How AdTalos cut mobile ad latency by 5x and halved infrastructure costs with Aerospike

AdTalos is a mobile advertising company specializing in intelligent ad placement and mobile media monetization. Operating primarily on the Android platform, AdTalos manages and resolves identities across 4 billion device IDs in real time to support full-scenario ad placement and monetization for brands and publishers.

AdTalos runs one of the most latency-sensitive workloads in data infrastructure. Every ad placement decision requires resolving device identities across a 4 billion device dataset within strict time limits. Each bid resolution depends on multiple lookups, and as the number of operations per request grows, the probability that at least one lands in a slower part of the latency distribution compounds. A decision that arrives after the bid window closes has failed, regardless of its accuracy. AdTalos was running a Redis cloud cluster to serve this workload and encountered three compounding problems that threatened both service quality and cost control.

Memory overhead made costs unsustainable

The Redis cloud cluster required 750 GB of memory across replicas to support AdTalos' data volume. Billing was based on replicated rather than native data volume, making capacity planning opaque. As data grew, projected costs were well beyond budget, with no efficient path to scale.

Restart windows created operational exposure

Reloading data after a restart took several hours. For a system serving live advertising traffic, this was a direct connection between routine maintenance and service disruption. The team had no reliable way to perform restarts without accepting significant downtime risk.

Limited support left the team without guidance

The Redis cloud service provided no direct technical partnership. Cluster management, scaling decisions and deployment guidance were handled entirely in-house, without access to expertise matched to the scale and complexity of their workload.

AdTalos evaluated Aerospike as a direct replacement for their Redis cloud cluster, with three requirements: read latency under 5 ms, reduced memory usage and reliable support for quick restarts. Aerospike met all three. The initial deployment, using Aerospike's shared-nothing architecture and gossip-based cluster configuration, was completed in one to two days.

Near-in-memory performance without the memory cost

Aerospike's Hybrid Memory Architecture keeps the primary index in RAM while storing record data on SSD/NVMe. This allowed AdTalos to serve 4 billion device ID lookups at 0.5 ms read latency with 290 GB of memory, down from 750 GB on their Redis cluster. Billing against native rather than replicated data volume made cost forecasting predictable and favorable as data grows.

Restart recovery reduced from hours to seconds

Aerospike Enterprise Edition uses shared memory to preserve index state across process restarts. Where AdTalos previously faced multi-hour recovery windows, Aerospike reduced that to tens of seconds, eliminating restart latency as a production risk and restoring the team's ability to operate the cluster without fear of extended downtime.

A technical partnership, not just a platform

Aerospike provided AdTalos with direct access to support staff and engineers throughout deployment and beyond. Ongoing guidance on cluster management and scaling strategy has allowed AdTalos to plan capacity proactively as their data continues to grow exponentially, rather than reacting to problems after they surface.

After switching to Aerospike, AdTalos saw immediate improvements across latency, cost and operational stability. At 0.5 ms, read latency is not just fast on average but predictable at the tail, which is what determines whether bid decisions consistently arrive in time to matter. With that headroom established, the team now has room to execute more queries per bid window and expand their mapping workload without breaching latency limits.

5x faster reads, with 10x throughput potential validated

In benchmarking, Aerospike delivered read latency of 0.5 ms against 2.5 ms on the Redis cloud cluster. AdTalos validated that with similar mapping workloads at their growth trajectory, 10x the query volume is achievable without performance degradation.

50% infrastructure cost reduction

The combination of HMA, RF=2 replication and volume-based pricing cut infrastructure costs in half compared to the Redis cloud cluster. As data volume grows, the cost advantage widens rather than compressing.

Zero maintenance incidents since deployment

Since switching to Aerospike, AdTalos has had no maintenance incidents. The team has redirected time from reactive cluster management to proactive scaling and architecture planning in partnership with Aerospike's engineering team.