0 (copyleft implications for commercial products) | | robots.txt compliance | Yes — by design | Yes | | TypeScript SDK | Yes | Yes | | Python SDK | Yes | Yes | | LangChain integration | Not official | Yes — native | --- ## Who each is right for **Use Tabstack when:** - You need schema-enforced structured JSON from specific pages - You want extraction, transformation, and research in one product at one price - Your use case is per-URL intelligence — competitive monitoring, pricing extraction, sales research - You need `/research` — multi-source cited answers in a single call - Simpler packaging and fewer moving parts can improve forecasting and maintenance **Use Firecrawl when:** - You need to crawl entire websites — thousands of pages recursively - Bulk markdown ingestion for RAG pipelines or LLM training data is the primary job - You want to self-host under AGPL-3.0 - LangChain or LlamaIndex native integration is required today - Large-scale batch processing across many URLs simultaneously --- ## Honest gaps **Tabstack limitations:** No site-wide crawling. No sitemap traversal. No recursive link following. LangChain and LlamaIndex integrations are not official yet. No self-hosting option. **Firecrawl limitations:** No AI transformation endpoint; fetch-instruct-return-structured-output requires a separate LLM call. No multi-source citation synthesis; `/agent` handles autonomous web discovery and extraction, not cited multi-source research synthesis. AGPL-3.0 self-hosting has copyleft implications for commercial products. --- ## The complementary angle These aren’t always head-to-head. A developer might use Firecrawl’s `/crawl` to ingest an entire documentation site into a vector store, then use Tabstack’s `/research` for live, multi-source research queries that need citations. Different jobs: Firecrawl for bulk historical ingestion, Tabstack for live intelligence retrieval. --- ## Try Tabstack Get an API key at [console.tabstack.ai](https://console.tabstack.ai) and make your first extraction call in under 5 minutes. ``` import Tabstack, { APIError } from '@tabstack/sdk' const client = new Tabstack({ apiKey: process.env.TABSTACK_API_KEY }) try { const result = await client.extract.json({ url: 'https://your-target.com', json_schema: { type: 'object', properties: { title: { type: 'string', description: 'Page title' }, price: { type: 'number', description: 'Price in USD' } } } }) console.log(result) } catch (err) { if (err instanceof APIError) { console.error(`${err.status} ${err.name}: ${err.message}`) } else { throw err } } ``` [Full documentation](https://docs.tabstack.ai)