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.
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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.
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Perguntas de exemplo
Uma prévia do que há no modelo. Todas as perguntas podem ser editadas antes de publicar.
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)
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)
Overall, how sensitive to latency are your primary workloads?
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
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
How important is reducing tail latency (p95/p99) compared to reducing average latency for your 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
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
If your median latency meets its target, how acceptable are occasional latency spikes?
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
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
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
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
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
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
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.
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.
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)
How many years of professional software development experience do you have?
- < 1
- 1–2
- 3–5
- 6–9
- 10–14
- 15+
Approximately how large is your organization?
- 1 (just me)
- 2–10
- 11–50
- 51–200
- 201–1,000
- 1,001–5,000
- > 5,000
In which region are you primarily located?
- North America
- Latin America
- Europe
- Middle East
- Africa
- Asia
- Oceania
- Prefer not to say
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.
O que está incluído
Acompanhamento com IA
Sondagens adaptativas nas respostas abertas que revelam detalhes que um formulário estático deixaria passar.
Verificações de atenção
Proteções integradas contra respostas apressadas e participantes de baixa qualidade.
Textos escritos por IA
Redação, ordem e ramificações escritas pela IA, ajustadas ao seu objetivo de pesquisa.
Relatório automático
Temas, citações e um resumo em linguagem simples se escrevem sozinhos assim que as respostas chegam.
Por que este modelo
Para que este modelo foi feito: não encontramos nenhum modelo diretamente comparável em outras ferramentas de pesquisa.
O que o diferencia
- 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
Perguntas frequentes
Quais perguntas estão no modelo “Developer Latency Sensitivity & SLO Benchmarking Survey”?
O modelo inclui 22 perguntas prontas para usar, começando por: “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)” · “Which of the following use cases are most relevant to your current work? (Select all that apply)”. O conjunto completo aparece acima e todas as perguntas podem ser editadas.
Quanto tempo leva para responder a esta pesquisa?
Os participantes normalmente terminam as 22 perguntas em cerca de 11 minutos.
Posso personalizar este modelo?
Sim: cada pergunta, cada opção de resposta e a ordem podem ser editadas antes de publicar. Você pode adicionar ou remover perguntas, ou pedir ao editor com IA para refazer a pesquisa em torno do seu objetivo.
Este modelo é gratuito?
Sim. Abra no editor e comece a personalizar agora: não é preciso criar conta para testar, e o plano gratuito cobre a publicação da sua pesquisa.
Pronto para publicar?
Abra este modelo no editor. Tudo é seu para mudar antes de o primeiro participante ver.
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