Erie Gripper

How to Find a Injection Molding Machine in Erie ?

Whether the fabricator’s shop is large or small, the Ironworker is the backbone. The Ironworker isn’t a single machine; it is five machines united into an engineering wonder. It has much more versatility than most people would imagine. The five working sections that are involved in the make-up of this machine are a punch, a section shear, a bar shear, a plate shear, and a coper-notcher.

A number of the cheaper ironworkers are constructed to employ a fulcrum where the ram shakes back and forth, building the punch go into the succumb at a small angle. This normally leads to the eroding of the punch and succumb on the front rims. The higher quality machines integrate a ram which moves in a direct vertical line and employs modifiable gibs and guides to insure a constant traveling path.

Eoat Gripper

When you look for a End of Arm Tooling (EOAT)  that develop a Injection Molding Machine in Erie, looks for experience and not only pricing.

That dedicates more life to the tooling, and allows the punch to penetrate the succumb right in the middle in order to capitalize on the machine’s total tonnage.

When looking for a design house that designs a Injection Molding Machine in Erie  don’t look just in Michigan , other States also have great providers.

End Of Arm Tooling Parts

Vacuum Pump Repair


Intel dominated and defined the semiconductor landscape during the PC era on two complementary fronts — silicon process technology and computing architecture (x86). Through its partnership with Microsoft, Intel enjoyed a near complete monopoly over the computing landscape during the PC era. That dominance began to erode with the emergence of two Segment Zero markets (Link) for Intel — embedded computing and mobile computing. The company that under the leadership of Andy Grove had successfully identified and vanquished at least two prior disruptive threats (Japanese memory makers in the 1980s and low cost PCs in the early 1990s) failed to successfully prepare for the next disruption — mobile computing and the ecosystem pioneered by ARM, the leader in low-cost/low-power architecture. While Intel pioneered the era of the standalone CPU with a vertically integrated business model, ARM enabled a massive lateral design/foundry ecosystem and pioneered the era of the mobile SoC (system-on-a-chip).

CPU vs. SoC

In the CPU space, chip functionality is largely determined by the computing core (e.g. Pentium, Athlon) and transistor performance is the critical metric. In the SoC space, the core is just one among a variety of IP blocks that are used to independently deliver functionality. Intel’s foray into SoC technology started in the early 2000s and was largely a response to the success of the foundry ecosystem. However, Intel’s SoC process technology has typically been implemented 1–2 years behind its mainstream CPU technology, which historically has focused on transistor scaling and performance. The foundries within the ecosystem instead focused on integrating disparate functional IP blocks on a chip while also aggressively scaling interconnect density.

The semiconductor industry today is increasingly driven by low-power consumer electronics (primarily smartphones) and SoC shipments now dominate total silicon volume. The sheer volume of desktop class computing chips like Apple A9 SoCs shipped to date has in turn dramatically improved the competitiveness of the foundry ecosystem (led by TSMC) compared to Intel. Until a few years ago, Intel’s process technology lead was unquestioned. That lead is now greatly diminished as the foundry ecosystem is on track to ship more 64 bit SoC chips than Intel by the end of this year.

The ascendance of ARM has not only displaced Intel’s leadership on the architecture front (x86) but indirectly, also on the process technology front by enabling the foundry ecosystem to ship incredibly large volumes of leading edge silicon and dramatically speeding up the manufacturing yield learning curve. Intel was late in recognizing the importance of the SoC and now finds itself playing catch-up to a strong ecosystem led by ARM on the architecture front and TSMC on the silicon process technology front.

Compounding this trend further is the reality that after 50 years of delivering consistent gains in power, performance and cost; transistor scaling is finally entering an era of diminishing returns where further shrinking the device is not only costly, but provides incremental gains in performance and power.

