Comparison

Riveter vs Exa: searching an index, or going and getting the answer

Abby Grills· CEO, RiveterPublished · Updated

Every competitor claim on this page links to that vendor’s own documentation or pricing page, verified Aug 18, 2026 — check any of it directly.

Exa built one of the best semantic search indexes on the web. If your question is “what’s out there about this,” Exa will find it fast, and its published benchmarks against other search providers back that up. The question this page answers is different: what happens when you run it over 5,000 rows and need an answer in every one.

The one-sentence version: Exa searches a pre-built index and returns the pages it holds; Riveter searches the live open web to find where the answer actually lives, navigates to it, and returns a finished, structured result — for a full list or a single record. Riveter fills rows Exa leaves blank because it is better at finding them, not only at formatting what it finds.

This page is for GTM, rev-ops, and data teams comparing the two for enrichment and dataset work, and for engineering teams who’ve built on Exa’s Search API and are hitting the ceiling on coverage or on multi-step logic.

TL;DR verdict

Choose Exa when the job is search: finding relevant pages semantically, exploring a topic, feeding a RAG pipeline, or powering a research agent that needs fast, token-efficient retrieval. It is very good at that and priced accordingly.

Choose Riveter when the job is finding and returning an answer for every record. An index can only surface what it has already crawled and stored; when the data you need sits on a page that wasn’t indexed, behind navigation, or inside a PDF, ranking quality is irrelevant because the page isn’t in the candidate set. Riveter searches the live web, follows the trail to the source, and extracts the structured answer in the same workflow. That single architectural difference is what fill rate, completeness, and freshness all reduce to.

Dimension Riveter Exa
Fill rate / coverage Goes live to the source per row, so it returns a value where the answer exists anywhere on the open web Returns what the index holds; rows whose answer isn’t indexed come back empty
Freshness Scrapes the live web at the moment of the request, so a value is as current as the source itself. Monitoring additionally re-runs workflows on a schedule to keep results current over time Index-served. Exa’s docs deprecate livecrawl and note it “does not guarantee freshly fetched parser output and may be served according to server freshness policy”
Completeness (“all N of X”) Built for enumeration — every attorney on a firm’s site, every state a manufacturer operates in Exa’s docs cap public search at 100 results per request; higher limits require sales
Discovery (no starting URL) Searches the live web to find where the answer lives — including sources never crawled — then navigates and extracts it Strong semantic retrieval of pages inside its index; anything outside it can’t be surfaced
Agentic search and extraction The core product — every Enrichment, Extraction, and Dataset build runs on it in production today An agent layer sits on top of an index-retrieval core; the index is what Exa leads with
Recurring workflows Define the task and output schema once, then rerun across changing inputs or on a schedule A search primitive; the recurring workflow around it is yours to build and maintain
Bulk and per-record Same workflow runs over a full list or one record at a time through the API as requests arrive Per-call by design; batching, queueing, and per-record orchestration are yours to build
Output consistency across changing inputs Core design goal — same schema every run, built to load straight into a downstream system Result shape varies with what the index returns; normalizing it is your code
Multi-step workflows Native — columns feed each other, so step one’s answer determines what step two does One search call per request; you orchestrate the chain in your own code
Hard pages, PDFs, images Navigates complex pages that normally need full browser automation, simplified to run fast and cheap at scale; reads large files and PDFs Contents returns indexed page text; JavaScript-heavy pages and very large documents are where index extraction thins out
Self-healing / maintenance Extractions regenerate automatically when a source site changes Not applicable — you’re querying an index, but the pipeline you build around it is yours to maintain
What you get A finished, structured, verified result — a full dataset, or a single enriched record returned live Ranked results, page contents, and highlights — excellent retrieval input
Who it’s for GTM, rev-ops, and data teams, plus engineers embedding web data in a product Developers and AI agent builders
Total cost of ownership Priced per action (credits), with the orchestration and maintenance included Low per-search price; the blank cells and the pipeline around them are the real cost

What is Exa?

Exa is an AI search API built on its own web index. Its own framing is “The world’s data for AI agents — search the largest index of public web and private information.” It reports tracking 1.4 trillion URLs across an index of roughly 80 billion documents, and its surfaces are Search (semantic and keyword retrieval), Contents (token-efficient page text and highlights), Agent (structured outputs and deep research), Monitors (continuous tracking), and Websets for list building.

