Request the 55 query / search seats
MongoDB Query Optimization 24/16 plus Elastic Core Search 31/6. Security-ops is out of the slice.
How the query seat classifies: Query Optimization in, Core Search in, security-ops out
How it is built
This page reads two job families first, not which bench is thicker. MongoDB 24 / 16 and Elastic 31 / 6 add to 55 / 22.
How titles classify
In: Query Optimization and Core Search. Security-ops, Glean, Datadog, and SIEM stay off this page.
Who compares to whom
MongoDB: 22 of 24 are US, New York 7. Elastic: only 12 of 31 are US.
Outside this slice
Elastic's 31 is Core Search, not security-ops. Company-wide MongoDB or Elastic headcount is not on this page.
Two job families first, not which bench is thicker
As of August 26, 2026, 6:27 a.m. PT (8/26), MongoDB maps 24 current query / search seats against Elastic 31, with 22 open jobs on that slice.
MongoDB maps 24 current query / search seats. This page reads the function seat at two database names.
Elastic maps 31 current. The two add 24 + 31 = 55.
Headline open jobs are 22. MongoDB maps 16 jobs; Elastic maps 6. This is a talent-flow contrast.
New York 7, United States 22 of 24, and jobs 16 versus 6 come in later sections. The citeable pair is 24 versus 31.
MongoDB query seats: 24 current, 16 jobs
Elastic maps 31 current query / search seats. The people side is Core Search and Search Foundations, not regional sales or consulting architecture.
Elastic's 31 are Core Search, not security-ops
22 of 24 MongoDB current seats are in the United States. Elastic's 31 current seats are spread, with 12 in the United States.
MongoDB: 22 of 24 US, New York 7
The current bench is thicker at Elastic; the job book is thicker at MongoDB: 16 versus 6.
| Company | Mapped current | Mapped open jobs | Read |
|---|---|---|---|
| MongoDB | 24 | 16 | headline · thicker job book |
| Elastic | 31 | 6 | headline · thicker current bench |
| Headline sum | 55 | 22 | these two database names only |
Request the 55 query / search seats
The same methodology exports these two families. 55 / 22 adds the two families, not company-wide search headcount.
Elastic: only 12 of 31 are US
Elastic's 31 current seats are spread, with 12 in the United States. Named cities: Raleigh 2, Sydney 2, Toronto 2.
55 / 22 is two families added
Function slice
Title tokens: query or search. Seat-family samples (no names published): MongoDB current Lead Engineer, Query / Query Optimization Lead / Product Manager, Query; jobs Staff Engineer, Query Optimization / Senior Staff Product Manager, Query / Senior Software Engineer, Query Execution. Elastic current Search Architect / Product Manager Search Foundations / Software Developer - Core Search. Atlas Search and ES|QL were not used as sole IN tokens. SIEM, XDR, threat, cyber, Glean, APM, observability, GTM, and sales were not used. Company-wide MongoDB or Elastic was not used.
Current and open jobs
The figures are profiles Metix maps as currently employed at that company name, and visible open jobs mapped to that name, not official headcount.
Names
MongoDB is the mongodb.com New York company record (10818204). Counts are id-only on that record; listed-name totals are not published as headcount. A bare MongoDB name collides. Elastic is elastic.co, San Francisco (6183507), id-only. A bare Elastic name collides.
As-of and source
As of August 26, 2026, 6:27 a.m. PT (8/26). Source: Metix AI. This page does not invent company-wide totals. This page sits in the series.
Questions this report answers
- What is the MongoDB 24-versus-Elastic 31 query / search seat?
- As of August 26, 2026, 6:27 a.m. PT, MongoDB maps 24 current query / search seats against Elastic 31, with 22 open jobs on that slice. These are Metix-mapped function-slice counts, not official headcount. Source: Metix AI.
- What does 55 current / 22 open jobs add?
- 55 current / 22 jobs adds MongoDB 24 / 16 and Elastic 31 / 6 only. The slice is query or search as title tokens: query execution, search engineer, search relevance, query optimizer, query engine. Atlas Search and ES|QL were not used as sole IN tokens. SIEM, XDR, threat, cyber, security unless clearly a query or search engine, Glean, APM, observability, GTM, and sales are out. Company-wide MongoDB or Elastic is out. This is not a remake of a product-name cut.
- Why is MongoDB 22 of 24 US while Elastic is 12 of 31 US?
- MongoDB mapped current: 22 of 24 US, Germany 1, Ireland 1. Named city: New York 7; the rest of the city layer is thin. Jobs 16: United States 5, Canada 8, Ireland 3. Elastic mapped current: 12 of 31 US, Canada 3, Spain 3, Australia 2, Germany 2, Netherlands 2, United Kingdom 2, France 1, Greece 1, Israel 1. Named cities are thin: Raleigh 2, Sydney 2, Toronto 2. Jobs 6: United States 3, India 2, Brazil 1. Official hiring pages: MongoDB https://www.mongodb.com/company/careers and https://job-boards.greenhouse.io/mongodb , Elastic https://www.elastic.co/careers and https://jobs.elastic.co/.
- How should the MongoDB 24 query / search map be cited?
- Metix AI Talent Intelligence, 2026-08-26. Mongo 24 vs Elastic 31 | Query / Search Seats | Metix AI. https://metix.ai/reports/series/mongodb-elastic-query-search-2026
Request the 55 query / search seats
The same methodology can export mapped current profiles and mapped open jobs on the query / search seat at MongoDB and Elastic, or rerun a custom map on your target companies. The list form is the primary path.
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