Technologies/Jaeger/gen_ai_server_request_time
JaegerJaegerMetric

gen_ai_server_request_time

Generative AI server request duration
Dimensions:None
Knowledge Base (1 documents, 0 chunks)
referenceTime to First Token (TTFT) in LLM Inference2183 wordsscore: 0.75This page provides a comprehensive technical reference on Time to First Token (TTFT) as a performance metric for LLM inference systems. It covers TTFT's definition, components (scheduling delay and prompt processing time), relationship to other metrics like TBT and TPOT, optimization strategies including dynamic token pruning and cache management, and advanced temporal analysis approaches like fluidity-index for better user experience assessment.

Technical Annotations (18)

Configuration Parameters (3)
max_retriesrecommended: 3
Maximum retry attempts for server errors
base_delayrecommended: 1.0
Base delay in seconds for retry backoff
http_options.timeoutrecommended: Set in generation config, not client constructor
Client constructor http_options parameter is ignored; timeout must be configured in generation config
Error Signatures (5)
500http status
503http status
The server had an error while processing your requestlog pattern
The engine is currently overloaded, please try again laterlog pattern
server disconnectedlog pattern
CLI Commands (1)
wait_time = min(60, (2 ** attempt)); time.sleep(wait_time)remediation
Technical References (9)
Backend APIcomponentUIcomponentSDKcomponentcircuit breakersconceptexponential backoffconceptenginecomponentgenai.Clientcomponenttypes.HttpOptionscomponentgeneration configcomponent
Related Insights (9)
Parallel Tool Call Performance Multiplierwarning

Sequential tool execution in Claude Code agents causes 90% longer research times compared to parallel execution. Enabling parallel tool calling for both subagent spawning (3-5 agents) and tool usage (3+ tools) dramatically reduces latency.

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Agent Coordination Overhead in Complex Workflowswarning

Multi-agent systems face coordination failures including spawning excessive subagents, endless source searches, and agent distraction through excessive updates. Lead agents must manage parallel subagents while maintaining coherent research strategy.

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Observability Blind Spots in Multi-Agent Tracescritical

Distributed agent architectures require trace correlation across multiple context windows and parallel execution paths. Without proper instrumentation, teams lose visibility into subagent activities, making root cause analysis impossible when investigations fail.

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LLM Time-to-First-Token Latency Spikewarning

High time-to-first-token from LLM providers indicates queuing, rate limiting, or model cold starts, causing user-perceived delays even when total generation time is acceptable.

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Traffic pattern changes cause elevated API latencywarning
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Server errors (500/503) require retry with exponential backoffwarning
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W&B Inference 500 Internal Server Error requires retry with backoffwarning
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W&B Inference 503 Service Overloaded due to high trafficwarning
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GenAI SDK client timeout configuration ignored causing 5-minute hard limitwarning
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