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Cost Performance And Capacity Planning documentation

Cost, Performance, and Capacity Planning

Definition

Methods to estimate, monitor, and optimize resource usage, latency, throughput, and spend for generative systems.

Why It Matters

  • Predictable costs and performance SLAs
  • Informs model/routing choices and batching policies

2025 State of the Art

  • Token-level accounting and caching/streaming strategies
  • Speculative decoding for speed, multiplexing/batching for utilization
  • Quantization to reduce memory/bandwidth and hardware costs

Key Players

  • OpenAI/Azure usage analytics, Google usage dashboards
  • vLLM/TGI runtime telemetry; NVIDIA DCGM for GPU metrics

Challenges

  • Variance from prompt lengths, tool calls, retries
  • Long-context cost explosions; cache invalidation complexity

Reference Architectures

  • Central cost service ingesting usage (tokens/images/minutes)
  • Autoscaler tied to queue depths and p95 latency SLOs
  • Prompt/template governance to cap worst cases

Opportunities

  • Cost-aware model routing (small-to-large backoff)
  • Aggressive KV cache reuse and prompt caching
  • Adaptive batching with tail-latency protection

Design Checklist & Acceptance Criteria

  • Define SLOs (p50/p95 TTFT/TPOT) and utilization targets
  • Track per-feature cost and unit economics ($/success)
  • Implement backpressure and circuit breakers
  • Simulate peak loads; run chaos tests

References

  • Title: vLLM (serving efficiency) URL: https://vllm.ai/ Publisher/Vendor: vLLM Project Accessed: 2025-08-14 Version_or_release: provider_reported
  • Title: TensorRT-LLM URL: https://developer.nvidia.com/tensorrt-llm Publisher/Vendor: NVIDIA Accessed: 2025-08-14 Version_or_release: provider_reported