The Real-Time Data Layer
Redis is an open-source, in-memory data structure store used as a database, cache and message broker. By keeping data in RAM, it delivers sub-millisecond reads and writes at massive scale.
Beyond simple key-value pairs, Redis supports rich data types — strings, hashes, lists, sets, sorted sets, streams and more — with atomic operations, persistence, replication and clustering. It powers the real-time layer behind many of the world's busiest applications.
Sub-Millisecond Latency
In-memory storage delivers reads and writes in microseconds, even under heavy concurrency.
Rich Data Structures
Strings, hashes, lists, sets, sorted sets, streams and more — far beyond plain key-value.
Persistence & HA
RDB/AOF persistence plus Sentinel and Cluster for replication and automatic failover.
Built for Speed at Scale
The capabilities that make Redis the default choice for real-time workloads.
In-Memory Speed
Data lives in RAM for microsecond reads and writes — orders of magnitude faster than disk-based stores.
Rich Data Types
Strings, hashes, lists, sets, sorted sets, bitmaps, streams and geospatial indexes out of the box.
Pub/Sub Messaging
Lightweight publish/subscribe and Streams for real-time event fan-out and message queues.
Persistence
Durable RDB snapshots and AOF logging so your in-memory data survives restarts and failures.
High Availability
Redis Sentinel and Redis Cluster provide replication, sharding and automatic failover.
Atomic Operations
Single-threaded command execution and transactions keep updates atomic and race-free.
End-to-End Redis Services
From caching layers to fully managed Redis clusters.
Caching Layer
Caching that takes load off your database - AI drafts the cache-aside code and invalidation tests, a developer reviews them before release.
Session Store
Fast, scalable session and token storage for high-traffic web and mobile back-ends.
Real-Time Leaderboards
Sorted sets power live leaderboards, rankings and counters that update instantly.
Rate Limiting & Queues
Robust rate limiting, job queues and pub/sub messaging built on Redis primitives.
Cluster Setup
Design and deploy sharded, highly-available Redis Cluster and Sentinel topologies.
Managed & Tuning
Ongoing monitoring, memory tuning, persistence config and managed Redis support, scoped to your needs.
Caching That Stays Correct
Most Redis projects start as a cache, and most cache problems are really invalidation problems. The usual pattern is cache-aside: the application reads from Redis, falls back to the database on a miss and writes the result back with a time-to-live. Write-through and write-behind keep the cache updated on every write instead, at the cost of more moving parts. The right choice depends on how stale each piece of data is allowed to be.
We map that tolerance per entity before writing code. Short TTLs suit prices and stock levels; longer TTLs with explicit deletes on update suit profiles and catalogue pages. We also plan for the failure modes that catch teams out — cache stampedes when a hot key expires (handled with request coalescing, jittered TTLs or early refresh), unbounded key growth from unversioned key names, and serialized objects that break when a data model changes.
How Redis Uses Memory and Disk
Redis offers two persistence mechanisms. RDB writes point-in-time snapshots that are compact and quick to restore, but writes made since the last snapshot can be lost. AOF logs every write and, depending on the fsync policy, narrows that window at the cost of more disk I/O and larger files. Many production setups combine both, while a pure cache may need neither. We set persistence according to what the data is — a rebuildable cache, a session store, or the primary home of queues and streams.
Memory is the main cost driver, so the eviction policy matters. Policies such as allkeys-lru or allkeys-lfu suit caches, volatile policies only evict keys that carry a TTL, and noeviction makes writes fail when memory is full — the right behaviour for data you cannot afford to lose silently. We size maxmemory with headroom for replication buffers and the copy-on-write overhead of snapshots, and choose compact encodings, such as small hashes instead of many separate string keys, to keep the footprint down.
Replication, Clustering and Where to Run It
A primary with replicas and Redis Sentinel gives automatic failover for most workloads. Redis Cluster adds sharding across 16,384 hash slots when one node can no longer hold the dataset or absorb the write load — but multi-key commands, transactions and Lua scripts then need related keys in the same slot via hash tags, which shapes key design from day one. Replication is asynchronous, so a failover can drop the most recent writes; we design around that rather than ignore it.
Managed services such as Amazon ElastiCache, Azure Managed Redis, Google Cloud Memorystore and Redis Cloud take patching and failover off your plate, while self-hosting on VMs or Kubernetes gives more control and can cost less at scale. Redis licensing has changed more than once in recent years, and Valkey, the Linux Foundation fork, is now offered by several cloud providers. We review licence terms and command compatibility for each option against your use case before you commit.
Pixel-perfect software, delivered at AI speed
PixoBots stands for Pixel & Bots. Our Bots are dedicated developers who work with AI tools: AI takes the repetitive work, a developer reviews every line, and the result is pixel-perfect.
Pixel
Polished UI and clean, tested code - detail is part of the job, not an afterthought.
Bots
Dedicated developers who join your team and use AI for boilerplate, tests and documentation.
Savings
AI-assisted delivery can save more than 50% of development cost compared with traditional development.
Ready to Go Real-Time?
Let our engineers add a fast in-memory Redis layer to your stack — talk to us today.