In programmatic advertising, milliseconds are money. Every 100ms of added latency in a real-time bidding (RTB) pipeline can reduce bid participation by 10% or more. When you multiply that across billions of daily impressions, the revenue impact is staggering. Ad tech companies that fail to optimize their data infrastructure don't just lose performance — they lose auctions, and with them, revenue.
Cachee.ai was built for exactly this class of problem: high-throughput, latency-critical workloads where intelligent caching decisions can transform the economics of the entire stack. Here's how we help ad tech companies move faster, spend less, and win more.
The Ad Tech Latency Problem
The programmatic advertising ecosystem operates under brutal time constraints. When a user loads a webpage, the publisher's ad server initiates an auction that must complete in under 200 milliseconds — from bid request to rendered creative. Within that window, demand-side platforms (DSPs) must receive the request, evaluate targeting data, consult audience segments, run pricing models, and return a bid response.
Most of the latency in this chain isn't compute — it's data retrieval. DSPs routinely query user profiles, frequency caps, campaign budgets, creative metadata, and blocklists on every single bid request. At 500,000+ queries per second, even a cache miss rate of 2% creates tens of thousands of slow-path database lookups per second, each one threatening to push a bid response past the exchange's timeout window.
How Cachee Solves It
Predictive Pre-Warming
Traditional caches are reactive. They store what was recently accessed and hope it gets accessed again. Cachee takes a fundamentally different approach. Our ML models analyze bid request patterns — time of day, geo distribution, campaign flight schedules, publisher traffic curves — and pre-warm cache entries before they're needed.
For ad tech, this means audience segments for an upcoming prime-time TV campaign are already cached at edge nodes before the first viewer opens their phone. Campaign budget counters are replicated to the regions where spend is accelerating. Creative assets are pre-positioned at CDN PoPs where impression volume is about to spike.
The result: cache hit rates above 99.5%, even during traffic bursts that would overwhelm a conventional caching layer.
Sub-Millisecond Lookups at Scale
Cachee's distributed cache mesh delivers consistent sub-millisecond read latency at any scale. Whether you're running 100K or 5M requests per second, lookup times stay flat. This is critical for bid evaluation pipelines where every microsecond counts.
Our architecture uses a tiered caching topology — L1 in-process caches for the hottest keys (user frequency caps, active campaign IDs), backed by a shared L2 mesh for broader dataset coverage (audience segments, creative metadata). Tiered eviction policies ensure that high-value, high-churn keys like real-time budget counters never get evicted by stale long-tail data.
Intelligent TTL Management
In ad tech, not all data ages at the same rate. A user's interest profile might be valid for hours, but a campaign's remaining daily budget changes every second. Static TTL policies force a painful tradeoff: set TTLs too long and you serve stale budget data (overspend); set them too short and you hammer your origin databases (latency spikes).
Cachee eliminates this tradeoff with adaptive TTLs. Our system monitors the mutation velocity of each key class and automatically adjusts expiration windows. Budget counters get sub-second TTLs with write-through propagation. Audience segments get longer TTLs with background refresh. Creative metadata gets cached until the flight ends. You get freshness guarantees and cache efficiency without manually tuning thousands of TTL rules.
Cutting CDN and Infrastructure Costs
Ad tech infrastructure costs are dominated by two things: compute for bid evaluation, and bandwidth for creative delivery. Cachee attacks both.
On the compute side, eliminating origin lookups means fewer database connections, less CPU time spent waiting on I/O, and smaller database instance requirements. Customers typically see a 40-60% reduction in origin database load within the first week of deployment, often allowing them to downsize their RDS or DynamoDB provisioning immediately.
On the bandwidth side, Cachee's edge caching layer serves creative assets — images, video pre-roll, VAST/VPAID tags — from the nearest PoP without round-tripping to origin. For a supply-side platform (SSP) serving 10 billion impressions per month, the CDN egress savings alone can exceed $50,000/month.
- Database costs: 40-60% reduction from fewer origin reads
- CDN egress: Up to 70% reduction through edge-cached creatives
- Compute: 30% fewer instances needed when I/O wait drops
- Total infrastructure savings: Typically $200K-$500K/year for a mid-size ad tech platform
Compliance and Privacy by Design
Ad tech operates in a regulatory minefield. GDPR, CCPA, TCF 2.0 consent strings, and an evolving patchwork of state-level privacy laws mean that every cached user record must respect consent boundaries. Cachee handles this natively.
Our platform supports geo-partitioned caching, ensuring that EU user data stays in EU regions and is automatically purged when consent is withdrawn. Consent-string-aware cache keys mean that the same user can have different cached profiles depending on their TCF consent state — no stale consent data is ever served. Deletion requests propagate across all cache tiers within seconds, not hours.
For SOC 2 and ISO 27001 compliance, every cache read and write is auditable. Cachee provides full audit trails showing what data was cached, where, for how long, and who accessed it — the same controls you'd expect from a database, applied to your caching layer.
