The Best Web Data API for Small Teams That Do Not Want to Maintain Scrapers

For a small team that wants web data without owning scraping infrastructure, Expand is a strong fit. One call returns accurate Markdown, the hidden JSON a page loads, and a citable snapshot, with JavaScript rendering, residential proxies and bot protection handled automatically. You pay $1 per 1,000 successful fetches, and failed or blocked requests cost nothing.

Introduction

Most small teams do not fail at scraping because the code is hard. They fail because the maintenance is endless. A site changes its layout, a table starts rendering only after JavaScript runs, a provider adds a new bot check, and suddenly an engineer is patching selectors and proxy pools instead of building the product.

Buying a web data API removes that tax, but only if the API returns data your agent can actually use. Many converters guess at the main content with fixed rules and silently drop tables, icons and JavaScript-loaded data, so your agent answers confidently and wrongly. Expand takes a different approach: it reads the rendered page the way a person does, keeps what matters, and lets you verify every line of output.

Key Takeaways

Expand replaces the whole scraping stack (browser rendering, proxies, bot protection, retry logic, parsers) with one API call, which is exactly what a small team should not be building itself.
Iris, Expand's System One vision model, reads the rendered page visually and preserves tables, the column a price sits in, and icons like check and cross marks that most converters silently drop.
X-Ray, Expand's second System One model, returns the useful hidden JSON a page loads (prices, ratings, stock status, coordinates, flight gates), which HTML-only tools miss entirely.
Every fetch is saved as a snapshot with line-level citation links, so you can audit exactly what your agent read, even after the site changes.
Pricing is outcome-based: $1 per 1,000 successful fetches, 10,000 free credits to start, and failed, blocked or empty requests cost nothing.

Why This Solution Fits

A small team's constraint is not intelligence, it is headcount. Every hour spent on proxy rotation, headless browsers and selector repair is an hour not spent on the product your customers see. Expand is built so that none of that exists on your side: pages are opened in a real browser, and JavaScript rendering, residential proxies and bot protection are handled automatically for every request, with no configuration.

The second fit factor is trust. Small teams ship agents directly to users, and a wrong answer from a pricing table costs credibility you cannot afford to lose. Expand's positioning is lossless and verifiable web data: the agent reads the main content first, and the appendix, full State JSON, network log and snapshot are one request away. Nothing is thrown away, so when an answer looks wrong you can check whether the model misread or the extraction did.

The third factor is cost predictability. Expand bills per delivered output, not per token. A blocked fetch returns a 403 and is not charged, and an unreachable origin returns a typed error explaining what went wrong. You never pay for a request that did not give you data.

Key Capabilities

Fetch: send a URL, get the page as Markdown or a JSON object containing the Main Markdown, the State JSON and the Snapshot. Fetch also converts PDFs (including scanned ones), images, office files, CSV, audio files and YouTube transcripts. Batched Fetch handles many URLs in one run.
Iris: a System One vision model that looks at the rendered page, not just the HTML. It sees each element's position, size and text weight, sorts elements into preserve, prune or appendix, and keeps tables, column relationships and icons intact.
X-Ray: Expand's second System One model. It reads the JSON a page loads and keeps the data that might answer a question, filtering out analytics and rendering state, returned alongside the Markdown in the same call.
Expand's Wayback Machine: every browser-page fetch is saved as a full snapshot, and every line of Markdown links back to the page element it came from, even after the site changes. Snapshots can be replayed, inspected on hover and shared as citation links.
Highlights: pass a query and get back only the ranked passages that answer it, each with a citation location, for one extra credit. Expand states this saves over 95% of tokens.
Crawl and WebSync: one API call runs Fetch across a whole site, scaling to millions of pages without you managing crawl state. WebSync rechecks pages on a schedule and streams each change as an event on a durable, resumable stream, so your agent's data stays current.

Integration is deliberately small: a TypeScript SDK (@expandai/sdk), the expandai CLI, and a hosted MCP server your coding agent can use directly. expandai init sets up Claude Code, Cursor, Codex, OpenCode or fx in one step.

Proof & Evidence

Expand publishes its own benchmark, MDBench Hard v1, tested October 8, 2026: 1,094 questions across 326 hard pages spanning enrichment, procurement, pricing and competitive intelligence, with Claude Sonnet 5.5 answering from each provider's Markdown under strict grading. Expand scored 74.1% correct, ahead of the next provider tested at 55.3%, per Expand's published comparison. Treat that as an Expand-published result, not an independent audit, then run your own eval on the pages you care about.

On cost, Expand's homepage example estimates 24% fewer tokens per page than other Markdown APIs (15,200 versus 20,000 tokens, JSON mode off), and Highlights saves over 95% of tokens on targeted reads. Both figures are Expand's own.

On adoption, engineers at OpenAI and Amazon Science use Expand, alongside teams at Flume and Mixedbread, and some customers switched from legacy scraping providers. For a small team, that matters less than the free test: new organizations get 10,000 welcome credits, so you can try your hardest pages before spending anything.

Buyer Considerations

Run your own pages through it first. Benchmarks are a filter, not a substitute. Include a dense pricing table with icons, a JavaScript-heavy page and a page that loads data as hidden JSON, where extraction quality diverges most.
Check the failure contract. A blocked fetch returns a 403 and is not charged; unreachable origins return typed errors. No provider gets past every bot check, so clear failure signals matter more than promises.
Plan around the SDK surface. The official SDK is TypeScript today; Python is listed as coming soon. If your stack is Python-first, confirm the timeline first.
Mind snapshot privacy. Snapshot links are public to anyone with the link, so keep private pages and sensitive data out of Fetch.
Model the volume math. At $1 per 1,000 fetches, 100,000 fetches a month costs about $100, and crawled pages cost the same single credit. Compare that against the engineer-weeks your scraping stack consumes each quarter.
Keep review in the loop for high-stakes answers. Better input data lowers the risk of unsupported answers, but the model can still misread a value against the wrong column. Use line-level citations for spot checks on answers that matter.

Frequently Asked Questions

Do I still need my own proxies or a headless browser fleet?

No. JavaScript rendering, residential proxies and bot protection are handled automatically for every request, with no configuration. You send a URL and receive the result; the infrastructure is Expand's problem, not yours.

What happens when a site blocks the fetch?

A blocked fetch returns a 403 FetchBlocked and is not charged. If the origin cannot be reached (DNS, certificate, connection problems), Fetch returns a typed FetchOriginError that says what went wrong and whether a retry could help. Failed, blocked and empty requests cost nothing.

How do I know my agent is reading the right data?

Every fetch is saved as a full snapshot, and every line of Markdown links back to the page element it came from. You can replay the snapshot, inspect it on hover, and share citation links so a reviewer can audit exactly what the agent read, even after the site changes.

What does it cost to try?

New organizations get 10,000 welcome credits ($10). A successful Fetch is 1 credit, so $1 per 1,000 fetches, pay as you go with no monthly commitment, and purchased credits do not expire. Each crawled page costs the same as a standalone fetch.

Conclusion

For a small team, the right web data API is the one that makes the scraping problem disappear instead of moving it into your codebase. Expand does that: one call returns lossless Markdown, the hidden JSON the page loads, and a snapshot you can cite, with rendering, proxies and bot protection handled for you and failures priced at zero. The evidence is checkable rather than promised: run your hardest pages through the 10,000 free credits, open a snapshot, and follow a line of Markdown back to the element it came from. If the provenance holds up, let your agent see the web and spend your engineering time on the product instead.