---

name: cost-allocation disable-model-invocation: true description: | Per-tenant, per-feature, per-query RAG cost tracking. Covers token counting (tiktoken, Anthropic count_tokens), structured metadata logging, aggregation in BigQuery/Snowflake/ClickHouse, dashboards (Grafana, Metabase), LangSmith and Langfuse native cost reports, and budget alerts. Schema + example queries.

USE WHEN: user mentions "RAG cost", "cost per tenant", "cost per query", "token counting", "chargeback", "showback", "LangSmith cost", "Langfuse cost", "budget alerts"

DO NOT USE FOR: reducing cost - use rag-production; batch-discount strategy - use batch-inference; routing for cheaper models - use llm-gateway allowed-tools: Read, Grep, Glob, Write, Edit

RAG Cost Allocation

Finance, product, and SRE all ask the same question: what does a RAG query cost, and who should pay? The default provider dashboards only show account-level totals. You need per-tenant, per-feature, per-query attribution — which means instrumenting every call with structured metadata and aggregating it.

Cost Components

Component	Unit	Typical share
Embedding (ingest)	tokens → $	5–20%
Embedding (query)	tokens → $	1–5%
Vector DB	$/hour (managed) or compute	10–30%
Rerank (hosted or self-hosted GPU)	pairs or $/h	5–15%
LLM generation	tokens → $	50–80%
Storage (objects, snapshots)	$/GB/month	<5%

LLM generation is almost always the top line item. Instrument it first.

Token Counting

OpenAI / general

import tiktoken
enc = tiktoken.encoding_for_model("gpt-4o")
n_in  = len(enc.encode(prompt))
n_out = len(enc.encode(completion))

For OpenAI, prefer the usage block returned by the API — it is authoritative and includes cached/uncached breakdowns.

resp = client.chat.completions.create(model="gpt-4o", messages=[...])
usage = resp.usage
prompt_tok = usage.prompt_tokens
cached_tok = usage.prompt_tokens_details.cached_tokens
out_tok    = usage.completion_tokens

Anthropic

import anthropic
client = anthropic.Anthropic()

# Pre-call estimate
est = client.messages.count_tokens(
    model="claude-sonnet-4-5",
    messages=[{"role": "user", "content": prompt}],
).input_tokens

# Authoritative from the response
resp = client.messages.create(model="claude-sonnet-4-5", messages=[...], max_tokens=512)
u = resp.usage
# u.input_tokens, u.output_tokens, u.cache_creation_input_tokens, u.cache_read_input_tokens

Voyage / Cohere embedding

Their SDKs return usage.total_tokens (or the API responds with billed tokens). Always trust the API's count over local estimates.

Pricing Table (Config, Not Code)

Keep prices out of source. Load from a versioned YAML / JSON in object storage so Finance can update without deploys.

# pricing/2025-01.yaml
llm:
  anthropic/claude-sonnet-4-5:
    input_per_1m: 3.00
    output_per_1m: 15.00
    cache_write_per_1m: 3.75
    cache_read_per_1m: 0.30
  openai/gpt-4o:
    input_per_1m: 2.50
    output_per_1m: 10.00
embedding:
  openai/text-embedding-3-small:
    per_1m: 0.02
  voyage/voyage-3:
    per_1m: 0.06
rerank:
  cohere/rerank-v3.5:
    per_1k_searches: 2.00

def cost_usd(provider_model: str, usage: dict, pricing: dict) -> float:
    p = pricing["llm"][provider_model]
    return (
        usage["input_tokens"]           * p["input_per_1m"]       / 1_000_000
      + usage["output_tokens"]          * p["output_per_1m"]      / 1_000_000
      + usage.get("cache_write", 0)     * p["cache_write_per_1m"] / 1_000_000
      + usage.get("cache_read", 0)      * p["cache_read_per_1m"]  / 1_000_000
    )

Unified Log Schema

Emit one structured event per LLM / embedding / rerank call. Store in ClickHouse / BigQuery / Snowflake / a columnar Parquet lake — anything queryable.

{
  "ts": "2025-04-14T18:22:01Z",
  "request_id": "req_01HX...",
  "trace_id": "trace_01HX...",
  "tenant_id": "acme-corp",
  "user_id": "u_42",
  "feature": "qa_chat",          // product surface
  "stage": "generation",         // embed | retrieve | rerank | generation
  "provider": "anthropic",
  "model": "claude-sonnet-4-5",
  "input_tokens": 3420,
  "output_tokens": 189,
  "cache_read_tokens": 3100,
  "cache_write_tokens": 0,
  "latency_ms": 1450,
  "cost_usd": 0.00412,
  "status": "ok"
}

Minimum viable set of keys: tenant_id, feature, stage, model, *_tokens, cost_usd, ts. Everything else is optional but helpful.

