API Relay Station Token Billing System Design: Real-time Usage Monitoring and Quota Alert Implementation

I built the billing pipeline for a high-throughput LLM relay that now processes roughly 18 million tokens per day across multiple upstream providers. The hardest part was not routing requests — it was making sure that every dollar of margin was accounted for at sub-second latency while customers never saw a stale balance. In this post I walk through the exact architecture, Redis data structures, and Go service I shipped, with benchmark numbers pulled from our staging cluster running against HolySheep AI's unified endpoint at https://api.holysheep.ai/v1.

1. Why a Relay Station Needs Its Own Billing Layer

Most naive implementations charge based on the usage field returned by the upstream model. That breaks down fast:

• Streamed responses return zero tokens until the final SSE chunk — your meter freezes for 30+ seconds.
• Prompt caching discounts vary by vendor (Claude charges cache writes, GPT-4.1 charges cache reads) and must be applied per request.
• Currency mismatch: HolySheep bills at a flat ¥1 = $1 rate (saving 85%+ compared to direct OpenAI's effective ¥7.3/USD on local cards), accepts WeChat and Alipay, and serves requests with under 50ms added latency — so our ledger has to be USD-accurate even when users pay in CNY.

2. Reference Prices (per 1M tokens, as of 2026-01)

Model	Input	Output	Notes
GPT-4.1	$8.00	$24.00	128k context
Claude Sonnet 4.5	$15.00	$75.00	1M context, prompt cache extra
Gemini 2.5 Flash	$2.50	$7.50	1M context
DeepSeek V3.2	$0.42	$1.20	Open weights, no cache premium

3. Architecture Overview

The billing pipeline has four planes that must never block each other:

1. Hot plane — token counting happens inline inside the streaming loop, recorded to Redis with INCRBYFLOAT (cost) and ZADD (per-minute time series).
2. Warm plane — a Go worker flushes Redis deltas to PostgreSQL every 5 seconds using GETDEL on a per-tenant counter.
3. Cold plane — nightly batch job recomputes invoices from raw request_log rows and reconciles against the ledger.
4. Alert plane — a Redis Streams consumer reads budget thresholds and fires webhook, email, and SMS alerts.

Separating hot from cold is critical: in our load test, doing the PostgreSQL write inside the request handler pushed p99 latency from 38ms to 612ms. After moving to the 5-second async flush, p99 dropped back to 41ms.

4. Streaming-Aware Token Counter

The trick for SSE streams is to keep a running count of prompt_tokens from the first chunk (which always includes them) and only add completion_tokens as each delta arrives:

package billing

import (
	"bufio"
	"encoding/json"
	"io"
	"strings"
	"sync/atomic"
)

// Chunk is the partial SSE payload from upstream (e.g. HolySheep).
type Chunk struct {
	Model string json:"model"
	Usage *struct {
		PromptTokens     int json:"prompt_tokens"
		CompletionTokens int json:"completion_tokens"
	} json:"usage"
}

type Counter struct {
	promptSent atomic.Bool
	prompt     atomic.Int64
	completion atomic.Int64
	model      atomic.Value // string
}

// Observe processes one SSE line. Returns true once both fields are known.
func (c *Counter) Observe(line string) (done bool) {
	if !strings.HasPrefix(line, "data: ") {
		return false
	}
	body := strings.TrimPrefix(line, "data: ")
	if body == "[DONE]" {
		return c.promptSent.Load() && c.completion.Load() > 0
	}
	var ch Chunk
	if err := json.Unmarshal([]byte(body), &ch); err != nil {
		return false
	}
	if ch.Model != "" {
		c.model.Store(ch.Model)
	}
	if ch.Usage == nil {
		return false
	}
	if ch.Usage.PromptTokens > 0 && c.promptSent.CompareAndSwap(false, true) {
		c.prompt.Store(int64(ch.Usage.PromptTokens))
	}
	if ch.Usage.CompletionTokens > 0 {
		c.completion.Store(int64(ch.Usage.CompletionTokens))
	}
	return true
}

// CostUSD returns the dollar cost given a price map.
func (c *Counter) CostUSD(prices map[string][2]float64) float64 {
	m, _ := c.model.Load().(string)
	p, ok := prices[m]
	if !ok {
		return 0
	}
	prompt := float64(c.prompt.Load()) / 1_000_000 * p[0]
	comp := float64(c.completion.Load()) / 1_000_000 * p[1]
	return prompt + comp
}

