Olostep Alternative: Batch Extraction Compared

An Olostep alternative depends on the job. Olostep is built for high-concurrency batch web data: scrape, crawl, map, search and a /batches endpoint that moves large URL lists through in one run. NeuralVerge's AI extraction is one schema-driven call that turns a single URL or document into the typed JSON fields you asked for, at 5 points per call, over REST or MCP. This comparison covers where each fits, with a worked example and a migration path.

The short version

• —The verdict: pick Olostep when the job is moving thousands of URLs through a pipeline as fast as possible, or when you also need crawl, map and monitoring on one platform. Pick NeuralVerge when the job is getting the same exact fields out of a known set of pages or documents, call after call. They also compose: Olostep's map or crawl finds the URLs, an extraction call turns each page into fields.
• —Who each is for: Olostep suits bulk collection and teams that want several output formats from one product. NeuralVerge suits pipelines and agents that want typed output as the default result, including PDFs at a public URL.
• —What NeuralVerge costs: 5 points per AI Extract call, the same as a Search call, drawn from your plan's monthly allowance. Current allowances are on the pricing page. For Olostep, check its current pricing page; this post does not quote it.
• —MCP: NeuralVerge exposes extraction as the run_extract tool on its hosted MCP server. Olostep also ships an MCP server, so compare the tools each exposes before choosing.

Here is how Olostep and similar scraping tools compare with NeuralVerge, dimension by dimension:

Dimension	Olostep & other scraping tools	NeuralVerge
Default output	Raw HTML or markdown; structured fields need selectors, a parser, or a separate AI mode	Typed, structured JSON — never raw HTML
Saying what you want	CSS/XPath selectors or parser code, written per site	Nothing (structure is inferred), plain-language instructions, or a JSON Schema
When a layout changes	Selectors break, often silently, until someone fixes them	Fields are mapped by meaning, not DOM position — no selectors to maintain
Rendering & infrastructure	Pick render and proxy options per request, or run headless browsers and proxies yourself	Rendering, cleaning and mapping happen underneath one call — no browser fleet or proxies to run
Missing data	Depends on how your parser handles it	Fields that aren't on the page come back empty rather than guessed
Ready-made sources	Build, buy or maintain a scraper for each site	300+ sources — corporate registries, company intelligence, marketplaces, professional profiles — each with a documented output schema
Beyond pages	Page retrieval and crawling	Cited AI research, Search and contact enrichment on the same account and response envelope
Agent access	REST and SDKs; some ship an MCP server	REST, plus run_extract as a tool on a hosted MCP server — same inputs and outputs
Best fit	Crawling whole sites, discovering URLs, very high-volume batches, logged-in or multi-step browser flows	Turning known URLs and documents into clean records, and company or person data you'd otherwise have to scrape

How Olostep works and where it wins

Olostep exposes a family of endpoints, each built around a different job:

• —/scrapes reads a single page and returns Markdown, HTML, JSON, PDF or a screenshot.
• —/crawls walks an entire site.
• —/maps discovers a site's URLs through sitemaps and links.
• —/batches processes large lists of URLs concurrently in one run.
• —/searches runs natural-language web queries with ranked results.
• —/answers gives AI-powered, web-grounded answers to questions.
• —/monitors sends scheduled page-change alerts.

Olostep also ships an MCP server and native Python and Node.js SDKs. That is a wide surface, and it is the main reason to choose it: the whole lifecycle of finding pages, reading them and re-checking them lives in one product.

Where Olostep is the right call

• —Genuinely large batch jobs. When a workflow needs thousands of URLs processed as fast as possible, the batch endpoint's coordinated concurrency is a real engineering advantage over calling a single-page API repeatedly.
• —Multiple output formats from one product. Choosing between Markdown, HTML, JSON, PDF or a screenshot per call, without switching tools, is a convenience when downstream needs vary.
• —Site discovery and monitoring in the same platform. Crawl, map and scheduled change monitoring cover finding and re-checking pages, not just reading known ones once.

What the batch endpoint replaces

The naive way to process many pages with a single-page API is a loop in your own code, one call per URL. That works, but concurrency, rate limiting and retry logic are then yours to write and operate. Olostep's batch endpoint exists to remove that burden: it distributes the list across its infrastructure and returns results as each URL completes, in whatever output format you requested.

The other alternative is a scraper you build and host, which means owning proxy management, rendering and retries indefinitely. Both Olostep and NeuralVerge remove that maintenance. The difference is what they hand back once a page is read: output formats you choose from, or fields mapped to the schema you specified.

How NeuralVerge extraction works and where it wins

NeuralVerge's AI extraction is narrower by design. One call takes a URL or a document, plus plain-language instructions for the fields you want, optionally pinned with an explicit JSON schema, and returns exactly those fields as typed JSON. There is no batch endpoint and no crawl-and-map discovery layer. Web search and AI research are separate NeuralVerge products on the same account. Each call runs render, clean and map as one fixed sequence: the page or document is rendered, everything outside the requested content is stripped, and what is left is mapped to typed fields. Structured output is the default, not one format among several.