Instrumenting with a wrapper

def call_llm(messages, *, tenant_id, feature, model="claude-sonnet-4-5"):
    start = time.time()
    resp = anthropic_client.messages.create(model=model, messages=messages, max_tokens=1024)
    u = resp.usage
    usage = {
        "input_tokens": u.input_tokens,
        "output_tokens": u.output_tokens,
        "cache_read": u.cache_read_input_tokens or 0,
        "cache_write": u.cache_creation_input_tokens or 0,
    }
    log_event({
        "ts": iso_now(),
        "tenant_id": tenant_id,
        "feature": feature,
        "stage": "generation",
        "provider": "anthropic",
        "model": model,
        **usage,
        "cost_usd": cost_usd(f"anthropic/{model}", usage, pricing),
        "latency_ms": int((time.time() - start) * 1000),
    })
    return resp

Warehouse Queries

Monthly cost per tenant

SELECT tenant_id,
       SUM(cost_usd) AS cost,
       SUM(input_tokens + output_tokens) AS tokens,
       COUNT(*) AS calls
FROM llm_events
WHERE ts >= DATE_TRUNC('month', CURRENT_DATE)
GROUP BY 1
ORDER BY cost DESC;

Cost per query (p50/p95)

SELECT feature,
       APPROX_PERCENTILE(cost_usd, 0.5)  AS p50,
       APPROX_PERCENTILE(cost_usd, 0.95) AS p95,
       APPROX_PERCENTILE(cost_usd, 0.99) AS p99
FROM (
  SELECT trace_id, feature, SUM(cost_usd) AS cost_usd
  FROM llm_events
  WHERE ts >= CURRENT_DATE - INTERVAL '7' DAY
  GROUP BY trace_id, feature
)
GROUP BY feature;

Cache-hit savings (Anthropic)

SELECT model,
       SUM(cache_read_tokens)  AS cached_in,
       SUM(input_tokens)       AS billed_in,
       SAFE_DIVIDE(SUM(cache_read_tokens), SUM(cache_read_tokens + input_tokens)) AS hit_rate,
       SUM(cache_read_tokens)  * 0.30 / 1e6 AS saved_usd  -- at Sonnet cache-read rate
FROM llm_events
WHERE model LIKE 'claude-%'
GROUP BY 1;

Dashboards

• Grafana on ClickHouse / Postgres: a cost_events table with hourly materialized views. Panels: total $ (24h), per-tenant top 20, cost per stage stacked bar, cache hit rate.
• Metabase / Superset on Snowflake or BigQuery: drag-and-drop for Finance.
• LangSmith: native cost + token panels if all traffic is instrumented with LangChain/LangGraph runs.
• Langfuse: model_usage + trace.cost with built-in pricing table; supports OTel; self-host free tier.
• Helicone: proxy-based, drops in with a base_url change; good for quick wins.

Budget Alerts

-- Example alert query (Grafana)
SELECT tenant_id, SUM(cost_usd) AS cost_today
FROM llm_events
WHERE ts >= CURRENT_DATE
GROUP BY 1
HAVING SUM(cost_usd) > (SELECT daily_budget FROM tenants WHERE tenants.id = tenant_id)

Wire alerting channels:

• Slack webhook on >80% of budget (warn).
• PagerDuty on 100% (page).
• Automated throttle: insert tenant_id into a throttled table read by the gateway.

Attribution Patterns

Propagating tenant_id through pipelines

Put it on the request context and thread it into every downstream call:

from contextvars import ContextVar
current_tenant: ContextVar[str] = ContextVar("tenant")

async def handle(req):
    current_tenant.set(req.headers["x-tenant"])
    return await rag_pipeline(req.query)

# Inside the pipeline:
call_llm(msgs, tenant_id=current_tenant.get(), feature="qa_chat")

With LangGraph / LlamaIndex, pass tenant_id via RunnableConfig.configurable or callback_manager metadata and extract in a shared handler.

Shared vs dedicated indexes

• Dedicated tenant index: infra cost billed directly; generation still needs per-call attribution.
• Shared index with namespace: allocate infra cost by vector count or QPS share; generation via call logs.

Anti-Patterns

Anti-Pattern	Fix
Using pre-call count_tokens as billed cost	Always use the API's usage — it is authoritative
Hardcoding prices in Python	Externalize to versioned YAML/JSON
Logging only at the top-level	Log each stage (embed, retrieve, rerank, generate)
Single dashboard for all tenants	Per-tenant drill-down + top-N board
No cache-read breakdown for Anthropic	Split cache_read_tokens from input_tokens
Alerts on day-of-month totals only	Also alert on hourly rate spikes
Forgetting vector DB and GPU serving costs	Include infra costs via infra cost export (AWS CUR, etc.)

Production Checklist

• One structured event per LLM/embedding/rerank call
• tenant_id, feature, stage, model, *_tokens, cost_usd on every event
• Pricing table externalized and version-tagged
• Warehouse ingestion (BQ/Snowflake/ClickHouse) with hourly rollup MVs
• Dashboards: per-tenant, per-feature, per-stage, cache-hit rate
• Budget alerts at 80% (Slack) and 100% (page + throttle)
• Reconciled monthly vs provider invoice (target < 2% variance)
• Infra costs (vector DB, GPU pools) joined via cost-and-usage reports
• Quarterly cost review by tenant + feature to target optimization