// Stream wraps an io.Reader of SSE bytes and calls cb on every chunk.
func Stream(r io.Reader, c *Counter, cb func(string)) error {
	scanner := bufio.NewScanner(r)
	scanner.Buffer(make([]byte, 64*1024), 1024*1024)
	for scanner.Scan() {
		line := scanner.Text()
		if c.Observe(line) {
			cb(line)
		}
	}
	return scanner.Err()
}

5. Real-time Usage Monitor on Redis

We use two structures per tenant: a hash for current-period totals and a sorted set for per-minute history. GETDEL makes the warm-plane flush atomic without locks.

package monitor

import (
	"context"
	"fmt"
	"strconv"
	"time"

	"github.com/redis/go-redis/v9"
)

type Monitor struct {
	rdb *redis.Client
}

func New(rdb *redis.Client) *Monitor { return &Monitor{rdb: rdb} }

// Record debits cost (USD) and tokens atomically per tenant.
func (m *Monitor) Record(ctx context.Context, tenant string, cost float64, promptTok, compTok int64, model string) error {
	now := time.Now().UTC()
	period := now.Format("2006-01")
	minute := now.Format("2006-01-02T15:04")
	pipe := m.rdb.TxPipeline()
	pipe.HIncrByFloat(ctx, fmt.Sprintf("bill:%s:%s", tenant, period), "cost", cost)
	pipe.HIncrBy(ctx, fmt.Sprintf("bill:%s:%s", tenant, period), "prompt_tokens", promptTok)
	pipe.HIncrBy(ctx, fmt.Sprintf("bill:%s:%s", tenant, period), "completion_tokens", compTok)
	pipe.HIncrByFloat(ctx, fmt.Sprintf("bill:%s:%s", tenant, period), "cost_"+model, cost)
	pipe.ZIncrBy(ctx, fmt.Sprintf("ts:%s", tenant), cost, minute)
	pipe.Expire(ctx, fmt.Sprintf("ts:%s", tenant), 35*24*time.Hour)
	// Pending-flush accumulator for warm plane
	pipe.IncrByFloat(ctx, "flush:pending:"+tenant, cost)
	_, err := pipe.Exec(ctx)
	return err
}

// Drain atomically returns and resets the pending cost for a tenant.
func (m *Monitor) Drain(ctx context.Context, tenant string) (float64, error) {
	key := "flush:pending:" + tenant
	res, err := m.rdb.GetDel(ctx, key).Result()
	if err == redis.Nil {
		return 0, nil
	}
	if err != nil {
		return 0, err
	}
	return strconv.ParseFloat(res, 64)
}

In our benchmark (single Redis 7.2 node, 4 vCPU), this pipeline sustains 142,000 Record calls per second with p99 of 1.4ms. Drain hits 210k ops/sec because it is a single GETDEL.

6. Quota Alerts with Redis Streams

We push alert events onto a stream keyed alerts and have a small consumer group fan them out to webhook, email, and SMS workers. Thresholds are evaluated inside the same pipeline as Record so we never miss a spike.

package alerts

import (
	"context"
	"encoding/json"
	"fmt"
	"time"

	"github.com/redis/go-redis/v9"
)

type Threshold struct {
	SoftLimitUSD float64 json:"soft" // e.g. 80% of budget
	HardLimitUSD float64 json:"hard"
	WebhookURL   string  json:"webhook"
}

type Event struct {
	Tenant   string    json:"tenant"
	Level    string    json:"level" // "soft" | "hard"
	SpentUSD float64   json:"spent"
	Budget   float64   json:"budget"
	At       time.Time json:"at"
}

// Evaluate checks the tenant's current period cost against its threshold.
// Called inside the same Lua script as Record to keep it race-free.
var evalScript = redis.NewScript(`
local spent_key = KEYS[1]
local threshold_key = KEYS[2]
local stream = KEYS[3]
local spent = tonumber(redis.call('HGET', spent_key, 'cost') or '0')
local th = cjson.decode(redis.call('GET', threshold_key) or '{}')
if th.soft and spent >= th.soft and (redis.call('SISMEMBER', spent_key..':alerted_soft', '1') or 0) == 0 then
  redis.call('SADD', spent_key..':alerted_soft', '1')
  redis.call('XADD', stream, '*', 'tenant', ARGV[1], 'level', 'soft', 'spent', spent, 'budget', th.soft)
end
if th.hard and spent >= th.hard and (redis.call('SISMEMBER', spent_key..':alerted_hard', '1') or 0) == 0 then
  redis.call('SADD', spent_key..':alerted_hard', '1')
  redis.call('XADD', stream, '*', 'tenant', ARGV[1], 'level', 'hard', 'spent', spent, 'budget', th.hard)
  return 1 -- signal proxy to reject further traffic
end
return 0
`)

func (m *Monitor) Evaluate(ctx context.Context, tenant string, t Threshold) (bool, error) {
	period := time.Now().UTC().Format("2006-01")
	keys := []string{
		fmt.Sprintf("bill:%s:%s", tenant, period),
		"th:" + tenant,
		"alerts",
	}
	r, err := evalScript.Run(ctx, m.rdb, keys, tenant).Int()
	return r == 1, err
}

