outscraper vs phantombuster vs hunter.io: b2b lead gen tools compared

outscraper wins for google maps and review scraping at scale. phantombuster wins for linkedin automation and multi-platform workflows. hunter.io wins for email finding and verification. all three are mature in 2026 with established products and clear pricing. they solve different parts of the lead gen pipeline. most teams running b2b outbound use two of the three together rather than picking one.

this comparison covers what each tool does well, where they overlap, what they cost, and how they fit into a 2026 outbound stack.

quick verdict by job

job	best tool
scrape google maps for local businesses	outscraper
pull google reviews and ratings	outscraper
extract emails from a domain	hunter.io
verify if an email actually delivers	hunter.io
find linkedin profiles by company	phantombuster
auto-message linkedin connections	phantombuster
scrape twitter, instagram, sales nav	phantombuster
build a list of agencies in a city	outscraper
find the cmo of a company	hunter.io or phantombuster

what each tool actually does

outscraper

outscraper sells public data scraping as a service. their api covers google maps places, google reviews, google search, ebay, amazon, yellow pages, trustpilot, and 30+ other sources.

you submit a query (e.g. “dentists in singapore”) and outscraper returns a structured csv or json with name, address, phone, website, rating, review count, and contact details where available. for reviews you get the full review text plus author metadata.

it is the most reliable way to build local business lists at scale without running your own scraping infrastructure.

phantombuster

phantombuster is a no-code scraping and automation platform with 100+ pre-built “phantoms” for linkedin, twitter, instagram, facebook, sales navigator, indeed, and others.

each phantom is a worker that runs in the cloud on a schedule. you set up a linkedin search url, point a phantom at it, and it scrapes profiles into a csv. another phantom can take that csv and send connection requests with personalized messages.

phantombuster excels at multi-step workflows. example: scrape sales nav search, enrich with hunter.io emails, send linkedin invites, then email follow-ups. the chaining is the moat.

hunter.io

hunter.io is the dominant email finder. you give it a company domain and it returns email addresses for people at that company, scored by confidence. their email verifier checks if a given email is deliverable without sending.

the data comes from public web sources (company websites, mailing lists, github commits, signature scraping) and is updated continuously. they cover 200m+ companies.

hunter is narrow but deep. emails are the only thing it does. they do it better than any general-purpose tool.

pricing in 2026

tier	outscraper	phantombuster	hunter.io
free	500 results/month free	14-day trial	25 lookups/month
starter	$35/mo (2,000 results)	$69/mo (20 hours)	$49/mo (500 lookups)
business	$99/mo (10,000 results)	$159/mo (80 hours)	$149/mo (2,500)
enterprise	custom	$499/mo (300 hours)	$499/mo (10,000)
pay-per-use	yes ($0.0035/result)	no	yes

outscraper bills by results returned. phantombuster bills by execution time of phantoms. hunter bills by lookups (one email find = one lookup). these are different units, which makes direct comparison hard.

for 1,000 google maps places per month, outscraper at $35 is cheapest. for the same data via phantombuster, you need a maps phantom (exists, slower) and budget around 5 hours of runtime, fitting in the $69 plan.

for 500 emails per month, hunter at $49 is the price-per-email leader. phantombuster has an email finder phantom but it consumes execution time and is less accurate.

data quality

outscraper data quality is high for google maps fields (name, address, phone, hours, rating). emails and “owner contact” are best-effort enrichments and accuracy varies by region. for sg and us markets it works well; for sea outside major cities, expect 30 to 50 percent miss rates on emails.

phantombuster data quality depends on the source. linkedin scrapes are reliable but limited by linkedin’s rate limits per session. instagram and twitter scrapes are less reliable because those platforms invest heavily in anti-bot. expect 60 to 90 percent accuracy depending on the phantom and recency of platform changes.

hunter.io email accuracy is the highest in the category. their verifier reduces hard bounces to under 2 percent in our testing across 10,000 emails. for cold email deliverability, hunter is what your inbox-warming team will demand.

gdpr and legal posture

all three have public posture statements on gdpr and ccpa.

outscraper scrapes only public data and provides standard privacy controls. they comply with subject access requests. their tos passes responsibility for downstream use to the buyer. read their outscraper terms before using for eu prospects.

phantombuster sits in a grayer zone. linkedin scraping violates linkedin’s tos but is generally legal under us case law (hiq v linkedin). phantombuster’s tos disclaims liability for platform-tos violations and pushes that onto the user. for eu prospects, run gdpr legitimate-interest analysis and document it.

hunter.io operates under legitimate interest in the eu and offers explicit subject removal flows. it is the cleanest of the three from a gdpr standpoint because it surfaces public-facing emails that companies have already published.

