HostingBrain Intelligence

Named provider shares for one national market — who holds the market, and how much, at a chosen layer. For how CONCENTRATED the market is rather than who is in it, use market_concentration. Arguments: market = TLD ('de','nl'); layer = control_plane | hosting | email | web; offset= walks the full ranking (an analyst-tier feature — free and pro receive the top 50 plus a tail bucket). meta.pagination carries the total, has_more and next_offset. Population: the active-domains book of that market; shares are of that denominator, which ships with the response. Layer definitions: definitions(section='attribution_layers').

Where the EMAIL of a website population lives once its front door has moved to a SaaS builder — the cross-layer wallet-fragmentation view, and the analysis behind the brief "the hosting stack didn't disappear — it fragmented". Arguments: market (TLD, e.g. 'no'); operator (control-plane key, e.g. 'hyp.net') — the operator filter is a named-provider feature. Population: the active-domains book overall, and the builder-fronted subset of it, reported side by side so the two are never read as one. Method: definitions(term='stack_profile').

Where NEW websites start versus where the installed base sits, by hosting-front class — the leading-indicator question for share shift, ahead of anything the installed base shows. Arguments: scope = an aggregate ('global', 'europe', or 'europe_screened' — bulk moves excluded, the organic headline) or a single market ('dk'); empty = every row. The allowed values are exactly the scopes this release answers for and are published in this tool's own input schema. Every row says which kind it is in scope_kind: 'eu' is the .eu market, 'europe' is the multi-market aggregate. chart=true returns a branded SVG. Population: domains newly observed in the trailing eight complete weeks. That cohort is paced by when a domain enters our view, not by when it was registered. A market where a single nameserver holds an outsized share of the new cohort is flagged as a bulk event rather than read as momentum. Definitions: definitions(section='momentum').

How concentrated a hosting market really is — HHI and structure per market and layer, for a competition or market-entry question. Named provider shares are market_structure's job; this tool answers how tight the market is, not who is in it. Arguments: market = 'global', 'europe', or a European market TLD ('dk'); empty = all rows. actor_scope selects which actor base leads the control-plane rows. chart=true returns a branded SVG. Population: the active-domains book, at two layers — control_plane (ownership-folded customer relationships) and hosting (visible serving network, CDN-masked origins excluded). A named market's control-plane row serves BOTH actor bases side by side — market_power (hosting competitors only, the default headline) and control_plane_ecosystem (infrastructure providers counted alongside) — each with the book it is measured on. Returns HHI with its conventional bands, effective_n, top1 and top3. Band and actor-base definitions: definitions(section='concentration').

Where a market's price level sits, without naming operators — the aggregate view for benchmarking a price point or sizing a repricing opportunity. Returns per-market medians that must not be conflated (storefront, ownership-group, share-weighted, and a like-for-like entry-rung median), the share of the market priced as an introductory teaser, and hosting AND domain renewal-cliff statistics side by side, plus a named list of operators pricing far below the market's entry median while renewing far above its cliff median. Arguments: category = shared_hosting (default) | managed_wordpress | vps | dedicated | email | domain_registration | website_builder | ssl | addon; market = one country. Population: persistent priced products in that category and market. A statistic publishes only where the market carries enough independently priced operators; market_weight_pct ships with every median and thin_operator_base flags the concentration-qualified case. Named operators: pricing_profile. Per-operator domain cliffs: tld_pricing(view='cliffs'). Which median means what, the publication test, and when a cliff is withheld: definitions(section='pricing').

Domain-name pricing per registrar brand and market — register, renew, transfer, and the domain renewal cliff (renew divided by register), the first-year-teaser signal on the domain side of the bill. Use it for a named-brand domain price; use market_pricing for a market's aggregate level. Arguments: view = cheapest (register ranked per market and TLD) | cliffs (steepest measured renewal multiples) | matrix (register/renew/transfer per brand); market = ISO country code, any case; tld = '.com' or 'com'. Population: a curated per-market reference set — the local country TLD(s), .com, .info and a short generic tail — not every TLD in existence. cliff_status says which case a row is and cliff_suppression_reason why a withheld one is withheld; never compute renew divided by register yourself on a withheld row. The reference set, the cliff statuses and the two grounds for withholding a ratio: definitions(term='tld_pricing').