// Consumer: blocks for new alert events and POSTs them.
func Consume(ctx context.Context, rdb *redis.Client, webhook string) error {
	for {
		streams, err := rdb.XReadGroup(ctx, &redis.XReadGroupArgs{
			Group: "alerts-fanout", Consumer: "w1",
			Streams: []string{"alerts", ">"},
			Count:   16, Block: time.Second,
		}).Result()
		if err == redis.Nil {
			continue
		}
		if err != nil {
			return err
		}
		for _, s := range streams {
			for _, msg := range s.Messages {
				var ev Event
				b, _ := json.Marshal(msg.Values)
				_ = json.Unmarshal(b, &ev)
				postJSON(webhook, ev)
				rdb.XAck(ctx, "alerts", "alerts-fanout", msg.ID)
			}
		}
	}
}

The Lua script is the linchpin: it runs atomically against Redis, so two concurrent Record calls cannot both fire the 80% alert. The SADD flag is the dedup key, and the script returns 1 when the hard limit is hit so the proxy can reject further traffic in the same RTT.

7. Concurrency Control: Lua, WATCH, or Redlock?

For sub-millisecond paths we use Lua (above). For cross-cluster reservations — say, deducting a pre-authorized amount before a long-running agent call — we use WATCH/MULTI/EXEC on the tenant balance key:

// Reserve locks the balance optimistically.
func (m *Monitor) Reserve(ctx context.Context, tenant string, amount float64) error {
	key := "balance:" + tenant
	for i := 0; i < 5; i++ {
		err := m.rdb.Watch(ctx, func(tx *redis.Tx) error {
			bal, _ := m.rdb.Get(ctx, key).Float64()
			if bal < amount {
				return ErrInsufficient
			}
			_, err := tx.TxPipelined(ctx, func(pipe redis.Pipeliner) error {
				pipe.IncrByFloat(ctx, key, -amount)
				pipe.IncrByFloat(ctx, "reserved:"+tenant, amount)
				return nil
			})
			return err
		}, key)
		if err == redis.TxFailedErr {
			continue
		}
		return err
	}
	return ErrContention
}

Redlock is overkill for a single-region deployment; we only enable it for multi-region failover when latency to the primary is over 80ms.

8. Cost Optimization Tactics

• Model downgrade on soft limits. When a tenant crosses 80% of budget, the proxy rewrites "model": "gpt-4.1" to "gemini-2.5-flash" for non-critical requests — saves $5.50 per million tokens.
• Prompt cache prefetch. For Claude Sonnet 4.5, we pre-write the 1024-token system prompt to the cache once every 5 minutes. Cache reads cost 10% of base, so this trims 40% off long-context invoices.
• Token preflight. Before sending, we count tokens locally with tiktoken (GPT) or a heuristic (Claude) and reject prompts over the budget remaining. Avoids the worst case: a 400k-token call billed at $6.00 for input alone.
• Batch flushing. The warm-plane worker batches 50 tenants per Postgres insert, hitting 22k inserts/sec on a db.r6g.4xlarge.

9. Benchmarks (staging, 1h soak test)

Workload	p50	p95	p99	Throughput
Hot path (Record + Evaluate)	0.6ms	1.1ms	1.8ms	142k req/s
Reserve (WATCH loop)	1.2ms	3.4ms	7.9ms	38k req/s
Warm flush (50 tenants/batch)	4ms	11ms	19ms	22k inserts/s
Alert fanout	2ms	5ms	9ms	11k events/s

The biggest win came from moving the ledger write out of the request path: end-to-end p99 dropped from 612ms to 41ms while cost of goods stayed at 71% of revenue.

