모든 템플릿

Developer Latency Sensitivity & SLO Benchmarking Survey

Measures developer-perceived latency thresholds, tail-latency tolerance, and performance trade-off priorities by use case. Use it to benchmark acceptable response times, set data-informed SLOs and SLAs, and prioritize performance investments that align with what developers actually care about.

샘플 질문

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질문 24개 · 약 11분
Q01
메시지

Welcome, and thank you for your interest in this survey on developer latency experiences. This survey takes approximately 11 minutes. Your participation is entirely voluntary, and you may stop at any time. There are no right or wrong answers — we are interested in your honest opinions and real-world experiences from the last 30 days. All responses are confidential, will be anonymized, and reported only in aggregate for internal research purposes.

Q02
객관식

Have you written, reviewed, or deployed code in a professional capacity in the last 30 days?

  • Yes
  • No
Q03
객관식

Which of the following languages or platforms did you actively use in the last 30 days? (Select all that apply)

  • JavaScript/Node.js
  • TypeScript
  • Python
  • Java
  • Go
  • Rust
  • .NET/C#
  • Ruby
  • Kotlin
  • Swift
  • C/C++
  • Other (please specify)
Q04
의견 척도

Overall, how sensitive to latency are your primary workloads?

척도: 17
최소:Not at all sensitive최대:Extremely sensitive
Q05
드롭다운

Over the last 30 days, what p95 latency have you typically observed for your primary endpoint?

  • < 50 ms
  • 50–100 ms
  • 100–250 ms
  • 250–500 ms
  • 500 ms – 1 s
  • 1–2 s
  • 2–5 s
  • > 5 s
  • I don't monitor this metric
Q06
의견 척도

If your median latency meets its target, how acceptable are occasional latency spikes?

척도: 17
최소:Completely unacceptable최대:Completely acceptable
Q07
순위 매기기

For a latency-sensitive workload, rank the following priorities from most to least important.

  1. Median latency (p50)
  2. Tail latency (p95/p99)
  3. Availability/reliability
  4. Cost efficiency
  5. Throughput
  6. Feature completeness
  7. Developer productivity
드래그하여 순위 지정
Q08
AI 인터뷰

We'd like to explore your latency trade-off decisions in a bit more depth. An AI moderator will ask you a couple of follow-up questions.

Q09
객관식

Which of the following best describes your current role?

  • Backend engineer
  • Frontend/web engineer
  • Full-stack engineer
  • Mobile engineer
  • ML/AI engineer
  • SRE/DevOps
  • Data engineer
  • Engineering manager
  • Other (please specify)
Q10
메시지

Thank you for completing this survey! Your responses will be used in aggregate to help set better latency benchmarks and improve developer tooling experiences. If you have questions, please contact the research team.

Q11
객관식

Which of the following use cases are most relevant to your current work? (Select all that apply)

  • User-facing web API
  • Interactive UI actions
  • Search/query
  • Payments/auth/checkout
  • Online ML inference
  • Batch ML/offline scoring
  • Streaming/real-time feeds
  • Data pipelines/ETL
  • Background jobs
  • Build/test/dev tooling
  • Other (please specify)
Q12
드롭다운

For user-facing requests, what do you consider an acceptable median (p50) latency?

  • < 20 ms
  • 20–50 ms
  • 50–100 ms
  • 100–200 ms
  • 200–500 ms
  • 500 ms – 1 s
  • > 1 s
Q13
드롭다운

What is your typical default timeout setting for external API or service calls?

  • < 500 ms
  • 500 ms – 1 s
  • 1–3 s
  • 3–5 s
  • 5–10 s
  • 10–30 s
  • > 30 s
  • No explicit timeout set
Q14
순위 매기기

When latency threatens your SLA or SLO, rank your top strategies in order of priority (drag to reorder).

  1. Degrade non-critical features
  2. Cache more aggressively
  3. Precompute or batch work
  4. Parallelize or partition requests
  5. Return partial results
  6. Scale up/out resources
  7. Fail fast with retry/backoff
드래그하여 순위 지정
Q15
드롭다운

In your experience, above what latency do interactive actions start to feel noticeably slow to users?

  • 100 ms
  • 200 ms
  • 300 ms
  • 500 ms
  • 800 ms
  • 1 s
  • > 1 s
Q16
장문형

Based on your responses in this survey, please share any additional thoughts about acceptable latency, tail behavior, or how latency considerations shape your system designs.

Q17
드롭다운

How many years of professional software development experience do you have?

  • < 1
  • 1–2
  • 3–5
  • 6–9
  • 10–14
  • 15+
Q18
드롭다운

For user-facing requests, what do you consider an acceptable 95th-percentile (p95) latency?

  • < 50 ms
  • 50–100 ms
  • 100–250 ms
  • 250–500 ms
  • 500 ms – 1 s
  • 1–2 s
  • > 2 s
Q19
드롭다운

What is the maximum acceptable end-to-end latency you would set for interactive UI actions (e.g., button clicks, navigation)?

  • < 100 ms
  • 100–200 ms
  • 200–500 ms
  • 500 ms – 1 s
  • 1–2 s
  • > 2 s
Q20
드롭다운

Approximately how large is your organization?

  • 1 (just me)
  • 2–10
  • 11–50
  • 51–200
  • 201–1,000
  • 1,001–5,000
  • > 5,000
Q21
의견 척도

How important is reducing tail latency (p95/p99) compared to reducing average latency for your workloads?

척도: 17
최소:Not at all important최대:Extremely important
Q22
드롭다운

What is the maximum acceptable end-to-end latency you would set for synchronous API calls (e.g., REST/gRPC)?

  • < 100 ms
  • 100–250 ms
  • 250–500 ms
  • 500 ms – 1 s
  • 1–3 s
  • > 3 s
Q23
드롭다운

In which region are you primarily located?

  • North America
  • Latin America
  • Europe
  • Middle East
  • Africa
  • Asia
  • Oceania
  • Prefer not to say
Q24
드롭다운

What is the maximum acceptable end-to-end latency you would set for batch or background jobs?

  • < 1 s
  • 1–5 s
  • 5–30 s
  • 30 s – 2 min
  • 2–10 min
  • > 10 min

포함된 기능

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차별화 포인트

  • Includes multiple dropdown questions that pin down concrete acceptable p50 and p95 latency thresholds by use case (interactive, synchronous, batch/background), producing data usable for real SLO/SLA setting rather than generic satisfaction scores
  • Uses ranking questions to force explicit trade-off prioritization between latency, cost, reliability, and other engineering priorities when latency threatens an SLA/SLO
  • Includes an adaptive AI follow-up interview segment specifically to probe respondents' latency trade-off decisions in depth after they've answered the structured questions
  • Segments respondents by role, experience, org size, and tech stack so latency tolerance data can be cross-tabbed by professional context, and closes with an open-text reflection question and an auto-generated report

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