How They Do ItBETA

COMPANY PLAYBOOK

Elastic

Elastic carries the distributed architecture of its product into the organization, prioritizing flexibility, open-source thinking, context sensitivity and progress over perfection.
Operating system
  1. Distributed by intention
  2. trust and flexible time
  3. context-dependent judgment
  4. progress-led iteration
  5. open community feedback.

System principles

  • Flex work around life.
  • Preserve context sensitivity—the answer may be “it depends.”
  • Choose progress over simplistic perfection.

Tensions and trade-offs

  • Flexibility includes different lives but increases shared-time and coordination cost.
  • Contextual judgment reduces dogma but can weaken standards and predictability.

PRACTICES

How Elastic builds its operating system

01
Flexibility

Home, Dinner

The workday can flex around personal life and care responsibilities.

How it works
People can flex the workday around care, family and personal needs.
Why it is used
Treats people as more than a job role and supports sustainable contribution.
Trade-off
Shared hours and response expectations become harder to manage.
Best fit
Outcome-oriented teams with strong async coordination.
Open original source
02
Product

Progress, SIMPLE Perfection

Movement and continuous improvement are preferred over perfection.

How it works
Movement, feedback and continuous improvement take precedence over a perfect first release.
Why it is used
Increases learning speed under uncertainty.
Trade-off
Can create quality debt and a sense of perpetual incompleteness.
Best fit
Teams with reversible decisions and strong observability.
Open original source
03
Inclusion

As YOU, Are

People are expected to be themselves rather than fit an arbitrary mold.

How it works
People are expected to contribute with their identity and perspective rather than fit an arbitrary professional mold.
Why it is used
Strengthens diverse perspective and belonging.
Trade-off
If inclusion remains rhetorical, trust damage becomes greater.
Best fit
Organizations whose leadership behavior and processes align with inclusion.
Open original source
04
Remote

Distributed by intention

Async, timezone-friendly work is designed intentionally.

How it works
Team and communication norms are intentionally designed for multiple time zones and cultures.
Why it is used
Enables location-independent talent access and diverse market perspective.
Trade-off
Can create decision latency, documentation load and belonging challenges.
Best fit
Teams serving global customers and open-source communities.
Open original source
05
Hiring

Candidate AI limits: allowed in preparation, banned in interviews

Elastic’s guide dated 16 July 2026 gives candidates an explicit list: AI is fine for polishing a résumé, structuring thoughts and practising likely questions; inventing experience and using AI in live interviews is not. Candidates who prepared with AI must be able to explain their approach and reasoning themselves.

How it works
A candidate-facing allowed/prohibited list is shared up front; live conversations stay human-to-human, and deliberate misrepresentation can lead to disqualification or, after hiring, termination.
Why it is used
Makes the boundary visible instead of banning AI outright, and protects the interview as a signal of the candidate’s own judgment.
Trade-off
The policy covers the candidate side only; how interviewers score and which monitoring tools are used are not described. Enforcement depends on interviewer training.
Best fit
Teams running remote interviews that want to frame rather than ban candidate AI use.
Open original source

Primary sources

Elastic Source CodeSource Code
Source details
Company publication: YesAccessed: 2026-09-05Visible update: Not displayedLicence: Not stated by publisherArchive: Public URL; archive feasibility not separately assessed
Distributed by DesignSource Code
Source details
Company publication: YesAccessed: 2026-09-05Visible update: Not displayedLicence: Not stated by publisherArchive: Public URL; archive feasibility not separately assessed
Elastic AI in the interview processCompany-published culture blog post
Source details
Company publication: YesAccessed: 2026-09-27Visible update: 2026-07-16 (published date shown)Licence: No reuse licence displayed; treat page prose as copyrighted/all rights reserved.Archive: Public HTML URL; eligible for an external web snapshot (no snapshot is claimed).
Our Source CodeCompany-published culture page
Source details
Company publication: YesAccessed: 2026-09-28Visible update: Not displayedLicence: No licence stated in the reviewed source.Archive: Public HTML URL; eligible for an external web snapshot (no snapshot is claimed).