Launch this developer survey to benchmark distributed tracing sampling: adoption, usage, and trade-offs. Includes head vs tail, rates, OpenTelemetry for APM.
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
Sample Survey Items
Q1
Multiple Choice
Which tracing/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
Q2
Opinion Scale
How familiar are you with tracing sampling concepts?
Range: 1 – 10
Min: Not at all familiarMid: Moderately familiarMax: Very familiar
Q3
Multiple Choice
Attention check: To confirm you’re reading the questions, please select “I am paying attention.”
I am paying attention
I am not paying attention
Prefer not to say
Q4
Multiple Choice
Which sampling approaches have you implemented or configured in the last 6 months? Select all that apply.
Always on (head, 100%)
Head 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
If you use tail-based sampling, what most often triggers retaining a trace? 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
Q6
Multiple Choice
If you do not use tail-based sampling, what are the main reasons? 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
Q7
Numeric
At peak hours, approximately how many spans per minute does your system generate?
Accepts a numeric value
Whole numbers only
Q8
Constant Sum
Allocate 100 points across the objectives below to reflect their relative importance.
Total must equal 100
Min per option: 0Whole numbers only
Q9
Matrix
Indicate your agreement with these statements about sampling in your environment.
Rows
Strongly disagree
Disagree
Neutral
Agree
Strongly agree
Lower sampling rates can miss intermittent failures in our system
•
•
•
•
•
Tail-based sampling improves time to debug critical incidents here
•
•
•
•
•
Adaptive/dynamic sampling keeps costs sufficiently predictable for us
•
•
•
•
•
Head probabilistic sampling provides enough coverage to monitor trends
•
•
•
•
•
It’s easier to manage sampling decisions at the collector/gateway than in code
•
•
•
•
•
Q10
Ranking
Rank the signals you most want sampling 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
Q11
Multiple Choice
Scenario: A consumer API averages 10k RPS with traffic spikes and a strict budget. Which baseline strategy would you start with?
Head 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
Q12
Long Text
Briefly explain your choice for the scenario above.
Max 600 chars
Q13
Dropdown
Where are sampling decisions primarily enforced today?
SDK/agent level
Collector/gateway level
Backend/vendor-managed
In-application custom logic
Unsure
Q14
Rating
How likely are you to adjust your sampling strategy in the next 3 months?
Scale: 10 (star)
Min: Very unlikelyMax: Very likely
Q15
Dropdown
What is your primary role?
Backend/software engineer
SRE/Operations
Platform/Infrastructure
DevOps
Observability/Telemetry
Data/Analytics
Engineering manager
Architect
Other
Q16
Dropdown
How many years have you worked with distributed systems?
Less than 1
1–2
3–5
6–10
11+
Q17
Dropdown
Approximately how large is your organization?
1–49
50–249
250–999
1,000–4,999
5,000+
Q18
Dropdown
Which region do you primarily work in?
North America
Europe
Asia-Pacific
Latin America
Middle East
Africa
Other
Q19
Long Text
Any other feedback or context about your tracing and sampling strategy?
Max 600 chars
Q20
Chat Message
Welcome! This short survey focuses on tracing sampling strategies over the past 6 months. Please answer based on your current or most recent environment.
Q21
AI Interview
AI Interview: 2 Follow-up Questions on your sampling decisions
AI InterviewLength: 2Personality: Expert InterviewerMode: Fast
Q22
Chat Message
Thank you for participating—your responses are greatly appreciated!
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