Meanwhile, the ARM ecosystem is also steadily making inroads into the high-end space traditionally dominated by Intel. Several new tablet and laptop computers (e.g. Google Pixel C) use SoC chips designed by fabless companies instead of CPU solutions from Intel. Over time, SoCs became much more powerful and competitive and now pose a meaningful threat to the standalone CPU. The predominance of the Intel-Microsoft partnership based on x86 architecture is waning and a huge swath of the mobile computing space is now supported by low cost Chinese design houses like MediaTek, AllWinner, RockChip and Spreadtrum that use ARM architecture and foundries like TSMC, SMIC or UMC.

The emergence of the SoC was thus a strategic inflection point for both Intel and the ARM ecosystem alike. While the silicon landscape during the PC era was defined by Intel and the CPU, it is fair to say that the silicon landscape during the mobile era continues to be defined by the SoC and the foundry ecosystem led by ARM and TSMC. In many ways, Intel’s ability to compete in the SoC space will determine the direction of the chip wars in the next wave of computing (IoT).

Transistor Technology

The process technology underlying CPUs and SoCs is similar, however the design points for each can be vastly different. For example, a CPU design requires fewer transistor variants spanning a limited range of leakage and speed. On the other hand, SoC designs require many more transistor variants spanning a much wider range of leakage and speed. SoC technology also needs to support higher supply voltages for IO devices (e.g. 1.8V, 3.3V) in addition to the nominal supply voltage for core devices (e.g. 0.9V) These differences, though subtle, require very different mindsets in transistor design and process architecture.

Intel’s focus on transistor performance can be traced back to the height of the PC wars when the benchmark was clock speed. While Intel focused on transistor performance, the foundries adapted Intel’s transistor innovations for their own SoC integration needs. In addition, they aggressively pursued metal density scaling and cost reduction. While Intel pursued a limited vertical functional integration, the foundries developed a lateral ecosystem and designed transistors for a variety of vendors that independently optimized functionality for each IP block (CPU, GPU, radio, modem, GPS, IO, SERDES, etc.).

This vast ecosystem of existing design IP is now a significant influence on the adoption of the next transistor architecture. Arguably, the foundries are today better positioned for the SoC era. By the end of 2015, TSMC will have shipped well over 100 million units of Apple’s A9 SoC. These processors are made in 16nm technology and will set new benchmarks for cost, power and connectivity features. The Apple A9 processor is possibly the most highly integrated SoC running on the most advanced silicon process technology (at TSMC and also Samsung). Intel’s advantage at the transistor level thus allowed it to win the CPU space, but the ecosystem has the advantage at the system level and is poised to win the SoC space.

In the mobile and IoT era, packing as many features on a chip as possible at the lowest integrated system cost and power will win. The transistor technology that is most compatible with all the IP needs of a complex SoC at the lowest cost will thus have the upper hand.

The Post-PC Era: Intel in an Open Ecosystem

The slowdown in the pace of Moore’s Law, the emerging importance of the SoC and the rapid growth of the mobile market all tend to favor an open, plug-and-play foundry and design ecosystem. One could expect that the ecosystem developing around ARM will continue to nip at Intel’s core markets as the development of ARM-based processors for laptops and servers accelerates. This emerging threat to Intel and Intel’s response to it will define the industry over the coming decade.

The operating system (OS) war between Microsoft and Apple in the 1980s came to define the PC and software industries. Microsoft’s open ecosystem model won as Windows became the de-facto OS for machines made by all kinds of PC makers. While Microsoft promoted an open ecosystem in the larger PC industry, ironically it spawned a closed ecosystem within the semiconductor industry. The Wintel alliance ensured that Windows only ran on x86 architecture which was pioneered and owned by Intel. The closed ecosystem hugely benefited Intel as it went on almost unchallenged to win the desktop, laptop and server space (AMD also used x86 yet could never match Intel’s scale or manufacturing expertise). A hallmark of the post-PC era is the emergence of an open ecosystem within the semiconductor industry.