WHO OWNS SEARCH for a market's head hosting term — the customer-acquisition channel view. A large host that is invisible here is leaving search to its competitors. It answers a different question from market_structure's ownership view; compare them, do not substitute one. Arguments: view = leaders (per group: best rank and page-one listings) | rankings (the folded page-one list) | leads (ranked hosts we do not yet recognize as operators — coverage gaps and onboarding leads); market = ISO country code, any case. Population: page-one (top-ten) results for the market's head term, folded to brand AND owning group through the same operator registry the market-structure tools use. Method and caveats: definitions(term='search_visibility').

The published offers of one named brand or consolidator group — the per-operator price sheet behind a competitive-pricing question. One row per persistent product identity, category-labelled, each carrying its ladder rung (rung 1 = the entry offer, the cross-brand comparison unit — plan names are merchandising), entitlements, commitment and invoicing terms, and full economics in local currency, each figure marked as stated terms or estimate. Group queries add a posture per brand and market. Arguments: brand_or_group = the brand or consolidator; market='XX' for the full per-market set; category narrows to one product taxonomy; include_all=true returns the underlying source observations. Responses cap at 50 offers and say so when the cap is hit. Population: the offers we currently hold for that brand. renewal_unobserved means NO renewal price was seen — flat-versus-teaser is unknown, never read it as flat. Field, posture and confidence vocabulary: definitions(term='pricing_profile'). Price changes over time: pricing_moves.

Whether an operator's prices actually moved, and what kind of move it was — the dated event feed behind a repricing or renewal-conduct question. Each event compares two consecutive observations of the SAME persistent product; change_class says what moved (economics = a comparable price, merchandising = promotional presentation only, observation = neither), and alert_grade is true only for a confirmed economics event. Arguments: brand_or_group and market narrow the feed to one operator or country; category narrows to one product taxonomy — a market query otherwise mixes shared hosting, domains and email in one feed; change_type names the non-commercial classes, which are excluded by default; include_all=true adds the underlying per-observation rows, labelled, which are not validated commercial intelligence; offset pages a total order, so the same request over the same priced data returns the same events in the same order and total_matching says what the page left out. Population: priced products we hold at least two dated observations for. The series is short by construction and an empty early feed is the honest state. Event classes, confidence grades, and how two prices are made comparable before they are compared: definitions(term='pricing_moves').

Email-authentication posture across a market or an operator's book — SPF and DMARC adoption and, more importantly, STRICTNESS: how much of the published policy actually rejects spoofed mail rather than merely monitoring it. Arguments: market grain ('global', 'europe', or a European TLD) is open; operator-book grain is a named-provider feature. Population: the mail-carrying part of the active-domains book — every percentage is of the active domains that carry mail, not of all websites. A count of policies published in the wrong place is reported separately as a misconfiguration, never as adoption. Field definitions and the 'protection theatre' reading: definitions(section='email_security').

How sticky each layer of the hosting stack ACTUALLY is — measured switching rates, for a churn-assumption or land-and-expand question. This is the proof behind "front doors migrate quickly, trust migrates slowly". Arguments: scope = 'global' or 'europe'; empty = both. chart=true returns a branded SVG. Population: the active-domains book over the trailing 26 weeks, annualized. A true provider switch is distinguished from onboarding and lapse journeys, aftermarket rotation, a serving move that left the customer relationship intact, and a provider's own address housekeeping — so the rate is not inflated by movement that is not switching. Metric definitions: definitions(section='switching').

Which SaaS categories an operator's customers have adopted — the attach-rate view for an upsell, whitespace or book-quality question, per hosting book rather than per market. Arguments: grain='group' (default) rolls up to the consolidator that owns the book; grain='operator' drills to one nameserver book. operators = 1-3 comma-separated names to compare; a name with no book at the requested grain falls back to its group and the response says so. chart=true returns a branded SVG. Population: the MEASURED part of each book — the active domains of it we hold a page observation for — published on every row beside the book's size and its measured share. Compare books only at similar measured share; the response warns when a compared set spans a wide range. Category definitions and the coverage caveat: definitions(section='saas').

