CatchAll Review (2026): Pricing, Features & Honest Verdict

TLDR

CatchAll is a recall-first web search API from NewsCatcher. Instead of returning the top 10 links, it scans 50,000+ pages per job, clusters them into real-world events, LLM-validates each against your criteria, and returns deduplicated structured records. It just launched, and credit pricing is tricky. Best for: teams building event datasets from the web. Price: Free plan: yes (2,000 credits), paid from $50/mo. Rating: 6.2/10.

What is CatchAll?

CatchAll is a web search API built for a specific job: turning scattered web signals into complete, structured datasets. Most search APIs optimize for precision, give me the best handful of results. CatchAll flips that and optimizes for recall: find as many relevant pages as possible, then validate and structure them. For a single query it scans tens of thousands of pages, NewsCatcher cites 50,000+ per job, groups the pages that describe the same thing into one real-world event using community detection, runs each candidate through an LLM that checks it against your stated criteria, and returns deduplicated records with entities, amounts, and dates already extracted.

It is built by NewsCatcher, a Y Combinator company whose original product was a news API, so indexing and structuring the web is their core competence. CatchAll generalizes that into a real-world-events search engine and launched publicly on Product Hunt in May 2026. We dug into it because the AI search API space is crowded with snippet-style tools, and CatchAll is taking the opposite bet: that the real pain for research and intelligence teams is not relevance ranking but coverage, the events you never saw because the top 10 results missed them. NewsCatcher reports an 86% recall rate and claims it finds roughly 3x more relevant events than tools like Exa Websets and Parallel AI FindAll, though those figures are vendor-reported.

What Are CatchAll’s Key Features?

Recall-first scanning

This is the whole pitch. Rather than returning a ranked shortlist, CatchAll casts a wide net, scanning a reported 50,000+ pages per job to surface relevant material that precision-tuned APIs skip. If you are trying to build a dataset of, say, every facility opening or every regulatory action in a sector, missing entries is the failure mode that matters, and recall is the metric that fixes it. The honest caveat: the 86% recall figure comes from NewsCatcher’s own Q1 2026 benchmark across 32 event-detection queries, so treat it as a directional claim, not an independent result.

Event grouping and structured records

CatchAll does not just hand you pages. It uses community detection to cluster the many pages that report the same real-world event into a single record, then returns that record with entities, amounts, and dates already parsed out. The output is deduplicated, so ten articles about one acquisition collapse into one row, not ten. For anyone who has built a scraping pipeline and then spent weeks deduplicating and entity-extracting, having that done at the API layer is the feature that saves the most time.

LLM validation against your criteria

Between scanning and returning, CatchAll runs candidates through an LLM that validates each one against the criteria you define, so you can filter to exactly the kind of event you care about rather than everything that mentions a keyword. This is what separates it from a raw crawler: it is recall first, then precision via validation. As with any LLM-in-the-loop step, the quality depends on how well you express your criteria, so expect some iteration to dial in the filter.

Scheduled monitors and webhooks

Beyond one-off searches, CatchAll runs scheduled monitors that re-check for new matching events and push them to you via webhooks, which turns it from a search tool into ongoing event tracking. Monitor slots and frequency scale with your plan: one daily monitor on the free Individual tier, up to five on Starter, and up to ten running every six hours on Scale. That makes it usable for keeping a live dataset current, not just building one once.

How Much Does CatchAll Cost?

CatchAll uses credit-based pricing, and the starting point is generous: 2,000 free credits at signup with no subscription, and NewsCatcher says it will 5x those credits if you share your use case. Paid plans are Starter at $50/mo for 6,000 monthly credits and Scale at $500/mo for 60,000 monthly credits, with a pay-as-you-go Individual tier and custom Enterprise pricing above that. Unused monthly credits roll over, which is a fair touch. The two consumption modes matter: in Base mode, one valid record costs 10 credits; in Lite mode, an entire search costs a flat 100 credits.