Unlike the Windows/x86 dominance of the past, the post-PC era is being defined by competing OS options (iOS, Android or Windows) and competing processor architectures (x86 or ARM). Today, the momentum is in favor of ARM-based operating systems as the vast majority of mobile devices being shipped today run iOS or Android (ARM architecture).

The chip wars will be fought in this fragmented and open ecosystem on three fronts — SoC (system integration), CPU (core architecture) and silicon (foundry technology). While performance and power will continue to be important benchmarks, the open ecosystem supporting a worldwide consumer market will make cost a key success metric on each battlefront.

Battlefront #1 — SoC (System Integration)

In the mobile SoC space, the battle for processor architecture will be between Intel on the one hand and incumbents like Qualcomm, Samsung and Apple on the other. In the mobile, power constrained space, it is more efficient to integrate a variety of hardware accelerators on a single chip to deliver custom functionality as opposed to implementing a general purpose core serving most functions. Low power cores are supplemented with elements as disparate as an on-chip radio, global positioning system (GPS), modem, image and audio/video processor, universal serial bus (USB) connectivity and a graphics processing unit (GPU). An open ecosystem is far more cost-effective for such modular, plug-and-play system-level integration.

A typical CPU design (Intel Core-M) dominated by core/graphics compared to a highly integrated SoC (NVIDIA Tegra 2). The integrated SoC design has obvious advantages in mobile formfactors.

Historically, Intel, being an integrated device manufacturer (IDM) has independently designed most of the functional IP blocks, while ensuring that each uses Intel transistor technology and process design rules. Intel’s process technology leadership has benefited it enormously in the CPU space giving its designers access to best-in-class transistor performance. However, Intel’s ability to compete in the mobile SoC space will be determined by how well it can re-engineer its CPU process technology to meet the diverse needs of a complex mobile SoC.

If Intel can successfully design and manufacture 14nm and 10nm processes that span the full range of the performance-power spectrum required for mobile SoC applications, it will have an edge over the competition. But for Intel to compete effectively in the mobile SoC space, it will also need to offer a cost advantage. Average Selling Price (ASP) in the SoC space is a fraction of that in the CPU space. While fabless Apple can drive the best possible deal from competing foundries, IDM Intel needs to ensure that its volumes and ASPs are high enough to recoup its own development and manufacturing CapEx.

Intel may try to enhance its SoC functionality offering by way of more acquisitions like Infineon Wireless. But post-merger, porting Infineon’s foundry standard design rules to Intel’s proprietary design rules will be non-trivial (In 2015, nearly 5 years after the acquisition, Intel is yet to port Infineon’s modem chips to their own fabs and continues to make them at TSMC!). By contrast, the Qualcomm acquisition of Atheros likely proved to be more seamless since the IP was from the open ecosystem and already foundry compatible.

Battlefront #2 — CPU (Core Architecture)

The main battle on the CPU front is between Intel/x86 and ARM architecture. While Intel historically has had the upper hand in performance, ARM-designed cores have delivered superior performance/watt.

To effectively compete against ARM, Intel will need to design its low-power Atom cores in the most power-efficient way possible. To design a true low-power core, Intel may need to decouple the Atom from legacy x86-based architecture and develop a new ground-up design that delivers highly competitive performance/watt.

Intel will also have to be in aggressive catch-up mode as it tries to reverse the momentum of an already large, established and robust ARM software ecosystem. In the initial years of the PC era, as x86 became the predominant CPU architecture, an entire ecosystem of application software was spawned that was designed to run solely on x86. This effectively precluded or seriously hindered competing architectures like PowerPC from ever gaining a foothold in the marketplace. Analogously, in the present day, ARM architecture is significantly further along in achieving critical mass in the mobile SoC space. The prevalence of ARM in a range of post-PC devices from smartphones and tablets (90% market share) to televisions and cars has placed ARM in a commanding position to inhibit the newer Intel Atom architecture from achieving traction. Practically speaking, for Intel to gain a meaningful share in the mobile market, it now has to ensure compatibility with the ARM software ecosystem. This again, will force Intel to compete on price which will limit how much revenue it can eventually generate. This is a dynamic that Intel never had to face in the PC segment.

