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Module 11 · Page 12 of 20 · 10 sections

Reliability, Fault Tolerance and High Availability

Keeping an AI system running when parts of it fail: failure scenarios, reliability techniques, fault tolerance strategies and an HA architecture.

05Model Serving and Inference Architecture10System Components Deep Dive13Monitoring, Logging and Observability
11.1

1. Introduction

  • Reliability, fault tolerance and high availability are critical for AI systems, especially in production, where users expect always-on, accurate and fast responses.
  • AI systems involve multiple components (models, databases, vector stores, APIs, etc.), so any component failure can impact the entire system.
11.2

2. Key Terms

1
Reliability
System works correctly over time.
2
Fault
An error or failure in a component.
3
Fault Tolerance
Ability to continue working despite failures.
4
High Availability (HA)
System is available most of the time with minimal downtime.
5
Uptime
Percentage of time the system is available.
6
Downtime
Time when the system is unavailable.
7
SLA
Service Level Agreement: the expected uptime, e.g. 99.9%.
11.3

3. Why is it Important in AI Systems?

  • AI systems serve real users and business-critical applications.
  • Model inference, vector database and external APIs can fail.
  • High availability ensures continuous service.
  • Fault tolerance improves user experience by handling failures gracefully.
  • Essential for enterprise and large-scale applications.
11.4

4. Common Failure Scenarios

  1. 1Model server down.
  2. 2Vector database not reachable.
  3. 3External API failure (LLM provider, web search, etc.).
  4. 4High traffic (traffic spike).
  5. 5Network issues or timeout.
  6. 6Data pipeline failure (ingestion).
  7. 7Hardware failure (server / disk).
  8. 8Region or data center outage.
11.5

5. Techniques for Reliability

1
Retry Mechanism
Retry failed requests with exponential backoff.
2
Timeout
Set proper timeouts for external calls.
3
Circuit Breaker
Stop calling a failing service.
4
Fallback
Use a backup model or cached response.
5
Graceful Degradation
Provide limited functionality instead of complete failure.
6
Monitoring & Alerting
Detect and fix issues early.
11.6

6. Fault Tolerance Strategies

1
Replication
Run multiple instances of services.
2
Auto-restart
Automatically restart failed services.
3
Health Checks
Continuously check service health.
4
Self-healing
Replace unhealthy instances.
5
Stateless Design
Avoid storing state in a single server.
6
Idempotent APIs
Handle duplicate requests safely.
7
Data Redundancy
Replicate databases and vector stores.
8
Queue-based Processing
Use message queues (Kafka, SQS, etc.) to handle temporary failures.
11.7

7. High Availability Architecture

ClientsWeb / mobile
Load BalancerNginx / AWS ALB
API ServersMultiple instances (API-1, API-2, API-3)
Model ServersMultiple instances (Model-1, Model-2, Model-3)
Cache · Vector Database (replicated) · Database (primary + replica)
11.8

8. Fault Tolerance Flow (Example)

User Request
Call Model API
Success?
Return Response
RetryWith backoff
Call Backup Model
Use Cached Response
Return Fallback Response
If the call succeeds the response returns; otherwise retry, then a backup model, then a cached response.
11.9

9. Best Practices

  1. 1Use multiple instances for all critical services.
  2. 2Implement health checks and auto-restart.
  3. 3Use a load balancer to distribute traffic.
  4. 4Use caching to reduce load on the LLM and vector DB.
  5. 5Implement retries, timeouts and circuit breakers.
  6. 6Use monitoring tools (Prometheus, Grafana, etc.).
  7. 7Design for graceful degradation.
  8. 8Test failure scenarios (chaos testing).
11.10

10. Real-world Examples

1
OpenAI / Llama
Multiple regions and auto-scaling.
2
Pinecone
High availability vector database.
3
AWS
Multi-AZ deployment for fault tolerance.
4
Netflix
Circuit breaker pattern and monitoring (Hystrix).
5
Uber
High availability microservices architecture.