Exa is credible and well-adopted. It publishes benchmark results against other search providers — 54.4% on FRAMES and 54.2% on Tip-of-Tongue, against lower scores for Perplexity and Brave — advertises sub-180ms latency on its Instant tier, and reports roughly 90% token reduction through its highlights feature. Customers include Cursor, AWS, Databricks, Groq, HubSpot, and Cognition. On the job of retrieving relevant content quickly and cheaply, it is one of the best options available.

Exa is a search engine for agents. That’s the design, it’s stated plainly, and most of what follows is about what happens when a team needs something other than search.

What to look for in a web data tool

Before comparing any two vendors, fix the questions. These are the ones that predict whether a web-data project survives contact with production.

  1. Fill rate / coverage. When you run 5,000 rows, how many come back with a real answer instead of a blank cell? This is the single most under-asked question in the category.
  2. Freshness. Is the data re-checked on a schedule, or is it accurate as of whenever it was last crawled? For pricing, hiring signals, and filings, stale is the same as wrong.
  3. Completeness. When you ask for “all N of X,” do you get the complete set, or a capped, plausible-looking subset?
  4. Discovery. Can you describe the data you want without knowing which site holds it?
  5. Maturity of the agentic layer. Is autonomous search and extraction the core of the product, or a layer on top of a retrieval index?
  6. Recurring workflows. Can you define the task and schema once and rerun it across changing inputs, or do you rebuild the orchestration each time?
  7. Bulk and per-record. Can the same workflow run over a full list and handle a single record arriving live?
  8. Output consistency. Does the same workflow return the same structure every run, ready to load downstream?
  9. Multi-step logic. Can step two depend on step one’s answer inside the tool, or do you build and maintain that chain yourself?
  10. Hard pages, PDFs, and images. JavaScript-heavy sites, very large pages, and long PDF documents are where index extraction stops.
  11. Maintenance. When a source site redesigns, does the extraction repair itself, or does an engineer?
  12. What you actually get. Ranked pages you still have to process, or a structured result ready to load?
  13. Who can operate it. Only an engineer, or the rev-ops or data analyst who needs the answer?
  14. Total cost of ownership. Per-call price plus the engineering time to build, orchestrate, and maintain the pipeline — and the cost of the records that came back empty.

Exa is strong on 4 and 12 for retrieval, and on speed. Riveter is built for 1, 2, 3, 5, 6, 7, 8, 9, 11, and 13.

Three ways Riveter operationalizes web data

Much of the difference below comes from this: Riveter isn’t a single call, it’s three workflow shapes that share one property — define the task and output schema once, then run it repeatedly as inputs change.

Enrichments. Start with rows or entities you already have and apply the same research workflow and output schema to each one. Give Riveter 5,000 company domains, define the fields once, and every row runs the same workflow and returns the same structure.

Extractions. Start with known sources and repeatedly convert them into a consistent structured schema. Extractions are self-healing: when a source site changes and breaks one, Riveter regenerates it automatically.

Dataset builds. Start with a task rather than a list of sources. Riveter discovers the relevant entities and sources, enumerates them, extracts the requested fields, and assembles the finished dataset.

All three run two ways: bulk, or one record at a time

A Riveter workflow isn’t only a batch job. Once an enrichment is defined you can run it over a full list or call the API with new entries as they arrive — each one flows through the same step-by-step workflow live and comes back finished. That’s what makes Riveter something you build into a product: enrich a lead the moment it signs up, resolve a record inside a live flow, or serve an agent’s tool call.

A search API is a component inside step one of any of these. The other steps — and the guarantee that the output looks the same next run — are the product.

Head to head on the scorecard

Fill rate — the row that decides most evaluations

An index can only return what it has already crawled, parsed, and stored. That is a structural property, not a quality problem, and it’s why coverage outside the well-indexed core tends to be inconsistent: for a well-known public company you’ll get a clean answer, and for a 40-person private manufacturer you’ll often get nothing at all. In an enrichment run, “nothing at all” is a blank cell, and blank cells are the thing that kills the project — not because the tool was wrong, but because someone now has to fill 1,800 rows by hand.

Riveter’s agents go live at request time. If the answer exists on the open web — on a careers page, in a state registry, inside a PDF — Riveter navigates to it and returns a value. That is the whole of the fill-rate difference, and it’s the reason teams who tried an index-based tool first tend to arrive with a spreadsheet full of gaps.

