AWS announces new savings plans to reduce complexity of reserved instances

Reserved instances (RIs) have provided a mechanism for companies, who expect to use a certain level of AWS infrastructure resources, to get some cost certainty, but as AWS’s Jeff Barr points out they are on the complex side. To fix that, the company announced a new method called Savings Plans.

“Today we are launching Savings Plans, a new and flexible discount model that provides you with the same discounts as RIs, in exchange for a commitment to use a specific amount (measured in dollars per hour) of compute power over a one or three year period,” Barr wrote in a blog post announcing the new program.

Amazon charges customers in a couple of ways. First, there is an on-demand price, which is basically the equivalent of the rack rate at a hotel. You are going to pay more for this because you’re walking up and ordering it on the fly.

Most organizations know they are going to need a certain level of resources over a period of time, and in these cases, they can save some money by buying in bulk up front. This gives them cost certainty as an organization, and it helps Amazon because it knows it’s going to have a certain level of usage and can plan accordingly.

While Reserved Instances aren’t going away yet, it sounds like Amazon is trying to steer customers to the new savings plans. “We will continue to sell RIs, but Savings Plans are more flexible and I think many of you will prefer them,” Barr wrote.

The Savings Plans come in two flavors. Compute Savings Plans provide up to 66% savings and are similar to RIs in this regard. The aspect that customers should like is that the savings are broadly applicable across AWS products, and you can even move work loads between regions and maintain the same discounted rate.

The other is an EC2 Instance Savings Plan. With this one, also similarly to the reserved instance, you can save up to 72% over the on-demand price, but with this option you are limited to a single region.  It does offer a measure of flexibility though allowing you to select different sizes of the same instance type or even switch operating systems from Windows to Linux without affecting your discount with your region of choice.

You can sign up today through the AWS Cost Explorer.


By Ron Miller

Amazon migrates more than 100 consumer services from Oracle to AWS databases

AWS and Oracle love to take shots at each other, but as much as Amazon has knocked Oracle over the years, it was forced to admit that it was in fact a customer. Today in a company blog post, the company announced it was shedding Oracle for AWS databases, and had effectively turned off its final Oracle database.

The move involved 75 petabytes of internal data stored in nearly 7,500 Oracle databases, according to the company. “I am happy to report that this database migration effort is now complete. Amazon’s Consumer business just turned off its final Oracle database (some third-party applications are tightly bound to Oracle and were not migrated),” AWS’s Jeff Barr wrote in the company blog post announcing the migration.

Over the last several years, the company has been working to move off of Oracle databases, but it’s not an easy task to move projects on Amazon scale. Barr wrote there were lots of reasons the company wanted to make the move. “Over the years we realized that we were spending too much time managing and scaling thousands of legacy Oracle databases. Instead of focusing on high-value differentiated work, our database administrators (DBAs) spent a lot of time simply keeping the lights on while transaction rates climbed and the overall amount of stored data mounted,” he wrote.

More than 100 consumer services have been moved to AWS databases including customer-facing tools like Alexa, Amazon Prime and Twitch among others. It also moved internal tools like AdTech, its fulfillment system, external payments and ordering. These are not minor matters. They are the heart and soul of Amazon’s operations.

Each team moved the Oracle database to an AWS database service like Amazon DynamoDB, Amazon Aurora, Amazon Relational Database Service (RDS), and Amazon Redshift. Each group was allowed to choose the service they wanted, based on its individual needs and requirements.

 


By Ron Miller

Annual Extra Crunch members can receive $1,000 in AWS credits

We’re excited to announce a new partnership with Amazon Web Services for annual members of Extra Crunch. Starting today, qualified annual members can receive $1,000 in AWS credits. You also must be a startup founder to claim this Extra Crunch community perk.

AWS is the premier service for your application hosting needs, and we want to make sure our community is well-resourced to build. We understand that hosting and infrastructure costs can be a major hurdle for tech startups, and we’re hoping that this offer will help better support your team.

What’s included in the perk:

  • $1,000 in AWS Promotional Credit valid for 1 year
  • 2 months of AWS Business Support
  • 80 credits for self-paced labs

Applications are processed in 7-10 days, once an application is received. Companies may not be eligible for AWS Promotional Credits if they previously received a similar or greater amount of credit. Companies may be eligible to be “topped up” to a higher credit amount if they previously received a lower credit.

In addition to the AWS community perk, Extra Crunch members also get access to how-tos and guides on company building, intelligence on what’s happening in the startup ecosystem, stories about founders and exits, transcripts from panels at TechCrunch events, discounts on TechCrunch events, no banner ads on TechCrunch.com and more. To see a full list of the types of articles you get with Extra Crunch, head here.

You can sign up for annual Extra Crunch membership here.

