[Launch] API Rate Limiting & Throttling

[chrizbo]

Overview

Introduce a robust rate limiting and throttling system for all public API endpoints. This launch delivers per-plan rate limit tiers, a token bucket enforcement engine, and a self-serve dashboard so customers can monitor their usage in real time.

Success Criteria

• Rate limits enforced at the gateway for all API keys
• Customers can view current usage and remaining quota in the dashboard
• Exceeded limits return standard 429 Too Many Requests with Retry-After header
• Configurable limits per billing plan (Free / Pro / Enterprise)

Dependencies

• Platform Reliability & Experience initiative (#74)
• API Gateway team availability

Rollout Plan

Phase rollout: Team → Alpha → Beta → GA
Related to #74

Generated by Sample Data Simulator · ● 1.7M · ◷

[chrizbo]

Planning update: We're targeting end of next sprint for the alpha rollout. The gateway team said they can prioritize this if we keep the scope tight. Redis cluster should handle the token bucket at our current scale — we'll just need to add a few new keys. Dashboard can reuse the existing billing components, so that should be straightforward.

[github-actions]

🔍 Assumptions Worth Discussing

I found 5 assumptions in this planning update that might be worth making explicit. These aren't criticisms — they're invitations to align.

🔴 High Risk

1. Gateway Team Prioritization Is Confirmed

"The gateway team said they can prioritize this if we keep the scope tight."

The assumption: The gateway team has formally committed to this timeline, and their definition of "scope tight" matches ours.

Why it matters: "Can prioritize" is conditional — it depends on their other commitments staying stable. If their backlog shifts or their interpretation of "tight scope" differs, this could block the alpha entirely.

Question for the team: Has the gateway team confirmed a specific sprint commitment in writing, and do we have a shared definition of what "tight scope" means for this integration?

---

2. Alpha Rollout Fits Within Next Sprint

"We're targeting end of next sprint for the alpha rollout."

The assumption: All work — gateway integration, Redis configuration, dashboard changes, and testing — can be completed within one sprint with current team capacity.

Why it matters: The issue lists multiple distinct workstreams (enforcement engine, dashboard, gateway). A sprint deadline without an explicit capacity and task breakdown is a common source of missed targets.

Question for the team: Has the work been broken down and sized against available capacity for this sprint, accounting for any existing commitments?

---

🟡 Medium Risk

3. Redis Cluster Scales for Token Bucket at Current Load

"Redis cluster should handle the token bucket at our current scale — we'll just need to add a few new keys."

The assumption: The existing Redis cluster has sufficient headroom in throughput, memory, and key-space to support token bucket operations across all API keys without performance degradation.

Why it matters: Token bucket enforcement can be write-heavy at scale (every API call touches Redis). "A few new keys" understates the actual access pattern — it's potentially one key per API key per time window. If the cluster is near capacity, this could introduce latency across unrelated systems.

Question for the team: Has anyone benchmarked the expected Redis operation rate against current cluster utilization, or consulted the Platform Reliability team (#74) about headroom?

---

4. Billing Components Compose Into the Usage Dashboard

"Dashboard can reuse the existing billing components, so that should be straightforward."

The assumption: The billing UI components are sufficiently generic and well-documented to be adapted for real-time quota/usage display without significant rework.

Why it matters: Billing components are often tightly coupled to billing data models and refresh cycles. Rate limit dashboards need near-real-time data (or polling), which may require different data-fetching patterns than billing's batch/event-driven model.

Question for the team: Have the frontend owners of the billing components confirmed they're reusable here, and is the data refresh rate compatible with usage monitoring expectations?

---

🟢 Low Risk

• Scope containment keeps gateway team available: "if we keep the scope tight" implies scope is fully within our control — but scope creep often emerges from discovered complexity, not deliberate expansion. → Who owns scope decisions if new requirements surface mid-sprint?

---

🤔 Things Not Mentioned

These aren't in the comment, but might be worth considering:

• 429 response contract with existing clients — The issue mentions returning 429 Too Many Requests, but there's no mention of client communication or SDK updates. Existing integrations may not handle this gracefully. Who owns the client-side impact assessment?
• Per-plan limit configuration ownership — Free/Pro/Enterprise tiers are in the success criteria, but who sets and maintains these values? A missing owner here could delay GA even if alpha ships on time.
• Observability for the token bucket — No mention of alerting or metrics for rate limit enforcement. If something misbehaves in alpha, will the team have enough signal to diagnose it quickly?

---

🧩 This comment was generated by the Assumption Surfacer workflow to help the team reason together. Reply to any assumption above to share your perspective — that's how shared understanding forms.

---

🧾 Workflow Run Cost

Metric	Value
Input tokens	3,241
Output tokens	812
Total tokens	4,053
Premium requests	1
Estimated cost	$0.04

Cost estimate based on current Copilot pricing. Actual billing may vary.

Generated by Assumption Surfacer for issue #93 · ● 111.4K · ◷