How to test it yourself: take 200 rows from the middle of your list — not the famous logos at the top — and run the same enrichment on both. Compare the count of populated cells before you compare anything else.

Agentic search and extraction: the core, or a layer on top

Exa has an Agent API, so it would be wrong to say Exa doesn’t do agentic work. The difference is what sits at the center of each product. Exa leads with its index — its own homepage headline is about searching “the largest index of public web and private information” — and the agent layer queries that index. For Riveter, agentic search, navigation, and extraction is the product: every Enrichment, Extraction, and Dataset build runs on it, in production, today, and the accumulated work of reaching hard-to-get data reliably is the core rather than something built over a retrieval engine.

That distinction is why the two behave differently on hard records. An agent over an index is still bounded by the index. An agent that navigates live is bounded by what exists on the open web.

Freshness

Riveter’s agents scrape the live web at the moment of the request. There’s no index layer between the question and the source, so whatever comes back is as current as the page itself was when you asked — freshness isn’t a setting, it’s a consequence of how the retrieval works. On top of that, monitoring re-runs whole workflows on a schedule to keep results current over time: monitored extractions as often as every minute, enrichment monitors as often as every 15 minutes, with prompt-defined logic for what counts as a meaningful change.

Exa serves from its index. Notably, Exa’s own documentation now deprecates the livecrawl parameter and states that it “does not guarantee freshly fetched parser output and may be served according to server freshness policy,” pointing users to maxAgeHours for freshness control instead. That’s a fair and honest description of how an index works, and it’s exactly the constraint that live retrieval doesn’t have.

Completeness

Exa’s documentation caps public search requests at 100 results, with higher limits available by contacting sales. For enumeration tasks that is a hard ceiling: if the honest answer is “all 340 attorneys at this firm” or “all 1,200 exhibitors at this conference,” a 100-result cap cannot produce it in one call, and stitching paginated queries into a complete, de-duplicated set becomes your engineering problem.

Retrieval and enumeration are different problems. Retrieval asks: can you find a relevant page or answer? Enumeration asks: can you identify the complete set of entities that satisfy this request? A system can be excellent at the first and still return an incomplete dataset — and the gap is invisible unless you already know the true count. Exa is one of the best retrieval products in the category. That is not the same as being built for enumeration, and the documented 100-result ceiling is the clearest illustration of the difference anywhere in this comparison.

Riveter’s Dataset Builder and Extractions are designed for enumeration — the complete requested set, not a sample. Teams describe the failure mode they’re escaping as “ChatGPT gave me 43 of 4,000.”

Discovery

Exa’s semantic retrieval is excellent, and for “what exists about this topic” it’s a real discovery mechanism. But retrieval and discovery aren’t the same task, and conflating them is how this comparison usually gets framed wrong.

Retrieval ranks pages that are already in an index. Discovery is finding where an answer lives when nobody has indexed it — following a trail from a company site to a state registry to a job posting to a PDF, and knowing when to stop. An index can rank brilliantly and still have never crawled the page that holds the answer, in which case no amount of ranking quality helps.

Riveter searches the live web at request time, so the reachable set isn’t bounded by what a crawler stored. It also doesn’t stop at ranked pages: discovery, navigation, extraction and structuring happen in the same workflow, so what comes back is the answer rather than a list of candidates to work through.

This is the source of the fill-rate difference, and it’s worth stating directly because the outcome is easy to mistake for the mechanism. Riveter doesn’t fill more rows because it’s tidier with the data it gets. It fills more rows because it finds more of them.

Multi-step workflows

Riveter’s enrichment columns feed into each other with logic, so one column’s answer determines what the next does — read the filing, identify the acquirer, then run a fresh search on that acquirer, then find the right people there. Exa gives you one high-quality search call per request; the chain is your code. This is the most common reason teams outgrow a search API, and it usually shows up as “I’ve got a whole pipeline stitched together and it keeps breaking.”

Hard pages, PDFs, and images

Riveter navigates complicated pages that normally require full browser automation and has simplified that to run fast and cheap at scale, and it reads large files, PDFs, and images as first-class sources. Index-based extraction is strongest on clean, static, text-heavy pages — which is most of the web, but not the part where the hard questions live. Large regulatory filings are the clearest case: the document is public, the answer is in it, and a search index will not read 200 pages of it for you.

