Snowflake adoption trends in PredictLeads data show strong growth that Snowflake does not have to itself. Trailing-12-month new Snowflake detections are up 72.0% year over year (13,635 vs. 7,929), yet Databricks (+71.7%) and Google BigQuery (+71.1%) are growing at a statistically indistinguishable rate, and against close cloud-warehouse competitors, review-confirmed switches still favor Snowflake by roughly 2 to 1 (104 into Snowflake, 54 out).
If you follow cloud data infrastructure through quarterly earnings, you are reading lagging, aggregated, self-reported numbers. Company-level technology detection data shows who is moving, in which direction, and where the evidence gets thin. This report covers the adoption curve, competitive momentum, the migration matrix, named public-company examples, and the caveats that decide how far each number can be trusted.
This is a data and methodology showcase, not investment advice. It makes no attempt to reconcile PredictLeads detections with Snowflake’s reported revenue, customer counts, or net revenue retention.
TLDR:
- Trailing-12-month new Snowflake detections rose 72.0% year over year (13,635 vs. 7,929), the fastest trailing-12-month growth Snowflake has shown in PredictLeads data since 2019 and 2020.
- Snowflake is holding pace with its closest rivals rather than pulling away: Databricks (+71.7%) and BigQuery (+71.1%) grow at the same rate, while Amazon Redshift (+48.1%), Azure Synapse Analytics (+37.9%), and Microsoft Azure SQL Database (+13.0%) grow markedly slower.
- Switch signals favor Snowflake 389 to 95 overall; against close warehouse competitors only, the split is 104 to 54, still roughly 2 to 1 in Snowflake’s favor, with the remaining all-technology margin driven by general-purpose databases such as Microsoft SQL Server (108) and Microsoft Access (85).
- Databricks and Azure Databricks account for 29 of Snowflake’s 54 close-competitor losses (54%), the largest single source of losses; on the wins side, SAP HANA is the largest single source at 30 of 104, ahead of Databricks and Azure Databricks combined (15).
- PredictLeads detects Snowflake almost entirely through hiring: 95.2% of 39,525 Snowflake detections carry job-opening evidence, drawn from a Technology Detections dataset of 1.9B+ detections across 97M+ websites.
What This Snowflake Adoption Data Measures, and What It Does Not
Snowflake adoption, in this report, is the number of companies where PredictLeads Technology Detections found public evidence that the company uses or has recently used Snowflake. As of the September 2026 snapshot, 39,525 companies have been detected with Snowflake evidence at some point, and 13,188 of them (33.4%) are active, meaning PredictLeads confirmed fresh evidence within the trailing 60 days.
The other 26,337 have not been re-detected in 60 days. That is “no longer detected,” not churn: most of the gap is companies the crawler has not revisited yet, and a missing detection can also come from a rewritten job ad, a recrawl gap, or a signature change.
What this data is not
- Not Snowflake’s customer count. PredictLeads observes company websites and job postings, not contracts. Snowflake publishes its own metrics each quarter on its investor relations site, and this report does not try to reconcile the two.
- Not a market-share study. Relative comparisons among platforms detected the same way are more defensible than any single platform’s absolute count.
- Not a stock call. Nothing here is an investment recommendation.
Every detection carries first_seen_at and last_seen_at timestamps, which is what lets you separate a new adopter from a long-tenured one and date a possible switch.
Snowflake Adoption Trends Since 2018: A Ramp, a Plateau, and a Surge
Snowflake adoption in PredictLeads data moved through an early ramp, a steady scale-up, a multi-year plateau, and a sharp acceleration that began in 2025. New companies detected per year grew from 4,546 in 2022 to 10,827 in 2025, and 2026 had already reached 9,593 by late September.
The four growth phases
- 2018 to 2019, early ramp: quarterly new detections climbed from single or low double digits to 100 to 300.
- 2020 to 2022, steady scaling: quarterly detections rose from the 300s to a first peak of 1,353 in Q2 2022.
- Q3 2022 to Q4 2024, plateau: ten consecutive quarters in a narrow band of 877 to 1,353 new detections, the flattest stretch in Snowflake’s detected history.
- 2025 to 2026, second acceleration: quarterly detections roughly tripled, from about 1,100 in Q4 2024 to 3,962 in Q4 2025, the highest quarter on record, and held above 3,100 per quarter through Q3 2026.