Battlefront #3 — Silicon (Foundry Technology)

Intel’s ability to make the best performing transistor at the highest possible yields and volumes is unparalleled. This capability served it immensely well in the closed ecosystem when Intel was essentially competing against itself in the quest to make a smaller and faster transistor. In the closed ecosystem, performance trumped power; and design flexibility and high ASPs ensured that development cost was not a significant limiter.

In the open ecosystem, however, the ability to integrate disparate functional accelerators in the most power-efficient and cost-effective manner is paramount. As an example, TSMC is able to deliver the highly successful and functional A9 processor for Apple using a state-of-the-art 16nm transistor process and integrate a variety of complex IP blocks while keeping the ASP under $20. TSMC’s minimum metal pitch at the 16nm node is larger (i.e. less dense) than that of Intel at the more advanced 14nm node, yet the A9 SoC can offer better power efficiency than a comparable 14nm CPU at an acceptable performance point and much lower price point and a much smaller form-factor.

In the post-PC era, mobile and IoT computing will have a larger influence on the semiconductor landscape. The success metrics in the new landscape are not just higher transistor performance but higher system functionality, lower system cost and lower power.

Based on the above discussion and judgment, the following trends are likely to define the semiconductor industry over the next decade.

  1. Shrinking pool of advanced semiconductor fabs: The economics of Moore’s Law and the advent of mobile computing have led to a dramatic reduction in the number of advanced semiconductor manufacturing sources. Just 3 major entitities (Intel, Samsung, TSMC) now offer unique 16nm or advanced technology. (Globalfoundries is effectively just a manufacturing partner for Samsung). A wildcard here is SMIC (Semiconductor Manufacturing International Corporation, Shanghai). Even though it is a relative newcomer, SMIC is extremely driven and has the full backing of the Chinese government which has made advanced semiconductor manufacturing a national priority. SMICs entry at 14nm (by 2020) may change the foundry landscape by dramatically altering silicon wafer price-points.
  2. Making things smaller doesn't help much anymore: The 28nm node will be the longest running planar transistor technology. In a departure from prior technologies, and in response to plateauing transistor cost, the leading foundry (TSMC) has developed over 5 flavors of the technology for all applications ranging from high performance 28HPM (FPGA, GPU, mobile SoC) to ultra-low power 28ULP (IoT edge computing). As the mobile computing era matures and the IoT computing era emerges, majority of the applications will be served by 28nm or older technology. As technology development lifecycles get longer and product lifecycles get shorter, foundries will try to extract all the goodness in an existing transistor technology before moving to the next one.
  3. Even fewer applications for advanced technologies: Only a minority of applications (e.g. high performance computing, AI/AR, machine learning, computer vision) will migrate to using sub-10nm and lower technology nodes. And these advanced nodes will also be long lived with multiple variants serving disparate power/performance/cost points.
  4. Intel CPU leadership: Intel will continue to dominate the single thread/high performance CPU/server segment, albeit with increasing competition from the ARM ecosystem. Intel’s acquisition of Altera is a defensive move aimed at creating a moat around its server leadership. However, the next five years will likely see the emergence of competitive ARM based servers. Using an open ecosystem with customizable IP will enable significant cost and power reduction for these new entrants.
  5. Lego block on-chip integration: In the power and cost competitive IoT era, on-chip integration of hardware accelerators (modem, CPU, graphics, etc) will continue to be extremely efficient. Compared to centralized CPU/GPU cores, SoCs will be far more effective, especially in the smartphone, tablet and convertible form-factors. As silicon scaling plateaus, packing as many disparate functional blocks as possible on a chip within a given transistor budget at the lowest integrated system cost and power will win. Companies will try to expand their footprint by capturing more real estate on the chip, either through consolidation or on their own.
  6. Ascendance of the SoC: Intel’s 14nm CPU (Skylake, 2015) and Apple/TSMC’s 16nm SoC (Apple A9, 2015) are two marquee technologies/products that will provide a barometer on the semiconductor landscape. Several benchmarking results indicate that the A9 is perhaps the most efficient mobile SoC with unparalleled performance/power metrics. This match-up will have remarkable implications — not only will it validate the rise of Apple as the dominant SoC design team, it will also suggest a vulnerability in Intel’s process technology leadership. It suggests that TSMC could go toe-to-toe with Intel on radical and highly complex transistor architectures (16/14nm tri-gate), while also supporting best-in-class SoC technology which is the enabling platform for mobile and IoT computing. Intel will need to dramatically improve its SoC offering in the years to come in order to be competitive in the SoC/IoT space.
  7. Slowing cadence of Moore’s Law: Two technologies that have the potential to significantly influence the economics of Moore’s Law and disrupt the industry cost model are (a) 450mm wafer size and (b) EUV lithography. However, a glut of fully depreciated 300mm fab infrastructure and decades long slow progress in the EUV tooling roadmap will make it a difficult value proposition at least in the foreseeable future. Conventional Moore’s Law scaling is likely to give way to more orthogonal scaling approaches (More-than-Moore) including 3D chip stacking and system/package level integration of heterogeneous chips.
Robot End Effector Gripper

