OpenTelemetry Adoption & Readiness Assessment
Measures developer familiarity, adoption stage, blockers, and rollout priorities for OpenTelemetry across engineering teams to inform instrumentation strategy and resource planning.
샘플 질문
템플릿에 포함된 내용을 미리 확인해 보세요. 모든 질문은 설문 공개 전에 자유롭게 수정할 수 있습니다.
Which best describes your primary role?
- Backend developer
- Frontend developer
- Full-stack developer
- Site Reliability Engineer / DevOps
- Data/ML engineer
- QA/Testing engineer
- Software architect / Tech lead
- Platform engineer
- Other (please specify)
Which observability tools have you used in the last 6 months? (Select all that apply.)
- Prometheus
- Grafana
- Jaeger
- OpenTelemetry SDK
- OpenTelemetry Collector
- Elastic APM
- Datadog
- New Relic
- Splunk Observability
- AWS X-Ray
- Azure Monitor
- Google Cloud Operations Suite
- None of the above
- Other (please specify)
Which best describes your organization's current OpenTelemetry adoption stage?
- Not considering
- Evaluating
- Piloting in one or a few services
- In limited production
- Broad production across services
What are the biggest blockers to adopting or expanding OpenTelemetry in your organization? (Select up to 5.)
- Limited time or competing priorities
- Unclear ROI / benefits
- Learning curve or lack of expertise
- Language or framework gaps
- Lack of organizational buy-in
- Tooling or integration maturity
- Data volume or storage cost concerns
- Performance overhead concerns
- Security/PII/governance concerns
- We're satisfied with current vendor tooling
- No need identified yet
- Other (please specify)
Which areas are your next targets for OpenTelemetry instrumentation in the next 6 months? (Select all that apply.)
- Java services
- Node.js services
- Python services
- Go services
- .NET services
- Mobile apps (iOS/Android)
- Browser RUM
- Databases
- Message brokers/streaming (e.g., Kafka)
- Serverless functions
- Batch/ETL workloads
- Other (please specify)
We'd like to understand more about your experience with observability and OpenTelemetry. An AI moderator will ask a few brief follow-up questions based on your earlier responses.
How many years of professional software development experience do you have?
- 0–1
- 2–4
- 5–9
- 10–14
- 15+
- Prefer not to say
Thank you for completing this survey! Your responses are anonymous and will be used in aggregate to identify adoption patterns and prioritize improvements to the OpenTelemetry ecosystem.
What is your primary programming language for services you work on today?
- Java
- JavaScript/TypeScript
- Python
- Go
- C#/.NET
- C/C++
- Ruby
- PHP
- Rust
- Other (please specify)
- Prefer not to say
How familiar are you with OpenTelemetry concepts and components?
Approximately what percentage of your production services are currently instrumented with OpenTelemetry?
- 0%
- 1–10%
- 11–25%
- 26–50%
- 51–75%
- 76–99%
- 100%
- Not sure
Which of the following support resources would be most helpful for your OpenTelemetry adoption? (Select up to 3.)
- Official documentation improvements
- Language-specific getting-started guides
- Reference architectures and deployment patterns
- Hands-on workshops or training sessions
- Internal champions / OTel working group
- Vendor-neutral migration tooling
- Community forums or Slack channels
- Consulting or professional services
- Other (please specify)
How likely is your team or organization to adopt or expand OpenTelemetry usage in the next 6 months?
Based on your responses in this survey, if you could change one thing about OpenTelemetry or its ecosystem, what would it be?
Where are you primarily based?
- North America
- Europe
- Asia-Pacific
- Latin America
- Middle East
- Africa
- Other (please specify)
- Prefer not to say
Where do your production workloads run today? (Select all that apply.)
- Kubernetes
- Serverless (e.g., AWS Lambda, Azure Functions)
- Containers without an orchestrator
- Virtual machines (VMs)
- Bare metal
- PaaS (e.g., Heroku, App Engine)
- On-premises data center
- Hybrid or multi-cloud
Which OpenTelemetry components or capabilities are you using today, if any? (Select all that apply.)
- OTel SDKs (language libraries)
- OTel Collector (any deployment)
- OTLP export protocol
- Auto-instrumentation
- Manual instrumentation
- Semantic conventions
- Sampling configuration (e.g., tail-based)
- Not using OTel yet
Rank the following outcomes by how important they are to your organization's OpenTelemetry goals (most important first).
- Faster incident detection and response
- Better root-cause analysis
- Cross-service traceability
- Standardized telemetry across teams
- Vendor portability / avoiding lock-in
- Cost control and optimization
- Improved application performance
- Security/compliance visibility
Approximately how many employees are in your organization?
- 1–10
- 11–50
- 51–200
- 201–1,000
- 1,001–5,000
- 5,001–10,000
- 10,001+
- Prefer not to say
포함된 기능
AI 후속 질문
정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.
주의력 확인 장치
성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.
AI가 작성한 문안
문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.
자동 리포트
응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.
다른 서비스와 비교
다른 설문 도구의 가장 유사한 템플릿을 검토했습니다. 그 도구들이 잘하는 점과, 이 템플릿이 한발 더 나아가는 지점을 정리했습니다.
이 템플릿을 선택하는 이유
- Includes a dedicated AI follow-up interview segment that adaptively probes each respondent's actual observability and OpenTelemetry experience, rather than relying only on fixed-choice questions
- Combines quantitative signals (opinion-scale familiarity and likelihood-to-adopt ratings, dropdown for percentage of services instrumented) with a ranking question to prioritize rollout outcomes and multiple-choice questions on blockers, support needs, and next instrumentation targets
- Closes with an open-text reflection question asking what respondents would change about OpenTelemetry, capturing qualitative detail a static form would miss
- Covers the full readiness picture in one flow — role, tech stack, current tools, adoption stage, blockers, and org demographics — feeding directly into instrumentation strategy and resource planning
SurveyMonkey
AI Readiness Assessment TemplateThis is a fielding-ready template but it assesses general organizational AI readiness, not OpenTelemetry or observability adoption specifically, so it would need substantial rewriting for this use case. It's useful as a structural reference for readiness-assessment question design.
잘하는 점
- Established survey platform with broad template library and easy distribution
- Likely includes standard readiness-assessment scaffolding (maturity stages, barriers) transferable across tech topics
아쉬운 점
- Static questionnaire with no adaptive AI follow-up interviewing to probe individual responses
- No voice AI interview or guided screen-share task option
- Not domain-specific to OpenTelemetry/observability tooling, adoption stages, or instrumentation targets
SurveySparrow
Information Security Risk Assessment QuestionnaireA ready-to-field questionnaire aimed at a technical/engineering audience, which makes it a reasonable structural comparison, but it targets security risk assessment rather than observability tooling adoption. Its conversational chat-style format is a known SurveySparrow strength.
잘하는 점
- Conversational, chat-like question flow that can feel more engaging than a plain form
- Ready-to-field questionnaire targeted at technical/engineering respondents
아쉬운 점
- No adaptive AI follow-up interview or automated per-response quality scoring
- No voice AI interview capability
- Topic is security risk, not OpenTelemetry adoption stage, blockers, or rollout prioritization
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