Managed Redis

Assistance-operated Redis for cache, sessions, queues, rate limits, and real-time data paths

Managed Redis is for teams using Redis as a production dependency: cache, session store, rate limiter, lightweight queue, pub/sub channel, or real-time data structure. Assistance operates the Redis runtime so your engineers can focus on application behavior and performance.

Best-fit use cases#

Use case	Why Redis fits
Application cache	Reduce database pressure and improve read latency for hot data
Session storage	Centralize session state for horizontally scaled applications
Rate limiting	Track counters and windows with low-latency operations
Queues and background jobs	Use lists, streams, or sorted sets for lightweight work coordination
Real-time features	Pub/sub, leaderboards, counters, and ephemeral state

What Assistance operates#

Area	Included managed service responsibility
Provisioning	Redis topology selection, memory sizing, network placement, secure defaults, and connection details
Reliability	Sentinel or Cluster design, replication health, failover procedure, persistence choices, backup options, and runbooks
Performance	Latency, memory, command, slow log, eviction, and connection monitoring
Maintenance	Patch planning, version lifecycle guidance, maintenance windows, and controlled restarts
Security	TLS options, ACLs, password/secret rotation support, network isolation, and audit-friendly access practices
Observability	Dashboards and alerts for memory pressure, latency, replication lag, persistence errors, and connection saturation
Support	Severity-based support for platform incidents and escalation for covered production services

Redis behavior is a shared design decision

Assistance operates Redis, but your application owns key design, TTLs, cache invalidation, queue semantics, and whether data can be safely evicted. We help choose safe platform defaults, but application data semantics must be explicit.

Ownership boundary#

Responsibility	Assistance owns	Customer owns
Runtime operations	Configure, monitor, patch, scale, back up where applicable, and operate Redis	Client usage, connection pooling, key design, and command patterns
Durability	Implement agreed persistence and backup approach	Decide which data is cache-only, recoverable, or business-critical
Eviction	Configure selected policy and monitor pressure	Define TTL strategy and acceptable behavior when memory fills
Availability	Operate Sentinel/Cluster/failover inside agreed topology	Application retry behavior and idempotency for Redis-dependent flows
Access	ACL model and rotation procedure	User approval, service credentials in applications, internal reviews

Deployment options#

Option	When to use it
Assistance physical servers	Development, staging, CI, internal tools, and predictable cache/session workloads
Customer cloud account	Production workloads needing low latency to application services or cloud network controls
Cache-only deployment	Performance optimization where data can be rebuilt from a source of truth
Durable Redis deployment	Queues, sessions, or stateful workflows that need persistence and recovery expectations

Topology choices#

Topology	Use when
Single node	Development, test, or non-critical cache-only workloads
Sentinel	You need HA failover for a single primary with replicas and manageable memory size
Redis Cluster	You need horizontal scale, higher write throughput, or data larger than a single node comfortably supports
Read replicas	You need read scaling or isolation for reporting/analytics-style consumers

Reliability and support model#

Topic	Managed Redis approach
Availability	Defined by topology and support tier; not all cache-only environments require production SLA language
Persistence	RDB, AOF, hybrid, or no persistence selected based on data semantics
Recovery	Restore expectations documented where persisted Redis is part of production recovery
Performance	Memory pressure, latency, and eviction are monitored because Redis failure is often capacity-driven
Response	P1 response targets scoped in the support plan; 24/7 critical response available for covered production services

Onboarding#

1. Workload classification#

We classify Redis usage: cache, session, rate limit, queue, pub/sub, streams, or mixed. This determines durability, topology, and operational risk.

2. Configuration design#

Assistance defines memory sizing, persistence, eviction policy, topology, network access, ACLs, monitoring, and support model.

3. Integration and migration#

We provision Redis, provide connection details, help migrate keys where needed, and validate application connection pooling and timeout behavior.

4. Operate#

We monitor latency, memory, command rates, replication, persistence, and failover signals. Capacity changes are planned before memory pressure becomes an outage.

Not included by default#

Designing every application cache key and invalidation path
Rewriting queue consumers or job processing logic
Guaranteeing durability for data declared cache-only
Unlimited memory, node count, or retention outside the plan
Debugging application-level race conditions unless scoped separately

Managed PostgreSQL — Source-of-truth relational database commonly paired with Redis
Managed MySQL — Relational backend for cache and session-heavy applications
Managed Prometheus — Metrics and alerting for cache hit ratio, latency, and saturation
SRE as a Service — Incident response, SLOs, and runbooks around Redis-dependent systems

Getting started#

Request a Redis assessment. We will classify your workload, define data durability expectations, choose Sentinel or Cluster where needed, and document ownership and support boundaries.

Request Redis assessment →

Frequently asked questions#

Should we use Redis Sentinel or Redis Cluster? Sentinel is usually simpler for HA around one primary. Cluster is appropriate when data size or throughput needs horizontal partitioning. We choose based on workload and growth, not default complexity.

Can Redis be used for queues? Yes, but queue semantics must be explicit. Redis Streams can be a good fit for lightweight queues, while Kafka is usually better for durable replayable event streams.

Is Redis data always persisted? No. Cache-only workloads may disable persistence for performance. Sessions, queues, or stateful workflows usually need persistence and recovery expectations.

Who owns cache invalidation? Your application team owns invalidation logic and TTL choices. Assistance monitors and operates the Redis platform and can advise on safe patterns.

What happens when memory fills? The agreed eviction policy applies. We monitor memory pressure and recommend capacity or TTL changes before Redis reaches unsafe saturation.