Stake in the ground

If you run Airewrite as a pay‑per‑use product and don’t have per‑tenant SLOs, quotas, and cost attribution, you will lose margin and create support debt. Build three things first: per‑tenant SLOs backed by automated throttles, precise token accounting for chargeback, and a redrive/canary pattern so misbehavior never hits billing unnoticed.

Why this matters: a single noisy tenant can double your monthly model bill overnight; you need per‑tenant telemetry, soft guardrails, and an auditable pipeline for invoices.

H2: Per‑tenant SLOs — measurable, tiered, and enforced

Take a binary stance: SLOs are product features, not ops suggestions. Define three tenant SLO dimensions up front: availability (endpoint success), latency (P95), and cost‑efficiency (tokens per successful request).

  • Defaults & tiers: create Standard/Pro/Enterprise tiers. Example: Standard = 99.5% success, P95 <= 1.5s; Pro = 99.9%/P95 <= 800ms; Enterprise negotiable. Put SLOs in the contract and mirror them in monitoring.
  • Where to enforce: run inference behind Vertex AI endpoints (or Cloud Run for custom stacks) and front with a Cloud Functions gateway that applies per‑tenant logic. Use Cloud Memorystore (Redis) for fast counters and short‑term rate limits.
  • Numeric guardrail example: default burst = 10 requests/min, sustained = 2000 tokens/min per tenant; Enterprise customers buy higher quotas.
  • Mirrored canaries: route 1% of production traffic for new models to a mirrored canary tenant that receives responses from both your production model and a candidate model. Track token delta and latency changes for 14 days before promoting.

H2: Token accounting and cost attribution (Datadog + Snowflake)

Token accounting is the single source of truth for billing — instrument at the gateway and persist an event stream that’s queryable.

  • Event capture: every request leaving the gateway should emit a JSON event: {tenant_id, request_id, model, prompt_tokens, completion_tokens, embedding_tokens, model_cost_usd, timestamp, request_size_bytes, pinecone_ops}. Send this to Pub/Sub.

  • Near‑real‑time metrics: increment a Datadog custom metric for visibility and alerting. Sample DogStatsD call from your gateway:

    statsd.increment('airewrite.tokens', prompt_tokens + completion_tokens, tags=[f"tenant:{tenant_id}", f"model:{model}"])

    Also emit a gauge for model_cost_estimate_usd per request.

  • Batch storage for billing: batch Pub/Sub → Cloud Function or Dataflow job → stage JSON to GCS and AUTO‑INGEST into Snowflake via Snowpipe. Snowflake table schema (tokens_billed):

    • tenant_id STRING
    • request_id STRING
    • model STRING
    • prompt_tokens INTEGER
    • completion_tokens INTEGER
    • embedding_tokens INTEGER
    • cost_usd FLOAT
    • recorded_at TIMESTAMP

  • Monthly chargeback query (example):

    SELECT tenant_id, SUM(prompt_tokens + completion_tokens + embedding_tokens) AS total_tokens, SUM(cost_usd) AS model_cost FROM tokens_billed WHERE recorded_at BETWEEN '2026-08-01' AND '2026-08-31' GROUP BY tenant_id;

  • Pinecone note: vector ops and storage are billed separately. Emit a separate metric pinecone_ops and include pinecone_cost_estimate in the same event.

H2: Soft vs Hard Token Quotas, and how to charge back

You must support both soft and hard quotas — customers expect predictable billing and graceful degradation.

