With traffic profiles where requests arrive in at periodic intervals, and a low total amount of requests, serverless architecture seems to be a great architecture in terms of cost, speed of delivery and effort. Thus, Lambda is probably the way to go if our application has sufficiently large periods of inactivity. Once the break-even point is reached, when EC2 is more cost-effective than Lambda, the cost difference grows rapidly, making Lambda less and less attractive in terms of cost. Thus, it is of great importance to know if the expected amount of traffic will be around the break-even point. Be aware of the CPU throttling you will get with the smaller memory flavors of Lambda. If your code is CPU-bound, choosing the smaller memory flavors might not be an option, since execution times, and thus latency, might grow beyond your requirements. On the other hand, if your code is I/O bound, the CPU throttling might not affect you significantly. Break-even point (if there is one, that is) strongly depends on the application itself. Without measuring the target application code, knowing the intended usage of the service, the SLA and the capabilities of the team in charge of building the application it is almost impossible to know for sure which service, Lambda or EC2, is more convenient.
we operate Kubernetes as follows to try and minimise it:
- We run multiple production clusters and teams are able to choose which clusters to run their application in. We don’t use Federation yet (we’re waiting on AWS support) but we use Envoy instead to load-balance across the different cluster Ingress load-balancers. We can automate much of this with our Continuous Delivery pipeline (we use Drone) and other AWS services.
- All clusters are configured with the same Namespaces. These map approximately 1:1 with teams.
- We use RBAC to control access to Namespaces. All access is authenticated and authorised against our corporate identity in Active Directory.
- Clusters are auto-scaled and we do as much as we can to optimise node start-up time.
- Applications auto-scale using application-level metrics exported from Prometheus.
Des serveurs d’Amazon jusqu’aux machines rouges installées directement chez les fournisseurs d’accès à internet, la firme a créé au fil des années un système complexe, mais d’une efficacité redoutable, le tout à un prix très raisonnable qui lui donne un avantage concurrentiel évident.
This page helps you find and compare AWS EC2 instance types, features and pricing. The data is provided by AWSPrice List API.
Easy to use tool that automatically replaces some or even all on-demand AutoScaling group members with similar or larger identically configured spot instances in order to generate significant cost savings on AWS EC2, behaving much like an AutoScaling-backed spot fleet