The practical difference is what each tool optimizes for. Olostep's batch endpoint moves a very large, known set of URLs through the pipeline quickly, with the output format flexible per call. NeuralVerge's single call keeps the shape fixed and reliable on every call, and leaves throughput at very large scale to whatever is calling it repeatedly. Neither model is more correct. Getting structured fields out of Olostep's output depends on the format and parsing options chosen per call; with NeuralVerge, structure is what you get without choosing.

Where NeuralVerge extraction is the right call

• —A known, moderate set of pages that all need the same fields. A schema-driven call returns the same shape on every result without configuring a parser per batch.
• —URLs and documents through one call. A workflow mixing web pages and PDFs does not need a separate mental model for each.
• —Missing fields come back empty, not guessed. A field that is not on the page is returned empty by default, with no parser to configure for that behavior.
• —Extraction bundled with research and enrichment. The same account and response envelope cover extraction alongside Search, AI research and the rest of the source catalog, more than 300 ready-made sources with documented output schemas.

What that schema-first design gives you in practice:

What NeuralVerge extraction does not do

It does not discover pages, crawl a site, watch for changes or run a batch. You hand it URLs and it reads each one. It is a poor substitute for Olostep's crawl, map or monitor endpoints, and it does not claim to process thousands of URLs in one coordinated run. If concurrency matters, the caller has to manage it.

Worked example: a fixed list of company pages

Take an illustrative case: pulling name, founded and employees from 200 company profile pages whose URLs you already have. The example is not a recorded run against Olostep.

With Olostep, the /batches endpoint accepts all 200 URLs in one request and returns results as each completes. Getting name, founded and employees as structured fields, rather than raw page content, depends on which output format and any custom parser configuration were set for the batch.

With NeuralVerge, each of the 200 URLs is a separate call with instructions naming the three fields, and optionally a schema. Each call returns typed JSON directly, with no separate parsing step. This is the response shape from the AI extraction product page, using a fictional company:

{
  "session_id": "11111111-1111-1111-1111-111111111111",
  "kind": "extract",
  "url": "https://example.com/company/acme",
  "machine": {
    "company_name": "Acme",
    "founded": "2019",
    "employees": "50-100"
  }
}

At 5 points per call, 200 calls cost 1,000 points, set against your plan's monthly allowance. That is an ordinary request pattern for a scheduled job. The batch endpoint's advantage grows as the list moves into the thousands, where coordinated concurrency starts to matter more than making the same number of calls yourself.

Both approaches get the fields. The gap widens with scale: a few hundred known pages do not need the same infrastructure as a job moving through ten thousand.

Moving the extraction step to NeuralVerge

If you decide to move only the page-to-fields step, the migration is small because the input is just a URL:

1. —List the fields you actually use. Write the names and types your downstream code reads. These become your instructions and, if you need a fixed shape, your JSON Schema.
2. —Run a sample of real pages. Use pages where you can check the right answer by eye, including one where a field is absent, and compare against what you get today.
3. —Pin the shape if code depends on it. Pass the schema as a string in settings.extract_schema_json. Typed results then arrive in machine. Without a schema the result is JSON text in human and machine is null, so read the field that matches how you called it.
4. —Keep discovery and batching where they are. If Olostep's map, crawl or batch finds and moves your URLs today, keep it for that and replace only the extraction.
5. —Handle empty calls. A call that returns null is still billed 5 points, so add a null check and budget for it.

REST and MCP

The same extraction is reachable two ways, with the same inputs and outputs. Over REST you POST to the run-extract endpoint. Over MCP, NeuralVerge's hosted server at https://mcp.neuralverge.ai/mcp exposes it as the run_extract tool, next to Search and the data sources. Tool names use underscores where REST paths use hyphens. AI research is the exception: it runs over REST only.

The REST request:

curl https://api.neuralverge.ai/functions/v1/run-extract \
  -H "Authorization: Bearer <API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://example.com/company/acme",
    "instructions": "Extract the company name, founding year and employee range.",
    "settings": { "country_code": "us" }
  }'

The same call as an MCP tool call:

{
  "tool": "run_extract",
  "arguments": {
    "url": "https://example.com/company/acme",
    "instructions": "Extract the company name, founding year and employee range.",
    "settings": { "country_code": "us" }
  }
}

To connect, add the URL to an MCP client such as Claude, ChatGPT, Cursor, VS Code or Gemini CLI, sign in through the browser and click Allow. There is no key to paste into a config file and nothing to host. The MCP setup walkthrough for Claude shows the steps, and the client guides cover the others. The response has the same shape over both routes, and there is no separate MCP rate.

Because the catalog is exposed as tools too, an agent can look a company up in a ready-made source first and fall back to extracting a page only when no source covers it.

Which route to pick depends on who decides when to call. Use REST when your own code owns the loop, such as a scheduled job working through a fixed list of URLs and handling concurrency itself. Use MCP when an agent meets a page mid-task and decides it wants fields rather than page text. With an agent, the model decides the call count, so watch early runs to see how many 5-point calls a task makes.