Why the 2025 to 2026 surge needs a caveat
The direction of the 2025 acceleration is real, but its size is confounded by PredictLeads’ own crawl expansion. New website records added per quarter went from roughly 1.3 to 5 million in 2023 and 2024 to 6 to 18 million in 2025 and 2026, in uneven bursts that do not match organic company formation. A control technology with no link to data platforms (jQuery) shows the same volatility, and every competing data platform shows a similar-shaped jump over the same window.
So read absolute counts after mid-2025 as an upper bound on real adoption growth. Share-of-cohort comparisons hold up better, because the same crawl expansion inflates every technology’s count and the shared denominator together. The next section leans on them.
Snowflake vs. Databricks, BigQuery, and Redshift: Competitive Momentum
On trailing-12-month growth in new detections, Snowflake, Databricks, and Google BigQuery are statistically tied, and all three are growing well ahead of Amazon Redshift, Azure Synapse Analytics, and the Microsoft Azure data stack. PredictLeads data does not support a claim that Snowflake is pulling away from either Databricks or BigQuery.
| Technology | Active detections (last 60 days) | New detections, trailing 12 months | YoY change |
|---|---|---|---|
| Snowflake | 13,188 | 13,635 | +72.0% |
| Databricks | 12,998 | 12,927 | +71.7% |
| Google BigQuery | 9,354 | 11,565 | +71.1% |
| Amazon Athena | 2,574 | 3,257 | +52.1% |
| Amazon Redshift | 5,121 | 6,439 | +48.1% |
| Oracle Database | 3,600 | 3,761 | +47.0% |
| Azure Databricks | 2,133 | 2,645 | +46.5% |
| Azure Synapse Analytics | 2,371 | 3,369 | +37.9% |
| Teradata Analytics Platform | 1,147 | 1,124 | +33.8% |
| SAP HANA | 4,500 | 4,737 | +29.3% |
| Microsoft Azure SQL Database | 1,127 | 1,673 | +13.0% |
| PostgreSQL (general-purpose, reference) | 33,245 | 35,623 | +74.5% |
| Microsoft SQL Server (general-purpose, reference) | 25,098 | 22,504 | +30.9% |
Two reference rows sit outside the headline comparison. PostgreSQL (+74.5%) is growing slightly faster than Snowflake and Microsoft SQL Server (+30.9%) at less than half its rate, but both are general-purpose databases, so they are tracked for context rather than treated as like-for-like warehouse rivals. The slower growth at Redshift, Synapse, SAP HANA, and Azure SQL Database can be read as legacy-cloud and Microsoft-first environments modernizing more slowly than the Snowflake, Databricks, and BigQuery cohort.
Share of new detections is the more reliable read
Share of new detections is the metric least distorted by crawl expansion, and on that measure Snowflake gained modest ground while Databricks gained the most. Within the close-competitor cohort, Snowflake held 21.6% of new detections in Q1 2021 (736 of 3,405) and 24.9% in Q1 2026 (3,118 of 12,534). Databricks rose from 14.4% to 23.5% over the same window, largely at the expense of Amazon Redshift, which slipped from 14.6% to 12.0%.
The Databricks-gaining-at-Redshift’s-expense pattern held across several cohort definitions tested during the analysis, even though the exact percentages shift with the denominator. To reproduce a share series, pull detections for each technology through the Technology Detections API and bucket first_seen_at by quarter.
Who Is Switching To and From Snowflake
PredictLeads found 484 review-confirmed technology switch signals at Snowflake-detected companies: 389 into Snowflake and 95 out of it. A switch signal is stricter than co-detection, because it requires evidence that one technology went quiet while a competing one took hold.
How a switch signal is confirmed
PredictLeads’ switch detection proposed 508 candidate switches in this pool, and an automated, LLM-assisted review approved 484 of them (95.3%). It rejected 24 as same-vendor renames, tools used side by side, or otherwise not a real competitive switch. Even a confirmed signal is evidence from public sources, not a contract record.