for any cold outreach campaign in the eu or uk, document your lawful basis before sending. the european data protection board guidance is the source of record.

integration and api

feature	outscraper	phantombuster	hunter.io
rest api	yes	yes	yes
zapier	yes	yes	yes
make.com	yes	yes	yes
webhooks	yes	yes	yes
crm direct integrations	hubspot, pipedrive	hubspot, salesforce	hubspot, salesforce, pipedrive
chrome extension	no	yes (linkedin)	yes

phantombuster and hunter both ship chrome extensions. outscraper does not. for sales reps doing manual research alongside automation, the extensions matter. for engineers building a pipeline, the rest apis matter.

all three offer good documentation and active 2026 maintenance. official refs: outscraper api, phantombuster api, hunter api.

the typical b2b outbound stack in 2026

most teams we see running outbound use two or three of these tools together.

stack 1: local services targeting (agencies, dentists, contractors)

• outscraper for the maps list
• hunter.io for emails of decision-makers at each company
• email outreach tool (mailchimp, instantly, smartlead)

stack 2: linkedin-first b2b saas outbound

• sales nav search url
• phantombuster to scrape profiles + send connection requests
• hunter.io to enrich profiles with emails for parallel email touch
• crm to track responses

stack 3: account-based marketing on enterprise targets

• internal tam list
• hunter.io to find decision-makers per company
• phantombuster for linkedin warm-up
• email outreach for direct touches

few teams use only one tool. each fills a different stage of the funnel. for the proxy and ip side of any cold outbound see our proxies for lead generation guide.

when to skip all three

three scenarios where you do not need any of these.

zoominfo or apollo subscriber. if you already pay for a contact-data platform, the email finder use case is covered. you may still want phantombuster for linkedin automation. for the proxy considerations on these platforms see our apollo and zoominfo proxies guide.

referral-led growth. if 80 percent of your pipeline comes from referrals, scraped lists rarely outperform asking customers for warm intros.

eu enterprise b2b. for large accounts in the eu, scraped data is risky. legitimate prospecting through events, content, and inbound has lower legal exposure.

for the multi-account safety side of running phantombuster at scale see our multi-accounting proxies guide.

cost example: 1000 quality leads per month

target: 1000 verified b2b emails of saas marketing managers at companies with 50 to 500 employees.

stack a: outscraper + hunter.io
– outscraper: build seed list from linkedin company-id mapping + google maps presence: $35
– hunter.io: 1000 lookups + 500 verifications: $49 to $99
– total: roughly $85 to $135 per month

stack b: phantombuster + hunter.io
– phantombuster sales nav profile scraper: $69 (20 hours, fits 1000 profiles)
– hunter.io: 1000 lookups: $49
– total: $118 per month

stack c: phantombuster only with email-finder phantom
– phantombuster business plan: $159
– email accuracy noticeably lower; expect to verify externally
– total: $159, lower data quality

practical advice: run hunter alongside whatever scraping tool you pick. the verification step is what keeps cold email deliverability above 95 percent.

faq

is outscraper legal to use?

outscraper scrapes public data only. for eu and uk prospects, run a gdpr legitimate-interest analysis before using the data for outreach. for us prospects, ccpa applies. always offer an opt-out in your outreach.

is phantombuster against linkedin’s tos?

scraping linkedin technically violates their tos. us case law (hiq v linkedin) generally protects scraping of public profiles for now. linkedin actively rate-limits and bans suspected scraping accounts. use cooldowns, residential proxies (separate from your main account ip), and budget for occasional account replacements.

what is the most accurate email finder in 2026?

hunter.io is the leader for accuracy and deliverability in our testing across 10,000 emails. apollo and zoominfo can match it on enterprise targets but cost more. for budget tools, neverbounce and zerobounce are competitive verifiers but less strong on the discovery step.

can these tools replace zoominfo or apollo?

partially. for smb and local-business prospects, outscraper plus hunter.io covers most use cases at one-tenth the price. for enterprise contact data with org charts and intent signals, zoominfo and apollo still have richer datasets.

do i need proxies to use phantombuster?

phantombuster runs in their cloud with their own ip pool. you can also bring your own proxies to reduce risk on linkedin and other rate-limited targets. for paid linkedin sales nav accounts, residential proxies are recommended to avoid suspension.

what is the cheapest way to build a b2b lead list in 2026?

outscraper for the seed list (e.g. all dentists in a city) plus hunter.io for emails. for a 1,000-email list this runs around $85 per month total. cheaper than zoominfo, apollo, or building a custom scraper.