The catch is predictability. Because Base mode bills per valid record returned, a broad query that surfaces hundreds of events can burn credits far faster than a narrow one, and you do not always know the record count in advance. Lite mode’s flat 100 credits per search is easier to forecast but less granular. Plans also gate more than credits: max results per search (100 on Individual, up to 1,000 on Scale), monitor count, and data depth, which runs from two weeks on the free tier to three months on Scale, with full archive and backfill reserved for Enterprise. If you need deep historical coverage, you are pushed toward a custom plan.

Against the alternatives, CatchAll is not the cheapest per query, and it is not trying to be. Exa charges around $7 per 1,000 search requests with a 1,000-request free tier. Tavily has a free researcher tier and project plans from roughly $30/mo, billing fractions of a cent per credit. Both are cheaper for simple snippet retrieval. CatchAll’s premium is for the structured, deduplicated, validated event records, work those tools leave to you. If you only need links, the others win on price. If you need the dataset built and cleaned, CatchAll’s higher cost can be the better deal.

Who is CatchAll Best For?

Use CatchAll if you are a data, research, or intelligence team whose job is building comprehensive datasets of real-world events from across the open web. That includes finance and banking, compliance and risk, supply chain, insurance, and similar fields where missing an event is the expensive mistake and where you need structured records, entities, amounts, dates, not just a page of links. The scheduled monitors make it a strong fit when you need to keep that dataset continuously updated rather than pulling it once.

Skip CatchAll if your need is ordinary search: grabbing the top few relevant pages for a RAG pipeline or a quick lookup. A snippet-style API like Exa or Tavily will be cheaper and simpler for that, and you will not pay the recall premium you do not need. Be cautious too if you require predictable monthly costs, because the per-record credit model can swing with query breadth, or if you need deep historical archives, which are gated to Enterprise. And weigh that this is a fresh launch with mostly vendor-reported benchmarks.

Best CatchAll Alternatives

Exa

Exa is a neural and semantic search API popular for AI applications, with a 1,000-request free tier and Search priced at about $7 per 1,000 requests, plus small add-on fees for full-text content. It is fast, well-documented, and cheaper than CatchAll for straightforward retrieval. The difference is philosophy: Exa optimizes for finding the most relevant results, while CatchAll optimizes for finding all of them and returning structured event records. Pick Exa when you want relevant pages for RAG or research; pick CatchAll when coverage and structured output are the point.

Tavily

Tavily is a search API built specifically for AI agents, with a generous free researcher tier, project plans starting around $30/mo, and full MCP support for clean agent integration. It bills per credit at fractions of a cent and is designed to drop into agent workflows with minimal friction. Tavily is the better choice if you are wiring search into an agent and want simple, predictable per-query costs. CatchAll is the choice when the deliverable is a deduplicated dataset of events, not live answers inside an agent loop.

Bright Data

Bright Data is the enterprise heavyweight for web data, with large-scale scraping, anti-bot bypass, real-time SERP access, and historical archives, plus MCP support for agent workflows. It is the pick when you need raw, high-volume extraction across difficult sites and have the budget and engineering to handle it. Bright Data gives you maximum control and coverage but leaves structuring, deduplication, and event grouping to you, which is exactly the part CatchAll automates. Choose Bright Data for raw scale and control; choose CatchAll when you want events out of the box.

Final Verdict: Is CatchAll Worth It?

CatchAll is one of the more differentiated web search APIs to launch in 2026 because it solves a problem the popular tools mostly ignore: recall. For teams whose job is to never miss a relevant event, scanning tens of thousands of pages, clustering them into deduplicated records, and validating each against your criteria with an LLM is genuinely valuable, and the structured output with entities, amounts, and dates removes a stack of post-processing. NewsCatcher’s news-indexing pedigree makes the underlying capability credible.

The 6.2 is about maturity and cost, not the idea. This is a brand-new launch leaning on vendor-reported benchmarks like 86% recall, so the track record is thin. The credit model, 10 credits per valid record or 100 per search, is hard to forecast when query breadth drives cost, and $500/mo for Scale plus historical depth gated behind Enterprise can get expensive. The smart move is to spend the 2,000 free credits on your real queries, measure both the recall and the credit burn against your own needs, and only then decide whether CatchAll’s coverage is worth a premium over Exa or Tavily.