The migration matrix
| Technology | Switch signals into Snowflake (from this tool) | Switch signals out of Snowflake (to this tool) |
|---|---|---|
| Microsoft SQL Server | 108 | 6 |
| Microsoft Access | 85 | 2 |
| PostgreSQL | 59 | 27 |
| SAP HANA | 30 | 5 |
| Oracle Database | 28 | 4 |
| Amazon Redshift | 24 | 1 |
| Google BigQuery | 11 | 3 |
| Databricks | 9 | 20 |
| Amazon Athena | 8 | 6 |
| Teradata Analytics Platform | 7 | 4 |
| Azure Synapse Analytics | 6 | 6 |
| Azure Databricks | 6 | 9 |
| Microsoft Azure SQL Database | 4 | 1 |
| Exasol | 2 | 0 |
| QuestDB | 1 | 0 |
| IBM DB2 | 1 | 0 |
| Oracle Autonomous Data Warehouse | 0 | 1 |
| Total | 389 | 95 |
Source: PredictLeads review-confirmed technology switch signals, snapshot September 2026. Bold marks the largest flow in each direction (SAP HANA leads close-competitor wins; Databricks and Azure Databricks lead close-competitor losses).
The headline ratio vs. the close-competitor ratio
The all-technology headline of 389 to 95 (net +294, roughly 80/20) overstates the margin somewhat. Restricted to close cloud-warehouse and lakehouse competitors, the analysis counts 104 switches into Snowflake and 54 out of it: net +50, still roughly 2 to 1. General-purpose operational databases (Microsoft SQL Server, Microsoft Access, Oracle Database, and PostgreSQL) account for most of the remaining all-technology total, a modernization pattern, but the close-competitor split shows Snowflake also winning warehouse-for-warehouse contests outright, not only modernizing legacy databases.
Databricks is the main destination when companies leave
Databricks and Azure Databricks account for 29 of Snowflake’s 54 close-competitor losses (54%), far more than any other rival, but only 15 of its 104 close-competitor wins. The bigger surprise is on the win side: SAP HANA is the single largest source of close-competitor wins into Snowflake, at 30 of 104 (29%), ahead of Databricks and Azure Databricks combined. Read together, Databricks is the rival Snowflake loses to most often, while SAP HANA migrations are the rival Snowflake wins from most often, a pattern closer to legacy-enterprise-database displacement than a head-to-head lakehouse contest. PostgreSQL is the largest single destination overall (27 signals), but it sits in an adjacent-infrastructure tier, so those cases more likely reflect workload-level moves than full warehouse replacements.
Timing skews recent in both directions: 292 of the 389 inbound signals (75%) and 79 of the 95 outbound signals (83%) complete in 2025 or 2026. Because the detection needs roughly a year of incumbent evidence plus a 60-day confirmation gap, its output lags, and the 2026 count is very likely still growing.
Who switches
Switchers skew large and IT-intensive on both sides. Of 341 sized companies with a switch into Snowflake, 65 (19%) have 10,001+ employees and 96 (28%) have 1,001 to 5,000, versus only 3 with 11 to 50. Leading industries are Software Development (31), IT Services and IT Consulting (25), Financial Services (25), Hospitals and Health Care (19), and Banking (13). Outbound switchers look similar (17 of 87 sized companies at 10,001+), which points to sophisticated buyers evaluating platforms rather than one segment prone to leaving. For the account-level version of this pattern, see our guide to finding companies likely to switch platforms.
Public-Company Switch Examples, Including a Loss
Cross-referencing switch signals against companies with a stock ticker surfaces named public companies with switch-level evidence, not just co-detection. Five representative cases:
| Company | Ticker | Switch signal | What the evidence shows |
|---|---|---|---|
| Delta Air Lines | NYSE:DAL | Microsoft SQL Server to Snowflake | SQL Server evidence last detected July 11, 2025; Snowflake first detected December 21, 2025. A textbook legacy-database-to-cloud-warehouse pattern. |
| Varonis Systems | NASDAQ:VRNS | Competing technology to Snowflake | Data-security software vendor; consistent with an internal analytics modernization. |
| O’Reilly Auto Parts | NASDAQ:ORLY | Competing technology to Snowflake | Large traditional retailer; consistent with retail data-platform modernization. |
| ZipRecruiter | NYSE:ZIP | Competing technology to Snowflake | Consumer-internet company in the recruiting space. |
| Robinhood Markets | NASDAQ:HOOD | Snowflake to PostgreSQL | A loss case, included deliberately: evidence of a workload moving off Snowflake. |
Robinhood is included so the examples are not all Snowflake-favorable. Its destination, PostgreSQL, sits in the adjacent tier, so the case is best read as a workload-level move, such as an operational or application database use case, rather than a full data-warehouse displacement. It is still a review-confirmed signal away from Snowflake at a large public company.