AWS recently announced its new per second billing for its EC2 instances and EBS volumes. This is perfect timing to talk about cost optimization. After a short intro we will guide you through some real world examples and best practices that we use at Teads to optimize our infrastructure costs.

The cloud computing opportunity and its traps

One of the advantages of cloud computing is its ability to fit the infrastructure to your needs, you only pay for what you really use. That is how most hyper growth startups have managed their incredible ascents.

Most companies migrating to the cloud embrace the “lift & shift” strategy, replicating what was once on premises.

You most likely won’t save a penny with this first step.

Main reasons being:

  • Your applications do not support elasticity yet,
  • Your applications rely on complex backend you need to migrate with (RabbitMQ, Cassandra, Galera clusters, etc.),
  • Your code relies on being executed in a known network environment and most likely uses NFS as distributed storage mechanism.

Once in the cloud, you need to “cloudify” your infrastructure.

Then, and only then, will you have access to virtually infinite computing power and storage.

Watch out, this apparent freedom can lead to very serious drifts: over provisioning, under optimizing your code or even forgetting to “turn off the lights” by letting that small PoC run more than necessary using that very nice r3.8xlarge instance.

Essentially, you have just replaced your need for capacity planning by a need for cost monitoring and optimization.

The dark side of cloud computing

At Teads we were “born in the cloud” and we are very happy about it.

One of our biggest pain today with our cloud providers is the complexity of their pricing.

It is designed to look very simple at the first glance (usually based on simple metrics like $/GB/month or $/hour or, more recently, $/second) but as you expand and go into a multi-region infrastructure mixing lots of products, you will have a hard time tracking the ever-growing cost of your cloud infrastructure.

For example, the cost of putting a file on S3 and serving it from there includes four different lines of billing:

  • Actual storage cost (80% of your bill)
  • Cost of the HTTP PUT request (2% of your bill)
  • Cost of the many HTTP GET requests (3% of your bill)
  • Cost of the data transfer (15% of your bill)

Our take on Cost Optimization

  • Focus on structural costs - Never block short term costs increase that would speed up the business, or enable a technical migration.
  • Everyone is responsible - Provide tooling to each team to make them autonomous on their cost optimization.

The limit of cost optimization for us is when it drives more complexity in the code and less agility in the future, for a limited ROI. 
This way of thinking also helps us to tackle cost optimisation in our day to day developments.

