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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Have you written, reviewed, or deployed code in a professional capacity in the last 30 days?
- Yes
- No
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)
Overall, how sensitive to latency are your primary workloads?
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
If your median latency meets its target, how acceptable are occasional latency spikes?
For a latency-sensitive workload, rank the following priorities from most to least important.
- Median latency (p50)
- Tail latency (p95/p99)
- Availability/reliability
- Cost efficiency
- Throughput
- Feature completeness
- Developer productivity
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.
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)
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.
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)
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
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
When latency threatens your SLA or SLO, rank your top strategies in order of priority (drag to reorder).
- Degrade non-critical features
- Cache more aggressively
- Precompute or batch work
- Parallelize or partition requests
- Return partial results
- Scale up/out resources
- Fail fast with retry/backoff
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
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.
How many years of professional software development experience do you have?
- < 1
- 1–2
- 3–5
- 6–9
- 10–14
- 15+
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
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
Approximately how large is your organization?
- 1 (just me)
- 2–10
- 11–50
- 51–200
- 201–1,000
- 1,001–5,000
- > 5,000
How important is reducing tail latency (p95/p99) compared to reducing average latency for your workloads?
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
In which region are you primarily located?
- North America
- Latin America
- Europe
- Middle East
- Africa
- Asia
- Oceania
- Prefer not to say
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
AI 후속 질문
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주의력 확인 장치
성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.
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문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.
자동 리포트
응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.
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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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바로 사용할 수 있는 질문 24개가 포함되어 있으며, 처음 질문은 다음과 같습니다: “Welcome, and thank you for your interest in this survey on developer latency experiences. This survey takes approximatel…” · “Have you written, reviewed, or deployed code in a professional capacity in the last 30 days?” · “Which of the following languages or platforms did you actively use in the last 30 days? (Select all that apply)”. 전체 질문은 위에서 미리 볼 수 있고 모두 수정 가능합니다.
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