--- title: "Stored data" description: "The ClickHouse tables and views ico writes and reads, and the names it stores metrics under." canonical_url: "https://inference-cost-optimizer.com/reference/data" --- # Stored data > The ClickHouse tables and views ico writes and reads, and the names it stores metrics under. Everything lives in one ClickHouse database, `ico` by default. Raw rows expire after 30 days, in whole days; replay results are kept until you delete them. ## Tables
Table Written from One row per Kept
requests the gateways request the gateway proxied 30 days
engine_spans the engines' OpenTelemetry traces engine span: queue, prefill, decode and end-to-end time, token counts 30 days
engine_metrics each engine's /metrics scraped sample 30 days
gpu_metrics the GPU exporters scraped sample, with its telemetry level 30 days
replay_runs ico replay replay run: replica, window, limits, budget, margin, status always
replay_results ico replay config tested in a run, at each stage always
## Views
View Gives
request_spans Every request with its engine span, joined on trace id; has_span is false where the engine sent none.
usage_by_tenant(since, until) Requests, errors and tokens per replica, model and tenant.
replica_busy(since, until) Per replica, the seconds with at least one request in flight, and the span of time its requests cover.
replica_phases(since, until) Per replica, engine time in prefill and decode, with the tokens of the same requests.
replica_engine(since, until) Per replica, when its engine was first and last heard from, and the model it reports.
host_gpus(since, until) Per host, its GPUs, telemetry level and the window's average of each counter.
The parameterized views are queried like functions: ```sql SELECT * FROM host_gpus(since = '2026-10-14 00:00:00', until = '2026-10-15 00:00:00') ``` ## Metric names Engine and GPU metrics are stored under ico's own names and units, so queries and reports read the same across vLLM versions and exporters. ### GPU metrics
Name Unit From dcgm-exporter From nvidia_gpu_exporter Level
sm_active_ratio 0 to 1 DCGM_FI_PROF_SM_ACTIVE L3
sm_occupancy_ratio 0 to 1 DCGM_FI_PROF_SM_OCCUPANCY L3
tensor_active_ratio 0 to 1 DCGM_FI_PROF_PIPE_TENSOR_ACTIVE L3
dram_active_ratio 0 to 1 DCGM_FI_PROF_DRAM_ACTIVE L3
util_ratio 0 to 1 DCGM_FI_DEV_GPU_UTIL nvidia_smi_utilization_gpu_ratio L2
memory_used_bytes bytes DCGM_FI_DEV_FB_USED nvidia_smi_memory_used_bytes L1
memory_total_bytes bytes DCGM_FI_DEV_FB_TOTAL nvidia_smi_memory_total_bytes L1
power_watts watts DCGM_FI_DEV_POWER_USAGE nvidia_smi_power_draw_watts L1
### Engine metrics
Name What it counts
requests_running , requests_waiting Requests in the engine's batch, and queued for it
kv_cache_usage Share of the KV cache in use
prompt_tokens_total , generation_tokens_total Tokens processed and generated, cumulative
prefix_cache_queries_total , prefix_cache_hits_total Prefix cache lookups and hits, cumulative
preemptions_total Requests evicted to free KV cache, cumulative
ttft_seconds , inter_token_latency_seconds , queue_time_seconds , prefill_time_seconds , decode_time_seconds Histograms, stored as _bucket , _sum and _count
Each maps every name vLLM has used for it, from 0.10 to 0.31. ## Sitemap See the full [sitemap](https://inference-cost-optimizer.com/sitemap.md) for all pages.