Image Registry Superpowers for
Accelerate your image deployments by 10x, reduce egress and idle compute costs, decrease multi-cluster complexity and increase reliability.
Accelerating "Time to Compute" only takes a few minutes
Being just a daemonset, Spegel Enterprise makes you wonder if you missed any steps during deployment.
10x Faster deployments
No more individual pulls from registry for each deployment. P2P distribution of cached images across nodes accelerates deployments up to 20x. Benefits increase with increased usage of short-lived containers.
Cold Starts
Spegel Enterprise, with its tracker, solves the DDOSing issue where each node tries downloading the same image from the source and effectively throttling the network.
Reduced egress and idle time costs
Up to 90% reduction in egress fees and idle time spent at pod initiation
Multi-region distribution
Enabling every cluster to be its own stateless registry, removes the need for stateful regional registries, and overhead of managing them.
AI & Edge AI Workloads
Images with AI models exceed tens of gigabytes in size. Increased sizes also make it harder to distribute at the edge. .
High Availability
Spegel Enterprise users don’t get effected by registry reliability issues as every node becomes a distribution node
One Image, One Pull
Each node downloading its own image is DDos'ing your network and choking its performance. Spegel Enterprise supports all registries.
As many registries as you want as your image source
Pull any image only once and distribute across nodes, clusters and regions in p2p fashion
Save from Egress costs and improve your infrastructure performance
No need to deal with regional image replication
Spegel Enterprise tracks all deployments and distributes images across the topology automatically
Cold start delays
Ensuring required images would be available in the cluster and distributed locally
Compare Editions
Please get in touch for inquiries.
| Open Source | Base Enterprise | Enterprise | |
|---|---|---|---|
| Best Effort Caching | |||
| Preemptive Caching | |||
| Regional Image Coordination | |||
| Custom Integrations | Case by Case | Case by Case | |
| Hardened Binary | |||
| Openshift Support | |||
| Enterprise Support | |||
| Quarterly Workshops |
Spegel
What began as a lightweight tool has become a critical part
of deployment strategies for many large-scale companies.
Spegel was created as a best effort cache for Kubernetes,
designed to make image distribution faster, simpler, and
more resilient across clusters. Over the past year, it has
seen remarkable growth, adding over 2,000 GitHub stars,
millions of image pulls, and widespread use in production
environments.
Kvick builds on this success. It exists to keep Spegel
moving forward, enabling continued investment, new
features, and the same focus on simplicity and reliability
that started it all.
Monthly GitHub Pulls
doesn't include private registries
Large Enterprises
across 20+ countries
Kubernetes Clusters
across 100+ locations
Contributors
Spegel OSS is created by a team of enthusisasts
Get in touch with the team!
We are a team of Open Source enthusisasts working to maximize the impact of Spegel and Spegel Enterprise. We would love to hear from you and feel free share your thoughts, recommendations or how we could improve!
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