This repository explores the platform architecture required to operate large-scale AI inference systems reliably under burst traffic and multi-tenant workloads. The focus is on platform reliability mechanisms such as admission control, fairness scheduling, bounded queues, and latency protection rather than model development.
The project models the control-plane mechanisms required to operate a multi-tenant inference platform reliably under unpredictable workloads.
Modern AI applications rely on large language model inference services that must serve highly variable requests while maintaining predictable latency.
Unlike traditional web services, inference workloads vary widely depending on prompt size, context retrieval, and generation length.
This lab focuses on platform-level architecture mechanisms used to maintain system stability and predictable latency under burst traffic.
Large inference systems face several operational challenges:
- protecting latency SLOs under burst traffic
- isolating tenants to prevent noisy-neighbor effects
- handling variable request cost
- maintaining predictable system behavior under overload
This repository demonstrates architectural mechanisms used in real inference platforms to address these challenges.
The platform is designed to achieve the following objectives:
- maintain predictable latency under burst traffic conditions
- isolate tenants through fairness scheduling
- prevent latency collapse using bounded queues
- degrade gracefully when inference capacity is saturated
- expose internal system behavior through observability metrics
The platform is designed around the following operational properties:
| Property | Description |
|---|---|
| Latency Protection | Admission control and bounded queues prevent latency collapse during bursts |
| Fairness | Tenant-aware scheduling ensures one tenant cannot starve others |
| Predictable Overload Behavior | The system sheds excess traffic instead of buffering requests indefinitely |
| Graceful Degradation | Retrieval budgets and generation limits can be reduced under load |
| Observability | Metrics expose queue depth, rejection rate, and latency distributions |
The system models a platform designed to protect latency targets such as:
| Metric | Target |
|---|---|
| p95 latency | < target threshold |
| queue depth | bounded |
| failure mode | fast fail or degraded response |
The architecture prioritizes latency protection over maximum throughput.
This lab models several architectural mechanisms used in large-scale inference systems:
- distributed inference routing
- retrieval-augmented generation pipeline
- admission control at the platform boundary
- bounded queues and backpressure
- fairness scheduling across tenants
- graceful degradation under overload
- observability of platform behavior
The goal is to explore platform reliability and behavior, not model optimization.
The platform simulates a distributed inference system composed of routing, retrieval, and inference layers.
Requests enter through a routing layer that enforces admission control, tenant isolation, and fairness policies before interacting with retrieval and inference services.
The architecture separates control-plane responsibilities (routing, scheduling, and admission control) from execution-plane components (retrieval and inference workers) to maintain predictable latency under burst traffic.The platform simulates a distributed inference system composed of routing, retrieval, and inference layers.
Requests enter through a routing layer that enforces admission control and fairness policies before interacting with retrieval and inference services.
flowchart TD
A[Client / Tenant]
B[Router API<br/>Admission Control<br/>Rate Limits]
C[Fairness Scheduler<br/>Tenant Queues]
D[Retrieval Service<br/>Vector Search]
E[Inference Worker Pool<br/>Batching / Execution]
F[(PostgreSQL + pgvector)]
G[(Redis Cache)]
H[(Prometheus Metrics)]
A --> B
B --> C
B --> D
D --> F
D --> G
C --> E
B --> H
D --> H
E --> H
The platform protects latency SLOs using admission control and bounded queues.
The architecture implements several core platform mechanisms to protect latency and ensure predictable system behavior under burst traffic:
- Admission control – deadline-aware request admission to protect latency SLOs
- Fairness scheduling – tenant-aware request queues to prevent noisy-neighbor effects
- Bounded queues – controlled queue sizes to avoid latency collapse under overload
- Retrieval latency budgeting – limiting retrieval work to preserve inference deadlines
- Inference batching – grouping requests to improve worker throughput
- Graceful degradation – reducing workload cost when capacity is constrained
- Observability – system metrics exposing queue depth, latency, and rejection rates
The platform exposes several tunable controls that influence system behavior under load.
| Control | Description |
|---|---|
| MAX_QUEUE_SIZE | Maximum requests waiting for inference |
| MAX_BATCH_SIZE | Maximum batch size for inference workers |
| BATCH_TIMEOUT_MS | Maximum batching delay before execution |
| RETRIEVAL_BUDGET_MS | Maximum time allowed for retrieval operations |
| ADMISSION_SAFETY_MARGIN | Buffer applied to latency SLO when admitting requests |
| CACHE_TTL_S | Lifetime of cached retrieval results |
The architecture prioritizes predictable latency and system stability over maximum throughput. Several key design tradeoffs were made to achieve this behavior.
The platform favors early request rejection over buffering excess traffic.
