When I started working on Atlas, my goal was simple: make LLM serving more predictable and fair. What I didn’t realize at first is just how central quota management would become to everything—from reliability to cost control to user experience.
Why Quota Management Matters
LLM workloads aren’t like traditional web traffic. A single request can vary from a tiny prompt to a massive, multi-page input that ties up GPUs for seconds. Without quotas, heavy users or sudden bursts can easily starve everyone else.
That’s why quota-aware serving is not just an optimization—it’s survival.
Atlas was built as a quota-aware LLM traffic gateway that sits in front of inference servers (like vLLM or Triton). It enforces tenant limits in real time, prioritizes requests based on subscriptions, and gives clear feedback when limits are hit.
The Architecture Challenge
Building Atlas meant solving several interconnected problems:
Traffic Management
- Burst handling: How to handle sudden spikes without overwhelming backends
- Fair queuing: Ensuring one tenant doesn’t monopolize shared resources
- Smart routing: Balancing fast routing with accurate quota checks
Observability & Control
- Real-time metrics: Teams needed to see why a request was throttled
- Clear feedback: Users should know what tier they need to upgrade to
- Debugging: Quota mismatches required deep metrics and tracing
Technology Stack & Design Decisions
After evaluating various approaches, we settled on:
Core Infrastructure:
- Redis - Millisecond-level quota tracking with atomic operations
- FastAPI - Lightweight, async API gateway with excellent performance
- Prometheus & Grafana - Comprehensive metrics and live dashboards
Quota Management:
- Tiered quotas supporting different service levels (free, pro, enterprise)
- Token-based limits with both rate and volume controls
- Priority queuing based on subscription tiers
These choices made Atlas both fast and transparent. The combination of Redis for state and FastAPI for routing kept latency under 10ms for quota checks.
Key Lessons Learned
Building Atlas in production taught me several crucial lessons:
1. Idempotency is Everything
Retries without proper safeguards don’t just double your load—they can create exponential amplification. We learned this the hard way when a client library bug caused retry storms that brought down our test environment.
Solution: Implemented request deduplication using Redis with a sliding window approach.
2. Quotas Must Be Dynamic
Static quotas break down quickly in the real world. Different models have vastly different costs, and hardware capacity changes throughout the day.
Solution: Built adaptive quotas that adjust based on model pricing and current system load.
3. Observability Pays for Itself
The time invested in comprehensive metrics, logs, and traces paid for itself many times over. When quota disputes arose, we could show exactly what happened and when.
Key metrics we tracked:
- Request latency by tier and model
- Quota utilization per tenant
- Queue depth and processing time
- Cost attribution and billing accuracy
4. Start Simple, Scale Smart
Our MVP was deliberately minimal—just basic rate limiting with Redis. This let us catch edge cases early before scaling to millions of requests.
Evolution path:
- Basic rate limiting → Token buckets
- Single-tier → Multi-tier quotas
- Static limits → Dynamic scaling
- Manual billing → Automated cost tracking
Performance Results
After six months in production, Atlas delivered:
- < 10ms quota check latency (99th percentile)
- 99.9% uptime during normal operations
- 80% reduction in quota-related support tickets
- Fair resource distribution across 50+ internal teams
What’s Next
Atlas is now powering our internal AI demos and experiments, but the vision extends further. We’re working on:
- Hyperion integration - Full ML inference platform with smarter scheduling
- Cost-aware routing - Automatically route requests to the most cost-effective model
- Predictive scaling - Use historical patterns to pre-scale capacity
- Cross-region quotas - Global quota enforcement for distributed teams
Final Thoughts
Building Atlas taught me that infrastructure isn’t just about keeping systems alive—it’s about governing fairness, cost, and reliability at scale.
The most important insight? Good infrastructure makes the right thing easy and the wrong thing hard. When quota management is transparent and automatic, teams naturally build more efficient applications.
That lesson will shape every system I build going forward.