LLM Production
LLM Disaster Recovery β Ensuring Business Continuity
LLM systems face unique failure modes: provider outages, model degradation, safety incidents, and capacity constraints. Robust disaster recovery planning ensures service continuity.
- Failover Strategies β Multi-provider, multi-region, and fallback chains
- Backup Models β Alternative models for graceful degradation
- Recovery Procedures β Automated failover and manual recovery playbooks
Hope for the best, plan for the worst.
LLM Disaster Recovery
LLM systems have unique failure modes that traditional disaster recovery plans don't address. Model providers can go down, safety incidents can require immediate model retirement, and capacity constraints can throttle service. This guide covers comprehensive disaster recovery for LLM systems.
Failure Modes
Provider-Level Failures
| Failure Type | Impact | Recovery Time |
|---|---|---|
| Complete outage | All requests fail | Minutes to hours |
| Rate limiting | Partial request failure | Seconds to minutes |
| Quality degradation | Incorrect outputs | Unclear until detected |
| Cost spike | Budget overrun | Immediate (financial) |
Model-Level Failures
Infrastructure Failures
Failover Strategies
Multi-Provider Architecture
Failover Chain Architecture:
Fallback Models
Fallback Strategy Matrix:
| Primary | Fallback | Trigger | Quality Impact |
|---|---|---|---|
| GPT-4 | GPT-3.5 | Latency > 5s | Medium |
| Claude Opus | Claude Sonnet | Rate limited | Low |
| Self-hosted 70B | Self-hosted 7B | GPU failure | High |
| Any API | Self-hosted | Provider down | Medium-High |
Graceful Degradation
Degradation Tiers:
| Tier | Condition | Behavior |
|---|---|---|
| Full | All systems operational | Full model capability |
| Degraded | Partial outage | Smaller model, shorter responses |
| Minimal | Major outage | Cached responses, rule-based fallback |
| Emergency | Complete failure | Maintenance message, retry queue |
Recovery Procedures
Automated Recovery
Circuit Breaker Pattern:
Manual Recovery Playbook
Incident Response Steps:
- Detect: Automated monitoring triggers alert
- Triage: Assess severity (P0-P3) and impact
- Mitigate: Activate failover, reroute traffic
- Investigate: Root cause analysis
- Recover: Restore primary service
- Post-mortem: Document and improve
Backup and State Management
Conversation Backup
Configuration Backup
Business Continuity
Recovery Time Objectives
Recovery Point Objectives
| Component | RTO Target | RPO Target |
|---|---|---|
| API routing | < 30 seconds | 0 (stateless) |
| Conversation history | < 5 minutes | < 1 minute |
| Model serving | < 5 minutes | 0 (stateless) |
| User preferences | < 15 minutes | < 5 minutes |
Practice Exercises
-
Conceptual: Design a multi-provider failover architecture for a customer service chatbot that uses GPT-4 as primary and must maintain 99.9% availability.
-
Mathematical: Calculate the expected monthly downtime for an LLM service with three independent providers, each with 99.5% uptime, using a failover architecture.
-
Practical: Create a disaster recovery runbook for a safety incident where the primary model must be immediately retired and replaced with a backup.
-
Research: Compare the cost-effectiveness of multi-provider redundancy versus self-hosted backup models for disaster recovery.
What to Learn Next
-> LLM Serving Architectures vLLM, TGI, TensorRT-LLM, and serving patterns for production deployments.
-> LLM Monitoring and Observability Logging, tracing, metrics, and drift detection for production systems.
-> LLM Versioning and Rollouts Model versioning, artifact management, and gradual rollout strategies.
-> LLM Security Best Practices Protecting systems from adversarial attacks and data privacy risks.
-> Multi-Tenant LLM Systems Tenant isolation, resource sharing, and customization at scale.
-> LLM Evaluation in Production Online evaluation, user feedback loops, and quality assurance.