What you get, and who it’s for

Exa returns ranked results, page contents, and highlights — high-quality input for something you’re building. Riveter returns a finished, structured, verified dataset through a single API, or streams live per-record results into a product flow. Both are legitimate products serving different jobs.

Total cost of ownership

Exa’s published pricing starts with a free Starter tier carrying monthly credits, with Search from $7 per 1,000 requests plus per-result charges, Contents at $1 per 1,000 pages, and Agent requests priced by effort mode. Riveter publishes a free plan, a $249/month self-serve plan, pay-as-you-go credits with automatic top-ups, and custom enterprise pricing that includes hands-on implementation and ongoing maintenance support. Check both pricing pages for current numbers.

The comparison that matters is cost per filled row, not cost per request. A search that returns nothing still bills, and the rows it missed still have to be filled by someone.

Where Exa wins

Worth knowing before you evaluate, not after.

  • Speed. Sub-180ms on the Instant tier is a different order of magnitude from any live-retrieval system, and for interactive agent loops that matters enormously.
  • Semantic search quality. Exa’s neural search is genuinely good at “find me things like this” — a query shape keyword search handles badly and live navigation doesn’t address at all.
  • Token efficiency. Highlights and token-efficient Contents output are well designed for feeding a model cheaply, and Exa publishes real numbers on it.
  • Published benchmarks. FRAMES, Tip-of-Tongue, and Seal0 results give buyers something checkable, which more of this category should do.
  • Breadth of adoption and ecosystem. Cursor, AWS, Databricks, HubSpot, Cognition — a very well-worn path with low procurement friction.
  • Price per search. For high-volume retrieval where an occasional miss is acceptable, the per-request economics are hard to beat.

If your problem is retrieval and an empty result is survivable, this comparison should end with Exa.

Where Riveter wins

Each of these is tied to a checkable scenario rather than an adjective.

The answer isn’t on any single page. A GTM team needed to know which of the 50 US states each large chemical manufacturer actually operates a facility in. No page states it. A company website won’t confirm that manufacturing happens in a given state, so the answer has to be assembled and verified across job postings, regulatory filings, and environmental reports — different kinds of sources, largely unindexed, each step depending on the last. They tried a search API first and the data came back badly incomplete. Riveter did it, and the deal closed in five days.

Finding the answer, not ranking pages about it. Ask for something no index has crawled — a value on a state registry, a figure inside a permit PDF, a detail on a mid-market company’s careers page — and semantic ranking has nothing to rank. Riveter runs its own searches against the live web and follows the trail to the source.

The same job, every week, across changing inputs. A team enriching every new account with the same twelve fields doesn’t need a better retrieval call — they need the workflow to exist as a thing they can rerun. Riveter makes the workflow the product: define it once, run it on new inputs or on a schedule, get the same schema back every time. With a search API, that orchestration is yours to build and keep working.

Live, per-record, inside your product. Because the same workflow accepts a single entry through the API, Riveter fits where a batch tool can’t: enrich a lead the second it signs up, resolve a record mid-flow, or serve an agent’s tool call — returning a finished, structured answer rather than ranked pages to post-process.

The list has to be complete. Ask for every attorney listed on a law firm’s site and an index returns the ones it holds, capped at its result ceiling. Riveter navigates the live site and returns the complete roster. Enumeration is a different problem than retrieval, and conflating the two is how projects ship with quietly missing data.

The work is A → B → C → D, across formats. An acquisition-intelligence team ingests an RSS feed of deal announcements, each linking to an SEC filing. For every deal they need the PE firm, the acquired company, the acquirer, and then the right people at each to contact. That fails on a single search call for two reasons: the filings are enormous PDFs, and the logic chains. In Riveter, that’s columns feeding columns — and because a column can read a PDF, pull a value off an image, or call a third-party API you already pay for, the chain doesn’t break when the next piece of evidence isn’t a web page. That’s what keeps multi-step workflows complete rather than partially filled.

The data has to stay current, in schema. Every request scrapes the live web, so results are as current as the source at the moment you ask; monitoring then re-runs the whole workflow on a schedule and hands back the same structure, refreshed — monitored extractions as often as every minute, enrichment monitors as often as every 15 minutes. A top US news source runs live election results through Riveter at 100% accuracy, refreshed every one to three minutes, across notoriously inconsistent county and state election sites — with self-healing extractions that regenerate when a source site changes.