Treat all five as medium-confidence signals worth a closer look, not independently verified events: they passed PredictLeads’ switch logic and automated review, but were not re-checked line by line against the underlying job-posting text. Public-company coverage is also a floor: 3,246 of the 39,525 Snowflake detections (8.2%) carry a ticker, and some listed companies lack one in the data.
The Stack Around Snowflake: dbt, Multi-Cloud, and a Snowpark Blind Spot
dbt is the most Snowflake-associated tool in the modern data stack: 73.6% of all dbt detections (15,557 of 21,137) co-occur with Snowflake, and 39.4% of Snowflake-detected companies also show dbt. That overlap fits dbt’s long-standing Snowflake adapter and its role in the Snowflake ecosystem.
| Technology | Share of Snowflake-detected companies also showing it |
|---|---|
| Amazon Web Services | 92.0% |
| Microsoft Azure | 84.4% |
| Google Cloud Platform | 66.1% |
| Databricks | 49.6% |
| Apache Airflow | 44.4% |
| dbt | 39.4% (and 73.6% of all dbt detections involve Snowflake) |
| OpenAI | 39.0% |
| Fivetran | 14.8% |
| Matillion | 6.1% |
| Snowpark | 0 detections (likely a coverage gap) |
Three more patterns stand out. Snowflake-detected companies show heavy multi-cloud evidence, with AWS, Azure, and Google Cloud all appearing at high rates at once. Databricks is co-detected at 49.6% of Snowflake companies, so coexistence is common and should never be read as switching. And Snowpark, Snowflake’s native Python and ML environment, shows zero detections: very likely a blind spot, since Snowpark work happens inside Snowflake and rarely appears as a named skill in job ads, not evidence of zero adoption.
Managed ETL tools also show up less than expected, which could reflect a smaller footprint or a similar naming gap in job postings. For how the modern data stack layers onto companies as they grow, see the average tech stack by funding stage.
Where Snowflake Adoption Is Accelerating: India, Japan, and Brazil
India, Japan, and Brazil show the sharpest inflections in new Snowflake detections of any market studied. India rose from 185 new detections in 2024 to 682 in 2025 (+269%), Brazil from 54 to 275 (+409%), and Japan from 7 to 134 (+1,814%, off a small base), while the United States, the United Kingdom, Canada, and Australia grew roughly 2x.
| Market | 2024 | 2025 | 2026 (partial) | 2024 to 2025 |
|---|---|---|---|---|
| United States | 2,167 | 4,666 | 4,071 | +115% |
| United Kingdom | 344 | 625 | 581 | +82% |
| France | 167 | 383 | 309 | +129% |
| Canada | 184 | 334 | 313 | +82% |
| Germany | 151 | 333 | 280 | +121% |
| Australia | 138 | 277 | 236 | +101% |
| Netherlands | 97 | 206 | 158 | +112% |
| India | 185 | 682 | 580 | +269% |
| Brazil | 54 | 275 | 266 | +409% |
| Japan | 7 | 134 | 118 | +1,814% |
New companies detected with Snowflake evidence, by headquarters country. Source: PredictLeads Technology Detections, September 2026.
The United States remains by far the largest market in absolute terms. If crawl expansion were the only driver, it should lift every geography roughly in proportion, so faster relative growth outside the U.S. is notable. It is not yet validated, though: the India and Japan jumps could reflect a regional data-source addition, and a follow-up restricted to those countries’ detection sources is the next check.
A Negative Finding: Snowflake’s AI-Intensity Edge Has Nearly Closed
Snowflake-detected companies are not becoming more AI-intensive than a comparable Redshift cohort; the gap between the two has nearly closed. Among companies first detected with Snowflake in 2017, 95.2% showed at least one AI or ML-category technology, versus 53.6% of companies detected on Amazon Redshift without Snowflake. By 2026, the figures were 85.4% and 85.0%.
| Year first detected | Snowflake cohort with 1+ AI/ML tech | Redshift-only cohort | Gap (points) |
|---|---|---|---|
| 2017 (n=21) | 95.2% | 53.6% | 41.6 |
| 2019 | 89.5% | 77.2% | 12.3 |
| 2021 | 89.1% | 79.9% | 9.2 |
| 2023 | 89.2% | 82.7% | 6.5 |
| 2025 | 87.1% | 86.9% | 0.2 |
| 2026 | 85.4% | 85.0% | 0.4 |
The hypothesis that Snowflake is capturing AI-forward enterprises faster than its rivals does not survive this test. Broad-based enterprise adoption of AI and analytics tools, affecting Snowflake and Redshift companies alike, is the better explanation.