Getting Started in Minutes
Cachee deploys as a sidecar or managed service — no rip-and-replace required. Most ad tech teams integrate via a single SDK call in their bid evaluation pipeline, replacing direct Redis or Memcached lookups with Cachee's API. The migration is incremental: start with one key class (say, frequency caps), measure the improvement, and expand from there.
Our onboarding wizard provisions your namespace, generates API credentials, and gives you a working curl command in under two minutes. From first API call to production traffic, most ad tech teams are fully deployed within a day.
Related Reading
The Numbers That Matter
Cache performance discussions get philosophical fast. Here are the actual measured numbers from production deployments running on documented hardware, so you can compare against your own infrastructure instead of trusting marketing copy.
- L0 hot path GET: 28.9 nanoseconds on Apple M4 Max, single-threaded against pre-warmed in-memory cache. This is the floor — there's no faster way to read a key.
- L1 CacheeLFU GET: ~89 nanoseconds on AWS Graviton4 (c8g.metal-48xl). Sharded DashMap with admission filtering.
- Sustained throughput: 32 million ops/sec single-threaded on M4 Max, 7.41 million ops/sec at 16 workers on Graviton4 c8g.16xlarge.
- L2 fallback: Sub-millisecond hits against ElastiCache Redis 7.4 over same-AZ network when L1 misses cascade through.
The compounding effect matters more than any single number. A 28-nanosecond L0 hit means your application spends almost zero time on cache lookups in the hot path, leaving the CPU free for the actual business logic that generates revenue.
Average Latency Hides The Real Story
Average latency is the most misleading number in cache benchmarking. The percentile distribution is what actually breaks production systems. Tail latency — the slowest 0.1% of requests — is where users notice the lag and where SLAs get violated.
| Percentile | Network Redis (same-AZ) | In-process L0 |
|---|---|---|
| p50 | ~85 microseconds | 28.9 nanoseconds |
| p95 | ~140 microseconds | ~45 nanoseconds |
| p99 | ~280 microseconds | ~80 nanoseconds |
| p99.9 | ~1.2 milliseconds | ~150 nanoseconds |
The p99.9 spike on networked Redis isn't a bug — it's the cost of running a single-threaded event loop that occasionally blocks on background tasks like RDB snapshots, AOF rewrites, and expired-key sweeps. Cachee's L0 stays inside a few hundred nanoseconds because the hot-path read is a lock-free shard lookup with no background work scheduled on the same thread.
If your application is sensitive to tail latency — payments, real-time bidding, fraud detection, trading — the p99.9 number is the one to optimize against. Average latency improvements that don't move the tail are vanity metrics.
Memory Efficiency Is The Hidden Cost Lever
Throughput numbers get the headlines but memory efficiency determines your monthly bill. A cache that stores the same hot data in less RAM lets you run a smaller instance class — and on AWS that's the difference between profitable and breakeven for a lot of services.
Redis stores each key as a Simple Dynamic String with 16 bytes of header overhead, plus dictEntry pointers in the main hashtable, plus embedded TTL metadata. For 1KB values, per-entry overhead lands around 1100-1200 bytes once you account for hashtable load factor and slab fragmentation. At a million keys, that's roughly 1.2 GB of resident memory just for the data.
Cachee's L1 layer uses sharded DashMap entries with compact packing — a 64-bit key hash, value bytes, an 8-byte expiry timestamp, and a small frequency counter for the CacheeLFU admission filter. Per-entry overhead lands at roughly 40 bytes of structural data on top of the value itself. For the same million-key workload, that's about 13% smaller resident memory. On AWS ElastiCache pricing, that gap is the difference between needing a cache.r7g.large versus a cache.r7g.xlarge for borderline workloads.
Observability And What To Measure
You can't tune what you can't measure. The four metrics that matter for any production cache deployment, in order of importance:
- Hit rate, broken down by key prefix or namespace. A global hit rate of 92% sounds great until you discover that one critical namespace is sitting at 40% and dragging your tail latency. Per-prefix hit rates expose which workloads are getting cache value and which aren't.
- Latency percentiles, not averages. p50, p95, p99, and p99.9 for both cache hits and cache misses. The cache miss latency is your fallback path performance — when the cache fails, this is what your users actually experience.
- Memory pressure and eviction rate. If your eviction rate is climbing while your hit rate stays flat, you're under-provisioned. If both are climbing, your access pattern shifted and you need to retune TTLs or rethink what you're caching.
- Stale-read rate. The percentage of cache hits that returned a value the application then discovered was stale. This is the canary for your invalidation strategy. If it's above 1%, your invalidation logic has a bug.
Cachee exposes all four out of the box via Prometheus metrics on the standard scrape endpoint, plus a real-time SSE stream for dashboards that need sub-second visibility. The right time to wire these into your monitoring stack is before the migration, not after the first incident.
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