DEVPROTON

Martech / SEO

Cutting API Latency from 800ms to <300ms at Martech Scale

<300ms

p95 latency (was ~800ms)

50K+

records per query

30%

infra cost reduction

A martech platform serving 50K+ records per query was bottlenecking on a legacy REST API. We re-architected the read path and dropped p95 latency from ~800ms to <300ms.

The challenge

The client — a Series B martech / SEO platform — had a hot endpoint that aggregated tens of thousands of records per request. As customer accounts grew, p95 latency drifted from ~300ms to ~800ms. Power users started seeing dashboard timeouts.

The team had already added caching and pagination. The remaining bottleneck was the query path itself — and the schema couldn't be changed without breaking downstream integrations.

Method

  1. Profiled the hottest endpoint with realistic production payloads. Three queries were responsible for 80% of the latency.
  2. Rewrote the read path to use composite indexes and a read replica, with Redis caching on the highest-cardinality lookups.
  3. Ran shadow traffic in production for two weeks before promoting the new path.
  4. Cut infra spend by ~30% by removing redundant in-memory caches and right-sizing replica instances.

Outcome

  • p95 latency: ~800ms → <300ms on the hot path
  • Throughput: 100K+ daily operations sustained without horizontal scale
  • Cost: 30% reduction in monthly cloud spend
  • Zero breaking changes to public API contracts

Stack

Python · FastAPI · PostgreSQL · Redis · AWS ECS · Terraform · Grafana

Services / Stack

custom-saasmartech-platform

Related work

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