Once you are signed up, you’ll receive a welcome email with a link to the AWS offer. If you are already an annual Extra Crunch member, you will receive an email with the offer at some point today. If you are currently a monthly Extra Crunch subscriber and want to upgrade to annual in order to claim this deal, head over to the “my account” section on TechCrunch.com and click the “upgrade” button.

This is one of several new community perks we’ve been working on for Extra Crunch members. Extra Crunch members also get 20% off all TechCrunch event tickets (email [email protected] with the event name to receive a discount code for event tickets). You can learn more about our events lineup here. You also can read about our Brex community perk here.


By Travis Bernard

Nvidia and VMware team up to make GPU virtualization easier

Nvidia today announced that it has been working with VMware to bring its virtual GPU technology (vGPU) to VMware’s vSphere and VMware Cloud on AWS. The company’s core vGPU technology isn’t new, but it now supports server virtualization to enable enterprises to run their hardware-accelerated AI and data science workloads in environments like VMware’s vSphere, using its new vComputeServer technology.

Traditionally (as far as that’s a thing in AI training), GPU-accelerated workloads tend to run on bare metal servers, which were typically managed separately from the rest of a company’s servers.

“With vComputeServer, IT admins can better streamline management of GPU accelerated
virtualized servers while retaining existing workflows and lowering overall operational costs,” Nvidia explains in today’s announcement. This also means that businesses will reap the cost benefits of GPU sharing and aggregation, thanks to the improved utilization this technology promises.

vComputeServer works with VMware Sphere, vCenter and vMotion, as well as VMware Cloud. Indeed, the two companies are using the same vComputeServer technology to also bring accelerated GPU services to VMware Cloud on AWS. This allows enterprises to take their containerized applications and from their own data center to the cloud as needed — and then hook into AWS’s other cloud-based technologies.

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“From operational intelligence to artificial intelligence, businesses rely on GPU-accelerated computing to make fast, accurate predictions that directly impact their bottom line,” said Nvidia founder and CEO Jensen Huang . “Together with VMware, we’re designing the most advanced and highest performing GPU- accelerated hybrid cloud infrastructure to foster innovation across the enterprise.”


By Frederic Lardinois

Enterprise software is hot — who would have thought?

Once considered the most boring of topics, enterprise software is now getting infused with such energy that it is arguably the hottest space in tech.

It’s been a long time coming. And it is the developers, software engineers and veteran technologists with deep experience building at-scale technologies who are energizing enterprise software. They have learned to build resilient and secure applications with open-source components through continuous delivery practices that align technical requirements with customer needs. And now they are developing application architectures and tools for at-scale development and management for enterprises to make the same transformation.

“Enterprise had become a dirty word, but there’s a resurgence going on and Enterprise doesn’t just mean big and slow anymore,” said JD Trask, co-founder of Raygun enterprise monitoring software. “I view the modern enterprise as one that expects their software to be as good as consumer software. Fast. Easy to use. Delivers value.”

The shift to scale out computing and the rise of the container ecosystem, driven largely by startups, is disrupting the entire stack, notes Andrew Randall, vice president of business development at Kinvolk.

In advance of TechCrunch’s first enterprise-focused event, TC Sessions: Enterprise, The New Stack examined the commonalities between the numerous enterprise-focused companies who sponsor us. Their experiences help illustrate the forces at play behind the creation of the modern enterprise tech stack. In every case, the founders and CTOs recognize the need for speed and agility, with the ultimate goal of producing software that’s uniquely in line with customer needs.

We’ll explore these topics in more depth at The New Stack pancake breakfast and podcast recording at TC Sessions: Enterprise. Starting at 7:45 a.m. on Sept. 5, we’ll be serving breakfast and hosting a panel discussion on “The People and Technology You Need to Build a Modern Enterprise,” with Sid Sijbrandij, founder and CEO, GitLab, and Frederic Lardinois, enterprise writer and editor, TechCrunch, among others. Questions from the audience are encouraged and rewarded, with a raffle prize awarded at the end.

Traditional virtual machine infrastructure was originally designed to help manage server sprawl for systems-of-record software — not to scale out across a fabric of distributed nodes. The disruptors transforming the historical technology stack view the application, not the hardware, as the main focus of attention. Companies in The New Stack’s sponsor network provide examples of the shift toward software that they aim to inspire in their enterprise customers. Portworx provides persistent state for containers; NS1 offers a DNS platform that orchestrates the delivery internet and enterprise applications; Lightbend combines the scalability and resilience of microservices architecture with the real-time value of streaming data.

“Application development and delivery have changed. Organizations across all industry verticals are looking to leverage new technologies, vendors and topologies in search of better performance, reliability and time to market,” said Kris Beevers, CEO of NS1. “For many, this means embracing the benefits of agile development in multicloud environments or building edge networks to drive maximum velocity.”

Enterprise software startups are delivering that value, while they embody the practices that help them deliver it.