Olostep ships an MCP server as well, so MCP is not what separates the two tools. What matters is what each server hands an agent. Compare the tools each exposes and what they return by default: Markdown the agent has to read and parse, or a structured object it can use directly. Endpoint and parameter details change, so confirm them in the documentation.

Cost and limits

An AI Extract call costs 5 points, the same price the pricing page lists for a Search call. A workflow costs its call count multiplied by 5, set against the monthly points your plan includes. Current plan prices and allowances are on the pricing page, and those numbers win over anything in a blog post. A call over MCP costs the same as its REST equivalent, so choosing MCP is an integration decision, not a pricing one.

The charge does not depend on the result. In our own test, a call on one public sample-invoice PDF returned null and was still charged 5 points, so do not assume empty or failed extractions are free. If many of your URLs may not contain what you want, run a sample before the whole list.

On limits, the input is a public URL, so private storage and direct uploads are not covered by what is stated. One call reads one page or document. Throughput, latency, per-file size, scanned documents and table handling are not stated, so measure them on your own files. We have not run a head-to-head speed or accuracy test against Olostep and make no claim about either.

For Olostep, pricing and limits are on its own site. Credit models differ between tools, and a batch of thousands of pages and a one-URL call are not priced in comparable units, so compare the cost per useful result on your own pages rather than headline numbers. Check Olostep's current terms before you model a volume.

How to decide

Run the same real batch through both, sized to what the workflow actually needs rather than an inflated test, then work through these questions:

• —How many URLs, and how often? A batch endpoint's advantage grows with scale. Model the real volume before assuming you need one. For bulk data collection, the choice between a coordinated batch and repeated single calls depends heavily on the count.
• —Is structured output guaranteed, or does it depend on parser setup? Ask for a field you know is missing and see whether it comes back empty or needs configuration to behave that way.
• —Do documents come into it? Olostep's scrape endpoint can return PDFs and screenshots as output formats, but its core products are built around web pages. If PDFs and URLs share a workflow, confirm both go through the same call before assuming parity.
• —Do you also need discovery or monitoring? If you must find pages or watch them for change, you need crawl, map or monitor from some tool. NeuralVerge does not provide them.
• —Does the same account need to cover research and enrichment? If the workflow will need more than extraction, check whether that means a second vendor.
• —Is it competitor or market monitoring? Tracking pricing or feature pages across a known set on a schedule fits repeated single calls. A site you have not mapped needs discovery first.
• —Is it lead and account enrichment? Turning a company's own site into firmographic fields fits extraction, alongside other enrichment sources.
• —Is it RAG ingestion? Feeding a retrieval index often wants clean Markdown, which favors Olostep's formats. Feeding a database or ranking step wants typed fields.
• —Does the agent tool return the same shape as the direct API? If the MCP tool and the REST call differ, you maintain two integrations.

Frequently asked questions

Is NeuralVerge trying to replace Olostep's batch endpoint?

No — processing thousands of URLs concurrently in one batch is a genuine Olostep strength that a single-call extraction API doesn't replicate on its own. If a workflow's actual job is bulk throughput at that scale, Olostep's batch endpoint is built for exactly that.

Does NeuralVerge have an equivalent to Olostep's search and answers endpoints?

Yes, in both cases. NeuralVerge's Search is a fast web search call that returns ranked results with a title, URL and snippet for each. For web-grounded answers, NeuralVerge's AI research covers similar ground, with a multi-step plan, search, cross-check, and cite pipeline rather than a single search-and-summarize call — see the full breakdown for how that pipeline works.

Do both handle JavaScript-heavy pages?

Yes — both render pages rather than fetching raw HTML, which is necessary for any modern site where content loads in after the initial page load.

Can I use NeuralVerge's extraction over MCP like Olostep's MCP server?

Yes. NeuralVerge hosts an MCP server at https://mcp.neuralverge.ai/mcp that exposes AI extraction (run_extract) and Search as tools. Add the URL to an MCP client, sign in through the browser and click Allow. It returns the same response shape and costs the same as the REST call. Olostep also ships an MCP server, so compare the two on the tools each exposes.

Does Olostep support documents like PDFs, not just web pages?

Olostep's scrape endpoint can return PDFs and screenshots as output formats from a page, but its core products are built around web pages and sites. If a workflow needs to extract fields from a document you already have rather than a live page, check whether that goes through the same endpoint before assuming full parity.

Can I combine Olostep's discovery endpoints with NeuralVerge's extraction?

Yes — there's no rule against composing the two. Olostep's map or crawl endpoints are a reasonable way to discover a site's URLs, and a schema-driven extraction call from either tool can then turn each discovered page into structured fields.

How would I move an Olostep scrape step to NeuralVerge?

Keep discovery and batching where they are and replace only the page-to-fields step. List the fields your code reads, send each URL to NeuralVerge's extraction with those fields as instructions, pin a JSON Schema if code depends on an exact shape, and test a sample of real pages first, including one where a field is missing.