Two caveats limit how much weight the result can bear. The AI category is broad, covering 4,667 technologies across Advanced Analytics and Data Science, Machine Learning, and Generative AI, so it measures general data and analytics maturity more than LLM adoption. And the comparison cohort is itself a cloud-warehouse population, not a neutral baseline. Confidence is medium; a narrower Generative AI-only cut is the natural next test.
The Unexpected Finding: Snowflake Is Detected Through Hiring
PredictLeads detects Snowflake almost entirely through job postings: 37,614 of 39,525 Snowflake detections (95.2%) carry job-opening evidence, and 24,102 (61.0%) rely on job-opening evidence alone. Website subpages contribute evidence on 24 detections (0.06%), technology reviews on 222 (0.6%), and DNS records and connections on none.
Where PredictLeads finds Snowflake evidence (share of 39,525 detections)
A detection can carry more than one source. Source: PredictLeads Technology Detections, September 2026.
That is structural, not a data-quality problem. A backend data platform has no client-side footprint, so without hiring evidence, 61.0% of Snowflake-detected companies in this dataset would be invisible. It is the clearest case for detection beyond website code, covered in more depth in how job openings data improves tech stack accuracy.
Hiring is the detection channel, not a leading indicator
A direct test of the “hiring predicts adoption” idea came back flat. Across a sample of 1,997 job-sourced Snowflake detections, the median gap between the detection’s first-seen date and the earliest linked job posting was 0 days, and only 0.5% showed job evidence arriving first. The reason is mechanical: the first-seen date is set from the earliest evidence PredictLeads has, and for most Snowflake detections that evidence is a job posting.
Two implications follow. The detected population leans toward companies that publicly hire for Snowflake skills, so a company working through existing staff or a systems integrator can be missed or detected late. And because Databricks, Redshift, and BigQuery likely share the same dependence, relative comparisons among them are more trustworthy than any absolute count. A source-mix breakdown for each competitor has not been run yet and would confirm or undercut that assumption. For the prospecting angle, see companies hiring data engineers while adopting new data tools.
Methodology and Limitations
Every figure comes from a weekly copy of PredictLeads production data, current to late September 2026, with the analysis focused on 2018 to 2026 because earlier detections are sparse. The unit of observation is a company website record, not a legal entity, and about 13% of Snowflake-detected websites have no industry on file. For how each detection source works, see how technographic data is collected.
- Right-censoring: “no longer detected” counts spike in the most recent quarters (4,117 in Q2 2026 alone) because recent detections have not had 60 days to qualify either way. Quarters older than six months are more trustworthy.
- Switch coverage: switch detection covers only higher-quality company records (10,604 of 39,525 Snowflake detections, 26.8%), so switch counts are a high-confidence sample among larger companies, not a census.
- Competitor mapping: the 24 Snowflake competitor pairs are LLM-assisted and not individually human-reviewed; the tiers used here are an analyst judgment that could shift some percentages.
- Crawl expansion: absolute counts after mid-2025 were not crawl-adjusted, as covered above.
- Industry anomaly: Staffing and Recruiting shows a one-year spike (28, then 190, then 108 new detections from 2023 to 2025), flagged as a probable detection artifact rather than a finding.
- No reconciliation: no figure is reconciled against Snowflake’s reported financials or customer counts.
How GTM Teams and Analysts Can Use Switch Signals
The data behind this report works at account level: pull every company with evidence of a technology, sort by first-seen date, and flag accounts where an incumbent is no longer detected.
- Pull the detection list. Query
GET /discover/technologies/{technology_id_or_fuzzy_name}/technology_detectionsfor Snowflake and each competitor, or start with our guide to finding every company using a technology by name. - Separate new from tenured. Use
first_seen_atto isolate recent adopters, and treat alast_seen_atolder than 60 days as “no longer detected,” not a cancellation. - Corroborate with hiring. Migration language in data-engineering postings is stronger supporting evidence of a possible transition, not confirmation.