Overall we can extend this famous quote from Kent Beck:

“Make it work, make it right, make it fast” … and then cost efficient.

Billing Hygiene

It is of the utmost importance to keep a strict billing hygiene and know your daily spends.

In some cases, it will help you identify suspicious uptrends, like a service stuck in a loop and writing a huge volume of logs to S3 or a developer that left its test infrastructure up & running during a week-end.

You need to arm yourself with a detailed monitoring of your costs and spend time looking at it every day.

You have several options to do so, starting with AWS’s own tools:

  • Billing Dashboard, giving a high level view of your main costs (Amazon S3, Amazon EC2, etc.) and a rarely accurate forecast, at least for us. Overall, it’s not detailed enough to be of use for serious monitoring.
  • Detailed Billing Report, this feature has to be enabled in your account preferences. It sends you a daily gzipped .csv file containing one line per billable item since the beginning of the month (e.g., instance A sent X Mb of data on the Internet). 
    The detailed billing is an interesting source of data once you have added custom tags to your services so that you can group your costs by feature / application / part of your infrastructure. 
    Be aware that this file is accurate within a delay of approximately two days as it takes time for AWS to compute the files. 
    UPDATE (June ‘18) Detailed Billing is officially deprecated, use the Cost and Usage Report instead.
  • Trusted Advisor, available at the business and enterprise support level, also includes a cost section with interesting optimization insights.
Trusted Advisor cost section - Courtesy of AWS
  • Cost Explorer, an interesting tool since its update in august 2017. It can be used to quickly identify trends but it is still limited as you cannot build complete dashboards with it. It is mainly a reporting tool.
Example of a Cost Explorer report — AWS documentation

Then you have several other external options to monitor the costs of your infrastructure:

  • SaaS products like Cloudyn / Cloudhealth. These solutions are really well made and will tell you how to optimize your infrastructure. Their pricing model is based on a percentage of your annual AWS bill, not on the savings that the tools will help you make, which was a show stopper for us.
  • The open source project Ice, initially developed by Netflix for their own use. Recently, the leadership of this project was transferred to the french startup Teevity who is also offering a SaaS version for a fixed fee. This could be a great option as it also handles GCP and Azure.

Building our own monitoring solution

At Teads we decided to go DIY using the detailed billings files.

We built a small Lambda function that ingests the detailed billing file into Redshift every day. This tool helps us slice and dice our data along numerous dimensions to dive deeper into our costs. We also use it to spot suspicious usage uptrends, down to the service level.

This is an example of our daily dashboard built with, each color corresponds to a service we taggedWhen zoomed on a specific service, we can quickly figure out what is expensive

On top of that, we still use a spreadsheet to integrate the reservation upfronts in order to get a complete overview and the full daily costs.

Now that we have the data, how to optimize?

Here are the 5 pillars of our cost optimization strategy.

1 - Reserved Instances (RIs)

First things first, you need to reserve your instances. Technically speaking, RIs will only make sure that you have access to the reserved resources.

At Teads our reservation strategy is based on bi-annual reservation batches and we are also evaluating higher frequencies (3 to 4 batches per year).

The right frequency should be determined by the best compromise between flexibility (handling growth, having leaner financial streams) and the ability to manage the reservations efficiently. 
In the end, managing reservations is a time consuming task.

Reservation is mostly a financial tool, you commit to pay for resources during 1 or 3 years and get a discount over the on-demand price:

  • You have two types of reservations, standard or convertible. Convertible lets you change the instance family but comes with a smaller discount compared to standard (avg. 75% vs 54% for a convertible). They are the best option to leverage future instance families in the long run.
  • Reservations come with three different payment options: Full Upfront, Partial Upfront, and No Upfront. With partial and no upfront, you pay the remaining balance monthly over the term. We prefer partial upfront since the discount rate is really close to the full upfront one (e.g. 56% vs 55% for a convertible 3-year term with partial).
  • Don’t forget that you can reserve a lot of things and not only Amazon EC2 instances: Amazon RDS, Amazon Elasticache, Amazon Redshift, Amazon DynamoDB, etc.