the bottom line

these tools solve different parts of the same funnel. outscraper handles structured data scraping at scale. phantombuster handles social platform automation. hunter.io handles email discovery and verification. the right answer is rarely “pick one” but “pick the two that fit your stack.”

for local services and smb outbound, outscraper plus hunter is the cleanest combination. for linkedin-first b2b saas, phantombuster plus hunter is standard. for enterprise abm, layer all three onto a zoominfo or apollo seat.

run gdpr lawful basis docs for eu prospects regardless of which tool you pick. cheap leads with bounces and complaints cost more than no leads at all.

best data marketplace platforms 2026: where to buy and sell datasets

the top 10 data marketplaces in 2026 are aws data exchange, snowflake marketplace, databricks marketplace, dawex, datarade, narrative.io, bright data dataset marketplace, kaggle datasets, datafiniti, and data.world. aws and snowflake dominate enterprise. dawex and datarade are the leading neutral platforms. kaggle stays the largest free pool for analysts. each handles delivery, payouts, and licensing differently.

this guide breaks down what each platform sells, how sellers get paid, and which marketplace fits buyer use cases from b2b leads to alt-data hedge fund inputs.

quick comparison: 2026 data marketplaces

marketplace	best for	pricing model	seller payout	dataset count
aws data exchange	aws-native enterprise	subscription, one-time	70/30	3,500+
snowflake marketplace	snowflake customers	revenue share, subscription	90/10	2,000+
databricks marketplace	lakehouse users	free or revenue share	90/10	1,000+
dawex	neutral b2b data	one-time or subscription	80/20	600+
datarade	discovery-first	varies by seller	seller-set	2,500+
narrative.io	identity and audiences	subscription	varies	200+
bright data	scraped public data	subscription	n/a (single seller)	200+
kaggle	analysts and ml	mostly free	n/a	300,000+
datafiniti	retail and business listings	api credits	70/30	n/a
data.world	community and open data	freemium	varies	100,000+

aws data exchange

aws data exchange is the default for enterprise buyers running on aws. data is delivered as files into your s3, redshift, or via api. payment runs through your aws bill so procurement is simple.

categories include financial data (refinitiv, factset), healthcare (iqvia), location (here, foursquare), and weather (the weather company). entry prices range from free to $50,000 per month for premium feeds.

sellers get 70 percent of revenue. integration with aws billing and analytics workloads is the moat. if your buyer or your team is already on aws, this is where to start.

snowflake marketplace

snowflake marketplace delivers data as live shares directly into your snowflake account. there is no etl or file copy; the data appears as a database you can query immediately.

this is the cleanest delivery model in the industry. for snowflake-native teams it is faster to integrate than any other marketplace by an order of magnitude.

sellers earn 90 percent of revenue, the highest split among major platforms. dataset count is smaller than aws but quality skews higher because most listings are vetted. categories include market data, identity graphs, and consumer panels.

databricks marketplace

databricks marketplace launched in 2023 and grew fast in 2025-2026. data is delivered as delta sharing tables, similar to snowflake’s live share but cross-cloud.

it currently leans heavily on free public datasets and ai training corpora. paid commercial listings exist but the catalog is smaller than snowflake. sellers earn 90 percent on paid datasets.

if your team is on databricks lakehouse, this is the natural fit. otherwise the snowflake or aws marketplaces have deeper paid catalogs.

dawex

dawex is the leading neutral b2b data exchange. it is cloud-agnostic, sells one-time and recurring data products, and runs in multiple regions including europe (gdpr-friendly defaults) and asia.

their seller dashboard is the most polished outside the hyperscalers. you upload, set licensing terms, price per buyer or per region, and dawex handles payments and contracts.

revenue share is 80/20 in favor of sellers. for vertical-specific datasets (mobility, energy, retail) dawex often has more depth than aws or snowflake.

datarade

datarade is more of a discovery layer than a marketplace. they list 2,500+ data products from 500+ providers, route buyer rfqs, and broker the deal. the actual data delivery happens off-platform between buyer and seller.

this works well for buyers who want one search across many providers. it is less smooth than snowflake’s in-account share. seller pricing is fully provider-set.

datarade is the right starting point if you do not yet know which provider has what you need. their search filters by category, geography, and use case are the best in the industry.

narrative.io

narrative.io specializes in identity, audience, and consumer data. they run a real-time bidding-style data ops platform where data is licensed in continuous streams, not files.

if you are buying audience segments for ad targeting, customer enrichment for crm, or identity graphs for fraud, narrative is purpose-built. for static datasets, look elsewhere.

bright data dataset marketplace

bright data sells pre-scraped public web data as datasets: linkedin profiles, amazon products, instagram, tiktok, glassdoor reviews, indeed jobs, and 200+ more. you buy the most recent snapshot or subscribe to refreshes.