- Route by audience. Teams selling into the Snowflake ecosystem (dbt consultancies, BI vendors, data-engineering services) can prioritize fresh adopters, while analysts can track share of new detections each quarter.
For the full playbooks, see cloud data warehouse migration signals and technographic data for competitor displacement.
How PredictLeads Delivers Technology Detection and Switch Data
PredictLeads is a B2B company intelligence and technographic data provider, not a platform: it turns public company activity into structured, source-backed datasets that other tools and teams build on. Every number in this report comes from Technology Detections, which hold 1.9B+ detections across 97M+ websites and 220,000+ tracked technologies. Detections draw on five public sources (website script tags, DNS records, IP ranges, cookies, and job descriptions) plus website subpages, and the behind_firewall field marks detections found outside website code, which is how a backend platform like Snowflake becomes visible at all.
Each record carries first_seen_at, last_seen_at, source_count, and fields such as seen_on_job_openings and competitive_technology_detections. Pair them with Job Openings (279.2M+ records since 2018 across 2.9M+ websites), News Events (10M+ since 2016 in 37 categories), and Similar Companies (18.9M+ companies) to corroborate a switch or expand a list. For how PredictLeads compares with other vendors, see our guide to the best technographic data providers.
Delivery comes by API, flat files, webhooks, and MCP (see the MCP integration docs). Flat files load directly into warehouses such as Snowflake or BigQuery for analysis like this one. All data comes from public sources only, and PredictLeads is SOC 2 Type II certified and GDPR and CCPA compliant.
Final Thoughts on Snowflake Adoption Trends
The honest read of Snowflake adoption trends is more useful than the flattering one. Snowflake is growing fast and gaining modest share, but it is holding pace with Databricks and BigQuery rather than pulling away. Its net migration gains lean on modernizing legacy databases and on displacing SAP HANA in the close-competitor tier, while Databricks is the rival that wins most often when companies leave. The methodology lesson travels further: for backend platforms, hiring evidence is the detection channel, so any technographic read on data infrastructure is only as good as its job-posting coverage.
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Frequently Asked Questions
Is Snowflake adoption still growing in 2026?
Yes, in PredictLeads data. Trailing-12-month new Snowflake detections are up 72.0% year over year (13,635 vs. 7,929), and 13,188 companies show active Snowflake evidence within the last 60 days. Part of the 2025 to 2026 jump reflects PredictLeads’ own crawl expansion, which lifted every data platform tracked, so share of new detections is the more reliable read. On that measure, Snowflake rose from 21.6% of close-competitor new detections in Q1 2021 to 24.9% in Q1 2026.
Are more companies switching to Snowflake or away from it?
More are switching to it. PredictLeads counts 389 review-confirmed switch signals into Snowflake and 95 out of it, roughly 4 to 1. Against close cloud-warehouse competitors only, the split is 104 in and 54 out, roughly 2 to 1. General-purpose databases account for most of the all-technology total, led by Microsoft SQL Server (108 signals) and Microsoft Access (85), but Snowflake is also winning close-competitor contests outright.
Where do companies go when they leave Snowflake?
Databricks is the most common close-competitor destination. Databricks and Azure Databricks account for 29 of Snowflake’s 54 close-competitor losses (54%). PostgreSQL is the largest single destination overall with 27 signals, but it sits in an adjacent tier, so those cases more likely reflect workload-level moves. Each signal requires the incumbent to go undetected for 60+ days while the challenger persists.
How does PredictLeads detect that a company uses Snowflake?
Mostly through job postings. Of 39,525 Snowflake detections, 95.2% carry job-opening evidence and 61.0% rely on job openings alone, because a backend platform leaves almost no trace in website code. PredictLeads Technology Detections mark this with the behind_firewall field and record first_seen_at and last_seen_at for every detection. A detection is evidence that a company uses or has recently used Snowflake, not confirmation of a live deployment.
Can investors use technographic data to track Snowflake vs. Databricks?
It can serve as an independent, company-level read, but not as a substitute for reported results or as investment advice. Detection data shows which companies adopt, switch, or are no longer detected, with timestamps on every record and without relying on vendor disclosures. Relative measures are more reliable than absolute counts: Databricks rose from 14.4% to 23.5% of close-competitor new detections between Q1 2021 and Q1 2026, while Snowflake rose from 21.6% to 24.9%. PredictLeads delivers this data by API, flat files, webhooks, and MCP.