Dimension Soft quota (recommended default) Hard quota (for abuse prevention)
Behavior Warn tenant when 80% of quota hit; continue serving Block further requests after exhaust unless overridden
Use case Upsell path, smooth UX for devs Prevent runaway bills and protect SLOs
Implementation Token window counter in Redis, email + webhook at 80/90/100% Cloud Functions gateway returns 429; increment incident metric
Billing Charge per token + monthly overage; show forecast Immediate suspension until payment/limit increase

Implementation notes:

  • Store tenant quota config in a central service (Firestore/Cloud SQL). Cloud Function reads config (cache in Redis) and applies counters.
  • Implement soft quota UX: return a 200 with X‑Warning headers plus a webhook to the tenant admin. Example header: X‑Airewrite‑Quota: 82%.
  • Hard quota path: return 429 with a machine‑readable body and link to upgrade. Track these rejections in Snowflake for auditing.
  • Chargeback metrics: invoice = model_cost_usd + pinecone_cost_usd + platform_fee. Keep raw cost and markups separate in Snowflake for transparency.

H2: Architecture, mirrored canaries, and redrive strategies

Use a small, auditable pipeline. Example stack: Vertex AI (inference) + Pinecone (vector store) + Cloud Functions gateway + Pub/Sub + Snowflake + Datadog. Use Cloud Tasks for deterministic retries and DLQs for manual redrive.

Architecture diagram (ASCII):

[Client] -> [Cloud Functions gateway] -> Pub/Sub (event copy) -> [Vertex AI / Pinecone]
                                      |-> Datadog metric
                                      |-> Pub/Sub (billing stream) -> Cloud Function -> GCS -> Snowpipe -> Snowflake
                                      |-> Pub/Sub DLQ -> Cloud Tasks replay (manual/muted)
  • Mirrored canaries: duplicate incoming request to a canary pipeline where the response is written to a canary table. Compare canary vs prod model on tokens, latency, and quality score. Promote only when no unexplained token increase >10%.
  • Redrive: keep the raw request in GCS for 30 days. Failed downstream writes go to a Pub/Sub DLQ. Use a Cloud Function that can reprocess a DLQ entry with idempotency (request_id dedupe) and a manual replay UI that triggers Cloud Tasks for controlled replay.

H2: Monitoring and incident playbooks

Datadog becomes your SRE control plane.

  • Key metrics: airewrite.tokens (sum by tenant), airewrite.requests.count, airewrite.request.errors, model_cost_estimate_usd (per minute), pinecone_ops. Tag by tenant_id, tier, and model.
  • Alerts to create:
    • Cost spike: tenant month‑to‑date spend > 3x daily average -> Pager (SRE) + Slack channel to account manager.
    • Token surge: tenant tokens/min > threshold (tenant config) for 5 minutes -> automatic soft throttle; if persists 30 minutes -> hard throttle.
    • Billing ingestion failure: Snowpipe lag > 10m -> Pager.
  • Incident playbook (token spike):
    1. Datadog alert fires; SRE verifies tenant and recent requests in Snowflake.
    2. If spike is legit (marketing campaign), notify account team and raise quota. If not, activate hard throttle and notify tenant admin via webhook + email.
    3. Postmortem: add rule to mirrored canary if the spike came from a new prompt template.

H2: Operational tips and pitfalls

  • Don’t assume Vertex logs tokens for you — count tokens at the gateway using the tokenizer for the model family (or use provider cost hooks) and store both prompt/completion counts.
  • Pinecone costs scale with vector count and queries — separate that telemetry and report it on invoices.
  • Keep pricing transparent: show model_cost, infra_fee, and your margin line items. Customers will accept markup if it’s auditable.
  • Automate refunds: if a billing audit finds overcharge, refund the model_cost_usd only — your platform fee stays unless you decide otherwise.

Conclusion & CTA

You don’t need a huge stack to make Airewrite billable and auditable — you need per‑tenant SLOs as product features, token‑accurate telemetry, soft/hard quotas, mirrored canaries, and a reliable redrive path. Instrument at the gateway, stream events to Snowflake for invoicing, and surface the same metrics in Datadog for SRE and account teams.

We build and ship these patterns — from MLOps for production AI to chargeback plumbing that ties into CRM and billing. If you’re turning Airewrite into a revenue line, you should treat product ops as an engineering deliverable, not a spreadsheet.

Need help with turning Airewrite into a billable, auditable multi-tenant service? Book a free strategy call with Niche.dev.

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