What technology the European web runs on — CMS, e-commerce, analytics, marketing, CRM, consent, support chat, security, anti-bot, backend, hosting, site builder, AI builder and AI API adoption, plus social presence — read from the page itself. Its neighbour header_adoption reads server response headers on a different population; vendor_adoption reads the DNS relationship layer. The three are not additive. Arguments: no category/provider/operator = adoption across every category published (free); naming category or provider = the per-provider breakdown (Pro; WordPress vs Wix vs Shopify). market = national-market TLD ('de','nl'); operator = a hosting operator or consolidator book ('ionos', 'team.blue' — brands resolve); grain = 'group' (default) or 'operator'; scope = 'content' (default, pages classified homepage or commerce) or 'all' (every measured website, including pages carrying none of those markers). Population: the active domains we hold a page observation for — a measurement subset of the book, not a billing count; the coverage is stated in every response. Benchmarks, the content classification and caveats: definitions(term='technology_adoption'). Examples: technology_adoption() ; technology_adoption(category='social') ; technology_adoption(provider='shopify', market='nl') ; technology_adoption(provider='wordpress', operator='hostnet.nl', grain='operator').

What INFRASTRUCTURE the European web runs on — CDN, origin cache, web server, control panel, PaaS and runtime — read from the server's own response headers. Answers "who runs Varnish, LiteSpeed, Plesk, IIS, OpenResty" by operator, market or overall. Its neighbour technology_adoption reads the PAGE instead, on a different population: do not add or compare the two. Arguments: no category/provider/operator = adoption across the categories (free); naming category or provider = the per-provider breakdown (Pro). market = national-market TLD ('de','nl'); operator = an operator or consolidator book ('ionos','group.one' — brands resolve); grain = 'group' (default, the consolidator book) or 'operator' (that brand's own nameserver book); scope = 'all' (default) or 'content' (the homepage and commerce cut). Population: websites carrying at least one recognized infrastructure header — NOT the operator's whole book. Every share is a floor: absence of a header is absence of evidence, never evidence of absence. Caveats and method: definitions(term='header_adoption'). Examples: header_adoption() ; header_adoption(provider='varnish', operator='group.one') ; header_adoption(category='cache') ; header_adoption(provider='varnish', operator='one.com', grain='operator').

WHO uses WHICH SaaS vendor, read from DNS rather than from the page — the relationship layer complementing technology_adoption. Two mechanisms answer two different questions: mechanism='verification' = domain-verification records, evidence the relationship was ESTABLISHED; mechanism='spf_include' = which platform SENDS the domain's email. Arguments: vendor = a name fragment ('openai','sendgrid'); mechanism = verification | spf_include; market = TLD ('de','nl'); top_n caps the rows; default = the all-markets rollup, most adopted first. Population: mechanism-specific and stated on every row — for verification, websites publishing such a record; for spf_include, those publishing a sending policy. An established relationship, not a billing count. Method and caveats, including what these records do and do not prove: definitions(term='vendor_adoption').

List INDEPENDENT hosting operators - well-integrated and not owned by any consolidator the ownership ledger maps - by size and market. A description of the independent segment, not a recommendation. Arguments: country_tld filters by the operator's dominant market; min_live sets the size floor and max_results the page size. Population: independent operators above the infrastructure-cohesion floor, above the size floor, and not owned by any consolidator the ownership ledger maps.

Full profile of ONE hosting operator — footprint, geography, infrastructure signature and cohesion, ownership check, book health and industry mix. Arguments: brand_or_operator = an NS brand ('kasserver.com') or a provider id. Population: that operator's own book. For the packaged one-call decision-support view across all sections and its peers, use the dossier tool instead.

Scorecard for ONE consolidator group (group.one, team.blue, your.online, united_internet, ovhcloud…) — raw versus integrated book, integration quality, and the customer base's industry mix, when the question is about one owner rather than the whole landscape. Arguments: group = the consolidator, or a brand that resolves to one. Population: the group's folded book; each figure states the book it is measured on. For the whole market side by side, use consolidation_landscape.

The quality of an operator's book, not its size: dead-page share, modern-email and SaaS-web adoption, geographic and industry concentration, plus tenure and churn. Arguments: operator = a consumer BRAND, a nameserver operator, or a consolidator GROUP; brand names resolve automatically (ionos to united_internet, domeneshop to miss_group) and prefer the group rollup, so a whole consolidator's book health is one call. chart=true adds a branded book-composition SVG. Population: the named book; the 'grain' field on each row says whether it is a single operator book or a folded group. Metric definitions: definitions(section='book_metrics').