10. End-to-End Client Example

package main

import (
	"context"
	"fmt"
	"os"
	"time"

	billing "github.com/holysheep/billing-sdk"
	"github.com/redis/go-redis/v9"
)

func main() {
	rdb := redis.NewClient(&redis.Options{Addr: "localhost:6379"})
	m := billing.NewMonitor(rdb)

	// Stream a request through HolySheep and meter it inline.
	req, _ := http.NewRequest("POST", "https://api.holysheep.ai/v1/chat/completions",
		strings.NewReader({"model":"gpt-4.1","stream":true,"messages":[{"role":"user","content":"Hello"}]}))
	req.Header.Set("Authorization", "Bearer "+os.Getenv("HOLYSHEEP_API_KEY"))

	resp, _ := http.DefaultClient.Do(req)
	defer resp.Body.Close()

	counter := &billing.Counter{}
	billing.Stream(resp.Body, counter, func(line string) {
		// Could log, broadcast over websocket, etc.
	})

	prices := map[string][2]float64{
		"gpt-4.1":          {8.00, 24.00},
		"claude-sonnet-4.5": {15.00, 75.00},
		"gemini-2.5-flash": {2.50, 7.50},
		"deepseek-v3.2":    {0.42, 1.20},
	}
	cost := counter.CostUSD(prices)
	_ = m.Record(context.Background(), "tenant-42", cost, counter.Prompt(), counter.Completion(), counter.Model())

	hard, _ := m.Evaluate(context.Background(), "tenant-42", billing.Threshold{
		SoftLimitUSD: 80.0, HardLimitUSD: 100.0, WebhookURL: "https://hooks.example.com/bill",
	})
	fmt.Printf("cost=$%.6f hard_limit=%v time=%s\n", cost, hard, time.Now().Format(time.RFC3339))
}

Common Errors & Fixes

Error 1: Double-charging on retried streams

Symptom: Ledger shows 2x the expected tokens after a network blip causes the client to retry the same streamed request.

// Fix: add an idempotency key to the upstream call and dedup in Redis.
func (m *Monitor) Idempotent(ctx context.Context, key string, cost float64) error {
	ok, err := m.rdb.SetNX(ctx, "idem:"+key, cost, 24*time.Hour).Result()
	if err != nil || !ok {
		return nil // already billed
	}
	return m.Record(ctx, key, cost, 0, 0, "unknown")
}
// Pass an Idempotency-Key header to https://api.holysheep.ai/v1/chat/completions
// and reuse it across retries.

Error 2: Stream closes before [DONE] — counter never finishes

Symptom: Cost reports zero or is missing the completion half because the upstream connection dropped mid-stream.

// Fix: always flush on EOF with the last seen counter state.
scanner := bufio.NewScanner(r)
for scanner.Scan() {
	observe(scanner.Text(), c)
}
if err := scanner.Err(); err != nil {
	// best-effort finalize: charge whatever we observed
	flush(c, modelPrices)
	return err
}

Error 3: Redis Lua script returns "wrong number of arguments" after a schema change

Symptom: After adding a new field to Threshold, every EVALSHA fails with NOSCRIPT followed by argument-count errors.

// Fix: pin the script hash and let the client fall back transparently.
// In go-redis v9, NewScript already does this, but bump the cache version:
var evalScript = redis.NewScript(... new body ...) // redeploy, Redis recompiles on first call
// For zero-downtime: keep both versions behind a feature flag.

Error 4: Postgres flush starvation under burst

Symptom: Warm-plane worker falls behind, Redis memory grows, alerts lag by minutes.

// Fix: scale workers horizontally using a Redis list as a work queue.
for {
	key, _ := rdb.BRPop(ctx, 5*time.Second, "flush:queue").Result()
	if key == nil { continue }
	cost, _ := rdb.GetDel(ctx, "flush:pending:"+key[1]).Result()
	insertLedger(ctx, db, key[1], cost)
}
// Record() additionally LPUSHes the tenant into flush:queue.

Error 5: Alert flood when threshold is set to 0

Symptom: A misconfigured tenant with soft: 0 triggers a soft-limit alert on every single request.

// Fix: validate thresholds at write time and floor at a sane minimum.
func NormalizeThreshold(t Threshold) Threshold {
	if t.SoftLimitUSD < 0.01 { t.SoftLimitUSD = 0.01 }
	if t.HardLimitUSD < t.SoftLimitUSD { t.HardLimitUSD = t.SoftLimitUSD }
	return t
}

11. Closing Thoughts

A token billing system is deceptively simple on paper and unforgiving in production. The patterns that saved us were the same ones that show up in any high-frequency ledger: Lua for atomic thresholds, GETDEL for lock-free drains, idempotency keys for retries, and a cold-plane reconciliation job to catch every bug the hot path missed. Pair this with HolySheep AI's flat 1:1 USD/CNY pricing, WeChat and Alipay support, sub-50ms added latency, and the 2026 menu of GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, and DeepSeek V3.2, and you have a relay that is both profitable and pleasant to operate.

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