2 - Optimize Amazon S3

The second source of optimization is the object management on S3. Storage is cheap and infinite, but it is not a valid reason to keep all your data there forever. Many companies do not clean their data on S3, even though several trivial mechanisms could be used:

The Object Lifecycle option enables you to set simple rules for objects in a bucket :

  • Infrequent Access Storage (IAS): for application logs, set the object storage class to Infrequent Access Storage after a few days. 
    IAS will cut the storage cost by a factor of two but comes with a higher cost for requests. 
    The main drawback of IAS is that it uses 128kb blocks to store data so if you want to store a lot of smaller objects it will end up more expensive than standard storage.
  • Glacier: Amazon Glacier is a very long term archiving service, also called cold storage. 
    Here is a nice article from Cloudability if you want to dig deeper into optimizing storage costs and compare the different options.

Also, don’t forget to set up a delete policy when you think you won’t need those files anymore.

Finally, enabling a VPC Endpoint for your Amazon S3 buckets will suppress the data transfer costs between Amazon S3 and your instances.

3 - Leverage the Spot market

Spot instances enables you to use AWS’s spare computing power at a heavily discounted price. This can be very interesting depending on your workloads.

Spot instances are bought using some sort of auction model, if your bid is above the spot market rate you will get the instance and only pay the market price. However these instances can be reclaimed if the market price exceeds your bid.

At Teads, we usually bid the on-demand price to be sure that we can get the instance. We only pay the “market” rate which gives us a rebate up to 90%.

It is worth noting that:

  • You get a 2 min termination notice before your spot is reclaimed but you need to look for it.
  • Spot Instances are easy to use for non critical batch workloads and interesting for data processing, it’s a very good match with Amazon Elastic Map Reduce.

4 - Data transfer

Back in the physical world, you were used to pay for the network link between your Data Center and the Internet.

Whatever data you sent through that link was free of charge.

In the cloud, data transfer can grow to become really expensive.

You are charged for data transfer from your services to the Internet but also in-between AWS Availability Zones.

This can quickly become an issue when using distributed systems like Kafka and Cassandra that need to be deployed in different zones to be highly available and constantly exchange over the network.

Some advice:

  • If you have instances communicating with each other, you should try to locate them in the same AZ
  • Use managed services like Amazon DynamoDB or Amazon RDS as their inter-AZ replication costs is built-in their pricing
  • If you serve more than a few hundred Terabytes per months you should discuss with your account manager
  • Use Amazon CloudFront (AWS’s CDN) as much as you can when serving static files. The data transfer out rates are cheaper from CloudFront and free between CloudFront and EC2 or S3.

5 - Unused infrastructure

With a growing infrastructure, you can rapidly forget to turn off unused and idle things:

  • Detached Elastic IPs (EIPs), they are free when attached to an EC2 instance but you have to pay for it if they are not.
  • The block stores (EBS) starting with the EC2 instances are preserved when you stop your instances. As you will rarely re-attach a root EBS volume you can delete them. Also, snapshots tend to pile up over time, you should also look into it.
  • A Load Balancer (ELB) with no traffic is easy to detect and obviously useless. Still, it will cost you ~20 $/month.
  • Instances with no network activity over the last week. In a cloud context it doesn’t make a lot of sense.

Trusted Advisor can help you in detecting these unnecessary expenses.

Key takeaways

Thank you for reading. This article was inspired by the talks I made during the #2 AWS Montpellier Meetup and Devops D-Day conference.

Devops D-Day 2017 — Marseille

If you like working on big cloud infrastructures and growth challenges, feel free to contact us, we are constantly looking for great teammates.

If you want to know more about Engineering at Teads:

About Teads Engineering
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