Who is winning and losing customers against WHOM — direction-labelled migration flows involving a consolidator group, for a competitive-dynamics question. Movement WITHIN one group's own portfolio is group_movement's job, not this one's. Arguments: subject = a consolidator group; layer = ns (the customer relationship) | host (the serving network). Population: observed provider changes between the two dated readings. Magnitudes are DIRECTION-ONLY until the longitudinal panel calibrates them — read the direction, not the size.

Side-by-side comparison of 2-6 named operators on footprint, book health and stability — for ranking a shortlist on one screen. Arguments: operators = a list of 2-6 names; brands resolve to the group that owns them. Population: each operator's own book, each figure labelled with the book it is measured on so unlike books are not silently ranked against each other.

THE INDUSTRY MAP of hosting consolidation — every consolidator group side by side: live-business footprint, integration quality, sponsor and hold status, and acquisition activity including the hosting-versus-software mix and latest deal. The one-call orientation on who is consolidating the European hosting market and how well. Arguments: none required. Population: the mapped consolidator groups; each row states the book its footprint is measured on, and a measurement we do not stand behind for a given group is withheld with the reason attached rather than served.

Takes no parameters.

HOW WELL a consolidator actually consolidates — the post-acquisition integration question. No argument returns the scoreboard: what share of the portfolio sits on group platforms, and how deeply ledger-matched acquisitions were integrated (a low score is a buy-and-hold FINDING, not an error). Arguments: group = a consolidator or a brand that resolves to one, returns the per-brand breakdown with acquisition dates and two drill-down levels that must not be conflated — how much of each brand's book already serves from the group backbone, and which machines serve it (a brand can be moved onto the group network while still running on its legacy fleet); include_network_map_detail = ask for the per-node and per-adjacency LISTS inside each brand's network_map. Off by default because this answer is large: the map's accounting (counts, namer mix, roles, placement rule) always ships and every block states how many nodes and adjacencies it read, so nothing is held back — the lists are asked for rather than sent unconditionally; brand = one brand id from the group answer's own brands[], which narrows the per-brand breakdown to that brand ALONE and leaves every other brand out. Nothing else narrows — the group row and the fleet, network and serving-location summaries are measured over the whole group either way, so a narrowed answer is never a narrower population — and brand_selection says how many brands the group has and how many this response is carrying. Use it when a wide group's full answer is too large to read; a brand that is not one of this group's is refused WITH the roster, never answered with a guess. Population: the group's brands, each placed in one integration stage from its own dominant serving, mail and DNS identity against the group's shared platforms. Weekly dating makes trajectories readable. Stage vocabulary, weights and worked examples: definitions(term='integration_depth').

ONE-CALL DOSSIER on a hosting operator or consolidator group — the packaged decision-support view when the question is "brief me on this company" rather than one metric: executive summary, market position, book quality, stability, SaaS attach, email-security posture, integration status with the acquisition ledger, assembled risks and a peer table. Arguments: target = a consumer brand ('ionos'), an operator ('domeneshop') or a group ('group.one'); detail='summary' (default: executive summary + risks), 'standard' or 'full'; or sections= 'pricing,integration,…' for exactly those. meta.sections lists what is available and what was returned. Population: every count is labelled with the book it is measured on, and the response reconciles the books it mixes; known measurement notices are declared with the figures they qualify. It is market research built from public signals: an input to the reader's own analysis, not investment advice and not a substitute for diligence. What each section measures: definitions(term='dossier').

How complete HostingBrain's universe is — the honest answer to "what share of the market do you actually see?", calibrated against PUBLIC REGISTRY totals (Norid .no, SIDN .nl and similar), plus operator-level calibration examples. Arguments: include_history=True returns the full dated series (the coverage trend); the default returns the latest calibration per market and metric. Population: our observed web-provisioned population against officially registered totals for the same market. Method: definitions(term='coverage_calibration').

Whether a provider's customers keep all their domains with it or spread them across several — a book-QUALITY signal no single provider can measure about itself, for a retention question. Arguments: no argument = the aggregate loyalty distribution (free): share of owners with a single provider, average wallet share, and the cross-border cut. provider= a consolidator or a brand that resolves to one returns its named exclusivity score against peers (analyst tier). chart=true returns a branded SVG. Population: owners identified from shared analytics or advertising accounts only, which is host-independent; certificate-based ownership detection is confounded with single-hosting and is excluded. is_layer marks CDN and DNS providers, where low exclusivity is expected. Method and caveats: definitions(section='faithfulness').

