A developer-focused research instrument for benchmarking distributed tracing sampling adoption, practices, and trade-offs across OpenTelemetry and related observability tooling. Designed for engineering teams seeking to understand how peers approach head-based, tail-based, and adaptive sampling decisions.
What's Included
AI-Powered Questions
Intelligent follow-up questions based on responses
Automated Analysis
Real-time sentiment and insight detection
Smart Distribution
Target the right audience automatically
Detailed Reports
Comprehensive insights and recommendations
Template Overview
23
Questions
AI-Powered
Smart Analysis
Ready-to-Use
Launch in Minutes
This professionally designed survey template helps you gather valuable insights with intelligent question flow and automated analysis.
Sample Survey Items
Q1
Chat Message
Welcome to this survey on distributed tracing sampling strategies.
Your participation is voluntary, and you may stop at any time. There are no right or wrong answers — we are interested in your actual practices and opinions. All responses are confidential and will be reported in aggregate only.
This survey takes approximately 8–10 minutes to complete.
Q2
Multiple Choice
Which of the following tracing or observability tools have you used in the last 6 months? Select all that apply.
OpenTelemetry
Jaeger
Zipkin
Honeycomb
Datadog
New Relic
AWS X-Ray
Grafana Tempo
Elastic APM
Other
None of the above
Q3
Opinion Scale
How familiar are you with tracing sampling concepts (e.g., head-based, tail-based, rate-limited sampling)?
Range: 1 – 7
Min: Not at all familiarMid: NeutralMax: Extremely familiar
Q4
Multiple Choice
Which sampling approaches have you implemented or configured in the last 6 months? Select all that apply.
Always on (head-based, 100%)
Head-based probabilistic (trace-level rate)
Rate-limited sampling
Tail-based sampling
Adaptive/dynamic sampling
Per-endpoint or attribute-based rules
I'm not sure
None
Q5
Multiple Choice
When using tail-based sampling, what most commonly triggers retaining a trace in your environment? Select the primary trigger.
Error status codes
High latency percentiles (e.g., p95/p99)
Specific endpoints or attributes
Adaptive scoring from backend
Business events or SLO breaches
Not applicable — I do not use tail-based sampling
Q6
Multiple Choice
What are the main reasons you have not adopted tail-based sampling? Select all that apply.
Implementation complexity
Infrastructure/resource constraints
Cost concerns
Data protection/compliance constraints
Not needed for our use cases
Lack of expertise or guidance
Tooling/vendor limitations
Not applicable — I already use tail-based sampling
Q7
Dropdown
Where are sampling decisions primarily enforced in your current environment?
SDK/agent level
Collector/gateway level
Backend/vendor-managed
In-application custom logic
Multiple layers
Unsure
Q8
Dropdown
At peak hours, approximately how many spans per minute does your system generate?
Fewer than 1,000
1,000–10,000
10,001–100,000
100,001–1,000,000
More than 1,000,000
Unsure
Q9
Ranking
Rank the following tracing objectives from most important (1) to least important in your environment.
Drag to order (top = most important)
Reducing observability costs
Faster debugging and root-cause analysis
Maintaining representative trace coverage
Meeting compliance or data-retention requirements
Supporting SLO monitoring and alerting
Q10
Opinion Scale
To what extent do you agree: Our current sampling rate provides sufficient trace coverage for debugging production issues.
Rank the signals you most want your sampling strategy to capture reliably (1 = highest priority).
Drag to order (top = most important)
Rare high-latency outliers
Error spikes or regressions
Customer-critical endpoint issues
Incidents after new releases
Cross-service contention or bottlenecks
Q14
Opinion Scale
How likely are you to adjust your sampling strategy in the next 3 months?
Range: 1 – 7
Min: Not at all likelyMid: NeutralMax: Extremely likely
Q15
Multiple Choice
Scenario: A consumer-facing API averages 10,000 requests per second with periodic traffic spikes and a limited observability budget. Which baseline sampling strategy would you start with?
Head-based probabilistic at a low fixed rate (e.g., 0.1–1%)
Rate-limited head sampling with per-service quotas
Tail-based triggers (errors/high latency) with a minimal baseline
Always on (100%) to maximize coverage
There isn't enough information to decide
Q16
Long Text
Briefly explain your reasoning for the sampling strategy you selected in the scenario above.
Max chars
Q17
AI Interview
We'd like to explore your sampling decisions in a bit more depth. An AI moderator will ask you a couple of follow-up questions based on your responses so far.
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Reference questions: 5
Q18
Long Text
Based on your responses in this survey, please share any additional thoughts or context about your tracing and sampling strategy.
Max chars
Q19
Dropdown
What is your primary role?
Backend/software engineer
SRE/Operations
Platform/Infrastructure
DevOps
Observability/Telemetry
Data/Analytics
Engineering manager
Architect
Other
Q20
Dropdown
How many years have you worked with distributed systems?
Less than 1
1–2
3–5
6–10
11+
Q21
Dropdown
Approximately how many employees are in your organization?
1–49
50–249
250–999
1,000–4,999
5,000+
Q22
Dropdown
Which region do you primarily work in?
North America
Europe
Asia-Pacific
Latin America
Middle East & Africa
Other
Q23
Chat Message
Thank you for completing this survey — your responses will help improve tracing and sampling practices across the community. Your data will be reported in aggregate only.
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