Explore Use Cases
Reduce AI
Workload Costs
& Complexity
Migrate from AWS
Lambda, Azure Functions,
or Google Cloud Run
Scale‑to‑Zero
Developer & Preview
Environments
Handle Spiky
& Seasonal
Traffic
Dynamic
Batch
Processing
Optimize
Event‑ Driven
Architectures
Prevent
Latency &
Service Delays
Problem:
LLM inference / AI pipelines are GPU‑heavy, bursty, and expensive to keep warm.
Kedify solution:
GPU‑aware autoscaling and OTel‑based signals scale on real usage (RPS, concurrency, custom metrics), then scale down (including to zero when appropriate).
How it works (example signals):
- HTTP/OTel for request rate, concurrency, or token throughput
- PRP (vertical right‑sizing) to shrink warm pods when idle (alternative to replica=0)
Migrate from AWS Lambda, Azure Functions, or Google Cloud Run
Docs / Guides:
Problem:
Fragmented serverless + K8s leads to complexity, limited visibility, and cold‑start trade‑offs.
Kedify solution:
Bring serverless‑style, HTTP‑triggered autoscaling to Kubernetes (scale‑to‑zero supported) with unified observability and security.
Scale‑to‑Zero Developer & Preview Environments
Docs / Guides:
Problem:
Preview/dev envs often run 24/7 and waste spend.
Kedify solution:
HTTP scaler + autowiring + waiting/maintenance pages hold traffic safely during cold starts and scale down when idle.
Problem:
Launches, flash sales, closes/rollovers, or viral spikes cause over‑provisioning or outages.
Kedify solution:
Real‑time HTTP scaler with burst‑friendly behavior, backed by production‑grade Envoy‑based proxying.
Problem:
Nightly ETL, log analysis, or periodic model training doesn’t need constant compute.
Kedify solution:
Use ScaledJobs on event queues (Kafka, SQS, Redis, etc.) to spin up capacity just‑in‑time and back to zero after.
Problem:
Queues spike unpredictably; consumers sit idle for hours.
Kedify solution:
Scale on queue depth/lag across Kafka, RabbitMQ, Pulsar, Redis, SQS, etc. (70+ scalers supported).
Problem:
Mission‑critical APIs must stay responsive under any load; cold starts can hurt UX.
Kedify solution:
HTTP scaler bursts on live traffic, and Waiting/Maintenance Pages protect UX during scale‑from‑zero or maintenance.
Reduce AI Workload Costs & Complexity
Problem:
LLM inference / AI pipelines are GPU‑heavy, bursty, and expensive to keep warm.
Kedify solution:
GPU‑aware autoscaling and OTel‑based signals scale on real usage (RPS, concurrency, custom metrics), then scale down (including to zero when appropriate).
How it works (example signals):
- HTTP/OTel for request rate, concurrency, or token throughput
- PRP (vertical right‑sizing) to shrink warm pods when idle (alternative to replica=0)
Migrate from AWS Lambda, Azure Functions, or Google Cloud Run
Problem:
Fragmented serverless + K8s leads to complexity, limited visibility, and cold‑start trade‑offs.
Kedify solution:
Bring serverless‑style, HTTP‑triggered autoscaling to Kubernetes (scale‑to‑zero supported) with unified observability and security.
Scale‑to‑Zero Developer & Preview Environments
Problem:
Preview/dev envs often run 24/7 and waste spend.
Kedify solution:
HTTP scaler + autowiring + waiting/maintenance pages hold traffic safely during cold starts and scale down when idle.
How it works (example signals):
- HTTP/OTel for request rate, concurrency, or token throughput
- PRP (vertical right‑sizing) to shrink warm pods when idle (alternative to replica=0)
Handle Spiky & Seasonal Traffic
Problem:
Launches, flash sales, closes/rollovers, or viral spikes cause over‑provisioning or outages.
Kedify solution:
Real‑time HTTP scaler with burst‑friendly behavior, backed by production‑grade Envoy‑based proxying.
Dynamic Batch Processing
Problem:
Nightly ETL, log analysis, or periodic model training doesn’t need constant compute.
Kedify solution:
Use ScaledJobs on event queues (Kafka, SQS, Redis, etc.) to spin up capacity just‑in‑time and back to zero after.
Optimize Event‑Driven Architectures
Problem:
Queues spike unpredictably; consumers sit idle for hours.
Kedify solution:
Scale on queue depth/lag across Kafka, RabbitMQ, Pulsar, Redis, SQS, etc. (70+ scalers supported).
Prevent Latency & Service Delays
Problem:
Mission‑critical APIs must stay responsive under any load; cold starts can hurt UX.
Kedify solution:
HTTP scaler bursts on live traffic, and Waiting/Maintenance Pages protect UX during scale‑from‑zero or maintenance.
Cross‑use‑case enablers
- Production‑grade HTTP & gRPC scaler and GPU‑aware algorithms (scale
down cost, keep latency in check). - OpenTelemetry scaler (push‑based, no Prometheus scrape delay) with an
LLM/vLLM example. - Pod Resource Profiles (PRP) for vertical right‑sizing during idle periods.
- Multi‑cluster dashboard and hardened builds (FIPS, CVE‑free commitment).
Real-World Proof
“Before Kedify, scaling up was a constant
challenge. Now, our platform adapts instantly to
our users' needs, and we've freed up our team
to focus on new features rather than managing
resource spikes.”
— Rafael Tovar, Cloud Operations Leader, Tao Testing
With Kedify Tao Testing handled a 200× traffic burst with
zero downtime and ~40% lower spend.
“With Kedify, our developers get the best of both worlds, cost-efficient scaling like Google Cloud Run, but fully integrated within our Kubernetes-based platform.”
— Jakub Sacha, SRE, Trivago
Trivago migrated 150–200 preview environments from Cloud Run to Kubernetes while keeping scale to zero efficiency.