How much a consolidator moves customers WITHIN its own portfolio — the "is this owner reorganizing its book" question, kept separate from wins and losses against other groups (migration_flows). Arguments: group = a consolidator or a brand that resolves to one; empty = the groups with the most internal movement. Deal tier. chart=true returns a branded SVG. Population: two intent-neutral tiers on the group's own book — the share that moved between the group's OWN provider identities over 90 and 365 days, and the subset attributable to distinct known brands as neutral observations (volume, shape, timing). We do NOT label a flow 'consolidation' — that inference is yours; the acquisition ledger is cross-referenced only as corroboration. Definitions: definitions(section='group_movement').

Which websites appear to be run by the same operator, and HOW STRONG the evidence is — the corroboration question behind an undisclosed-ownership or shared-operation claim. Use it for evidence about a cluster of domains; for a hosting group's book use consolidator_scorecard or dossier. Arguments: domain= finds the cluster containing that domain; operator= takes a cluster's representative domain key. Population: clusters built from public corroborating signals — shared certificates and shared analytics or advertising accounts. Every result carries a grade family that is LOAD-BEARING and must not be overstated: ownership evidence, ownership plus a shared account (highest confidence), and shared operations only — the last is the same digital operation, which may be one owner, a franchise or a shared agency, and is NOT a legal-ownership claim. Analyst tier. Corroborating evidence, never legal proof. Grades: definitions('ownership').

Search HostingBrain's hosting-market intelligence — canonical definitions, published analyst briefs, and provider or consolidator names — when you need to find the right term, brief or entity before asking a numeric question. Arguments: query = what to look for; results carry ids usable by the fetch tool, plus meta.suggested_tool, an intent-routed pointer into the dedicated analytical tools, which give richer structured answers.

Fetch full detail for a search result id — the companion to the search tool. Arguments: id = 'def:<term>' (a canonical definition with its caveats), 'brief:<slug>' (a published analyst brief summary and link), or 'operator:<group>' (a free-tier aggregate view).

How fresh the data behind an answer is — the public freshness summary for the current analytical release, for when a number's date matters to the decision. Arguments: none. Returns the external metadata only. Term-level methodology and book-type definitions are available through definitions(term=…).

Takes no parameters.

Report a problem or suggestion so HostingBrain can improve — for the calling model or a user. Use it when a number looks WRONG or contradicts another tool, when a tool was confusing or missing, or when you have a suggestion. Arguments: kind = data_error | confusing | missing | suggestion | other; about = the tool or topic it concerns; reference = the specific value or entity in question (e.g. 'team.blue hosting_footprint', 'one.com DK price', 'market_concentration dk'). Logged against the current data release for human review and returns a receipt id. Data-error reports that name a specific tool AND value are the most actionable.

The canonical HostingBrain glossary — every dataset, coverage tier, attribution layer, metric and evidence grade in one authoritative place, so nobody conflates our terms. Consult it whenever a term's exact meaning, denominator or confidence grade matters; it is the source every other tool's response points back to. Arguments: no arguments = the full grouped overview; term='<key or phrase>' = one definition with its caveats (e.g. 'hhi', 'web_active', 'own_network_serving_share_pct', 'operator_switch', 'ownership'); section='<name>' = one part of the glossary (coverage_tiers, attribution_layers, book_metrics, concentration, switching, momentum, saas, email_security, ownership, access, method).

WHO, WHEN and WHAT in the industry's move up-stack — hosting consolidators branching into SaaS and adjacent software: the dated non-hosting deal timeline, each group's hosting-versus-software acquisition mix, and the acquisition clusters the deals fall into. Arguments: none. Population: the curated acquisition ledger — publicly reported deals, each carrying the date its source states. It is a record of what was ANNOUNCED, not of everything that happened.

Takes no parameters.

How digitized a market's small businesses are — modern email plus SaaS web adoption in one index, as macro context for a market comparison or a whitespace question. Arguments: market_grain='country' = the like-for-like national ranking; 'generic_tld' = generic namespaces (.io/.shop/…); '' = both, each row still labelled with its grain. market='pl' (a country code, or a namespace like 'com.au') returns that market's own row and its rank within the full ranking, wherever it sits — the ranking page is capped by top_n, a named market is not. Population: the active-domains book of each market. Score formula and caveats: definitions(term='market_digitization').