Large queues can temporarily absorb burst traffic but lead to severe tail latency and wasted compute resources when requests exceed client timeouts. By enforcing bounded queues and admission control, the system preserves responsiveness for admitted requests.
The platform models fairness scheduling using Deficit Round Robin (DRR).
More mathematically precise algorithms such as Weighted Fair Queuing (WFQ) provide stronger theoretical fairness guarantees but introduce higher scheduling overhead. DRR offers practical fairness with constant-time scheduling, which is better suited for latency-sensitive gateway systems.
In distributed inference systems, enforcing strict global quota checks would introduce additional latency on every request.
Instead, the platform favors local decision-making with bounded inconsistencies. Admission and fairness decisions occur locally at the gateway to keep the request path fast and resilient.
Under heavy load, the system reduces workload cost before rejecting traffic.
Examples include limiting retrieval depth, truncating context, or capping generation length. This approach preserves system availability while accepting temporary reductions in response fidelity.
The platform exposes detailed metrics for queue depth, admission decisions, latency distributions, and degradation behavior.
Although metrics collection introduces small overhead, it enables operators to understand system behavior and detect overload conditions before they escalate into failures.
The platform was load tested using Locust to simulate burst traffic scenarios.
Load tests evaluate:
- queue saturation behavior
- latency distribution under load
- admission control effectiveness
- degradation behavior during overload
Metrics were collected using Prometheus.
Example results:
| Scenario | Users | RPS | p95 Latency | Failure Rate |
|---|---|---|---|---|
| Baseline | X | X | X | X |
| Burst | X | X | X | X |
A detailed architecture description is available in the repository: docs/architecture/ AI Inference Platform Architecture.
This document provides a TOGAF-style architecture description including:
- architecture principles
- application architecture
- data architecture
- technology architecture
- deployment model
- architecture tradeoffs
Key architectural decisions are documented as Architecture Decision Records (ADRs):
- ADR 001 – Admission Control Strategy
- ADR 002 – Bounded Queues
- ADR 003 – Multi-Tenant Fairness Scheduling
- ADR 004 – Graceful Degradation Strategy
Run the platform locally:
docker compose up --buildThe system starts the router, retrieval service, inference worker, data stores, and metrics stack.
Configuration defaults are documented in .env.example.
Copy this file to .env to override runtime settings locally.
Each service exposes metrics for system behavior analysis.
Metrics include:
- request latency
- queue depth
- admission rejection rate
- degradation events
Metrics are collected using Prometheus.
Services expose both liveness (/health) and readiness (/health/ready) endpoints, and Docker Compose health checks enforce dependency-aware startup ordering across the stack.
This lab models several architectural behaviors of AI inference platforms, but it intentionally simplifies some aspects of production systems.
The inference worker simulates model latency rather than executing real GPU-backed inference.
This allows the platform behavior—admission control, queueing, and scheduling—to be evaluated independently of model performance.
The current implementation runs in a single-region environment.
Production inference platforms typically operate in active-active multi-region deployments with regional routing and failover.
The scheduler models fairness behavior conceptually but does not yet implement a full production-grade multi-tenant scheduling algorithm such as hierarchical DRR or WFQ.
Request cost is estimated using simplified heuristics rather than real token accounting.
Production inference platforms track precise token usage and GPU memory consumption.
The retrieval layer includes caching behavior, but a full semantic cache with embedding similarity lookup is not implemented in this version of the lab.
The platform is designed to fail in controlled and predictable ways under stress conditions.
When inference queues reach their configured capacity, new requests are rejected immediately.
This prevents queue buildup and protects latency for admitted requests.
Client behavior:
Requests receive HTTP 429 or 503 responses and should retry with backoff.
If retrieval exceeds its latency budget, the system falls back to degraded behavior.
Fallback options include:
- serving cached responses
- reducing retrieval context
- skipping retrieval entirely
This prevents retrieval latency from propagating to inference workers.
If inference workers reject requests due to queue pressure, the router may retry once with reduced context.
If the retry also fails, the request is rejected.
This protects the inference worker pool from cascading overload.
Under sudden traffic spikes, the admission control layer sheds excess requests.
This ensures that admitted requests maintain acceptable latency rather than being delayed by large queues.
If observability systems (e.g., Prometheus) fail, request processing continues normally.
Metrics collection is intentionally isolated from the request path so that monitoring outages do not impact service availability.
Potential extensions to this lab include:
- hierarchical fairness scheduling
- deadline-aware admission policies
- semantic caching layers
- GPU-backed inference workers
- multi-region inference routing
- advanced batching strategies
Related Architecture Notes
distributed-systems-notes