The person who needs the data isn’t an engineer. Riveter’s Enrichments surface is a spreadsheet that’s also an API: a rev-ops lead defines columns in plain language and gets a finished result, and the same workflow is callable from the API when engineering wants it in a product flow — including from inside Clay.

When to choose Exa, and when to choose Riveter

Choose Exa if:

  • The job is semantic search: finding relevant pages, exploring a topic, feeding a RAG pipeline.
  • Latency matters more than completeness — an interactive agent loop where a fast partial answer beats a slow full one.
  • You want token-efficient page content to feed a model.
  • Your volume is high, the queries are broad, and an occasional empty result is acceptable.
  • Your team is developer-led and comfortable owning orchestration and the pipeline around the call.

Choose Riveter if:

  • You need to enrich records live, one at a time, inside a product flow or an agent’s tool call.
  • You need an answer for every record, and blank cells are the problem you’re trying to solve.
  • You need the complete set — all N of X — not a capped subset.
  • The task has dependent steps, where step one’s answer determines what step two searches for.
  • Your sources are mixed: web pages, large PDFs, images, and third-party APIs in one workflow.
  • The data has to stay fresh on a schedule, with alerting on meaningful change.
  • The buyer is a GTM, rev-ops, or data team that needs the finished result, not a search primitive.

And plenty of teams use both. Exa for fast, broad retrieval inside an agent loop; Riveter for the enrichment runs and recurring datasets where every row has to come back filled.

FAQ

Is Riveter a good Exa alternative?

Riveter is a strong Exa alternative when you need an answer for every record rather than a set of relevant pages. Exa searches a pre-built index and returns what it holds; Riveter’s agents go live to the open web at request time, navigate to the source, and return a finished, structured result — which is why it fills rows that index-based tools leave blank. If your use case is fast semantic retrieval, Exa is likely the better fit.

Can I use Riveter for real-time requests, or only to build a whole dataset?

Both. Riveter can build a complete dataset from a prompt and keep it fresh, or you can call the API with new entries as they arrive — each one runs through the same step-by-step workflow live and returns a finished result. That makes Riveter usable for on-demand, per-record work like enriching a lead the moment it signs up or serving an agent’s tool call, not just one-time bulk datasets.

How is Riveter different from Exa?

Exa is a search API built on its own web index. Riveter is a complete agentic-data system that runs the whole workflow end to end — search, navigate complex pages, extract, structure, verify, and monitor — and returns a finished, structured result through one API, in bulk or per record. The structural difference is live retrieval versus a pre-built index: Exa’s agent layer sits on top of its index, while agentic navigation of the live web is Riveter’s core product. Fill rate, completeness, and freshness all follow from that.

Why does Exa return blank results for some rows?

An index can only return what it has already crawled and stored, so records whose answer isn’t in the index come back empty. That’s inherent to the architecture rather than a defect. Riveter navigates to the source at request time, so it returns a value wherever the answer exists on the open web — which is what “fill rate” measures.

Does Riveter do semantic search like Exa?

Riveter searches the live web as part of its workflow, but it isn’t a semantic search primitive and doesn’t compete on retrieval latency. The difference is what you get back: Exa returns ranked pages and highlights, Riveter returns the structured answer — found, extracted, verified, and kept fresh in a fixed schema.

How does Riveter’s pricing compare to Exa?

Both are priced per action rather than per seat, and both offer a free tier. Exa publishes per-request search pricing; Riveter publishes a free plan, a self-serve plan at $249/month, pay-as-you-go credits with automatic top-ups, and custom enterprise pricing that includes implementation and ongoing maintenance support. Compare on cost per filled row rather than cost per request — a search that returns nothing still bills.

Can Riveter replace Exa?

Riveter can replace an Exa-based pipeline when the work involves enrichment at scale, complete enumeration, multi-step logic, large PDFs, live per-record enrichment, or scheduled refresh, because Riveter runs those natively instead of requiring you to orchestrate them. For fast, broad semantic retrieval inside an agent loop, Exa does that job well and many teams keep both.

Which is better for GTM and rev-ops teams?

Riveter is built for GTM, rev-ops, and data teams: describe the columns you want in plain language and get a finished, structured result, with the same workflow available live through the API for per-record use like enriching a lead the moment it signs up. Exa is developer-first by design and expects you to build the surrounding workflow.

Try Riveter on your hardest use case

Take the enrichment run that came back with gaps, or the list that kept missing entries, and see how many rows come back filled.

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