this is single-seller (bright data only) but the depth on public web data is unmatched. for competitive intelligence, e-commerce pricing, and social listening, the time-to-data is faster than scraping yourself.

pricing is per record or per gb. for context on pricing benchmarks see our proxy pricing comparison and the best web scraping apis guide.

kaggle datasets

kaggle hosts 300,000+ free datasets used mainly by analysts, ml engineers, and competition participants. licensing varies; many are creative commons or public domain.

it is not a commercial marketplace. there is no payment infra and no commercial licensing layer. for prototyping a model or learning, it is the largest free pool. for production data licensing, look at the platforms above.

datafiniti

datafiniti specializes in business listings, product data, and property records as api endpoints. you pay per query or per record. the data is scraped and normalized from public sources.

it competes with bright data’s dataset marketplace and outscraper for similar use cases (lead enrichment, retail intelligence). for our take on the b2b lead tool category see outscraper vs phantombuster vs hunter.io.

data.world

data.world started as an open data community. in 2024 it pivoted to enterprise data catalog and governance, and the marketplace component is now secondary to their cataloging product.

for free open data with social features (comments, queries, shared notebooks) it remains useful. for commercial licensing it is no longer competitive.

buyer checklist before purchasing

four checks save weeks of contract back-and-forth.

licensing. confirm whether the data is licensed for internal use, commercial product use, or resyndication. these are three different price tiers on most platforms.

freshness. ask the seller for the typical data delivery cadence (real-time, daily, weekly, monthly) and what “current” means in their schema. some “live” feeds are 24 hours stale.

geography and pii. for eu and uk buyers, confirm gdpr basis. for california buyers, confirm ccpa compliance. data marketplaces are intermediaries, not regulators.

trial access. every reputable marketplace offers a sample. always pull 1,000 rows before committing to a year-long contract. many “verified” datasets are not what the marketing claims.

for news data buyers specifically see our news apis comparison which covers similar buying decisions for content feeds.

seller checklist before listing

want to monetize data you already collect? four things matter.

audience. listing on snowflake reaches snowflake customers. listing on aws reaches aws customers. listing on dawex reaches everyone but with less integration. pick by where your buyers already work.

revenue share. snowflake and databricks pay sellers 90 percent. aws is 70 percent. dawex is 80 percent. for high-volume listings the difference compounds.

delivery effort. live share platforms (snowflake, databricks) handle data ops for you. file delivery platforms (aws to s3) need more buyer-side wiring. for solo sellers, live share platforms are easier.

contracts. most platforms ship a default eula. read the resale, sublicensing, and warranty clauses. dawex and aws let you customize terms more than snowflake.

faq

what is the largest data marketplace in 2026?

aws data exchange has the most paid commercial listings at 3,500+. kaggle has the largest dataset count overall at 300,000+ but most are free and non-commercial.

what does a data marketplace charge sellers?

snowflake and databricks take 10 percent. dawex takes 20 percent. aws takes 30 percent. datarade and data.world have variable terms set by the seller. budget the platform cut into your retail price.

can i buy real-time data from a marketplace?

yes. snowflake live shares update in real time within snowflake. aws data exchange supports real-time api feeds for some sellers. for streaming-heavy use cases (ad targeting, fraud) narrative.io is purpose-built.

how do i evaluate dataset quality before buying?

every reputable marketplace offers a sample or trial. pull at least 1,000 rows, profile completeness, freshness, and schema accuracy, then run your actual production query against it. if the seller refuses a sample, walk away.

is data marketplace licensing compliant with gdpr?

it depends on the dataset and the seller’s lawful basis. eu and uk buyers should require the seller to disclose lawful basis (consent, legitimate interest, contract) and the data subject’s rights process. for context see the european commission data act overview.

do hedge funds buy from data marketplaces?

yes. alt-data is a multi-billion-dollar segment in 2026. funds typically buy from snowflake marketplace, aws data exchange, or specialized providers (yipitdata, second measure) directly. neutral marketplaces like dawex are less common in this segment.

the bottom line

for enterprise buyers on aws, snowflake, or databricks, use the marketplace native to your stack. for cross-cloud buyers, dawex is the cleanest neutral platform. for discovery, datarade is the search engine of data marketplaces.

for sellers, snowflake and databricks pay the best revenue share at 90 percent and handle data ops via live share. aws reaches the largest enterprise buyer base at a 70/30 cut. dawex sits in the middle on both axes and wins on neutrality.

run a sample pull before any contract. the gap between “marketplace listing” and “production-ready data feed” is wider than most buyers expect.