The secrets to speed, agility and customer focus

Speed matters, but only if the end result aligns with customer needs. Faster time to market is often cited as the main driver behind digital transformation in the enterprise. But speed must also be matched by agility and the ability to adapt to customer needs. That means embracing continuous delivery, which Martin Fowler describes as the process that allows for the ability to put software into production at any time, with the workflows and the pipeline to support it.

Continuous delivery (CD) makes it possible to develop software that can adapt quickly, meet customer demands and provide a level of satisfaction with benefits that enhance the value of the business and the overall brand. CD has become a major category in cloud-native technologies, with companies such as CircleCI, CloudBees, Harness and Semaphore all finding their own ways to approach the problems enterprises face as they often struggle with the shift.

“The best-equipped enterprises are those [that] realize that the speed and quality of their software output are integral to their bottom line,” Rob Zuber, CTO of CircleCI, said.

Speed is also in large part why monitoring and observability have held their value and continue to be part of the larger dimension of at-scale application development, delivery and management. Better data collection and analysis, assisted by machine learning and artificial intelligence, allow companies to quickly troubleshoot and respond to customer needs with reduced downtime and tight DevOps feedback loops. Companies in our sponsor network that fit in this space include Raygun for error detection; Humio, which provides observability capabilities; InfluxData with its time-series data platform for monitoring; Epsagon, the monitoring platform for serverless architectures and Tricentis for software testing.

“Customer focus has always been a priority, but the ability to deliver an exceptional experience will now make or break a “modern enterprise,” said Wolfgang Platz, founder of Tricentis, which makes automated software testing tools. “It’s absolutely essential that you’re highly responsive to the user base, constantly engaging with them to add greater value. This close and constant collaboration has always been central to longevity, but now it’s a matter of survival.”

DevOps is a bit overplayed, but it still is the mainstay workflow for cloud-native technologies and critical to achieving engineering speed and agility in a decoupled, cloud-native architecture. However, DevOps is also undergoing its own transformation, buoyed by the increasing automation and transparency allowed through the rise of declarative infrastructure, microservices and serverless technologies. This is cloud-native DevOps. Not a tool or a new methodology, but an evolution of the longstanding practices that further align developers and operations teams — but now also expanding to include security teams (DevSecOps), business teams (BizDevOps) and networking (NetDevOps).

“We are in this constant feedback loop with our customers where, while helping them in their digital transformation journey, we learn a lot and we apply these learnings for our own digital transformation journey,” Francois Dechery, chief strategy officer and co-founder of CloudBees, said. “It includes finding the right balance between developer freedom and risk management. It requires the creation of what we call a continuous everything culture.”

Leveraging open-source components is also core in achieving speed for engineering. Open-source use allows engineering teams to focus on building code that creates or supports the core business value. Startups in this space include Tidelift and open-source security companies such as Capsule8. Organizations in our sponsor portfolio that play roles in the development of at-scale technologies include The Linux Foundation, the Cloud Native Computing Foundation and the Cloud Foundry Foundation.

“Modern enterprises … think critically about what they should be building themselves and what they should be sourcing from somewhere else,” said Chip Childers, CTO of Cloud Foundry Foundation . “Talented engineers are one of the most valuable assets a company can apply to being competitive, and ensuring they have the freedom to focus on differentiation is super important.”

You need great engineering talent, giving them the ability to build secure and reliable systems at scale while also the trust in providing direct access to hardware as a differentiator.

Is the enterprise really ready?

The bleeding edge can bleed too much for the likings of enterprise customers, said James Ford, an analyst and consultant.

“It’s tempting to live by mantras like ‘wow the customer,’ ‘never do what customers want (instead build innovative solutions that solve their need),’ ‘reduce to the max,’ … and many more,” said Bernd Greifeneder, CTO and co-founder of Dynatrace . “But at the end of the day, the point is that technology is here to help with smart answers … so it’s important to marry technical expertise with enterprise customer need, and vice versa.”

How the enterprise adopts new ways of working will affect how startups ultimately fare. The container hype has cooled a bit and technologists have more solid viewpoints about how to build out architecture.

One notable trend to watch: The role of cloud services through projects such as Firecracker. AWS Lambda is built on Firecracker, the open-source virtualization technology, built originally at Amazon Web Services . Firecracker serves as a way to get the speed and density that comes with containers and the hardware isolation and security capabilities that virtualization offers. Startups such as Weaveworks have developed a platform on Firecracker. OpenStack’s Kata containers also use Firecracker.

“Firecracker makes it easier for the enterprise to have secure code,” Ford said. It reduces the surface security issues. “With its minimal footprint, the user has control. It means less features that are misconfigured, which is a major security vulnerability.”

Enterprise startups are hot. How they succeed will determine how well they may provide a uniqueness in the face of the ever-consuming cloud services and at-scale startups that inevitably launch their own services. The answer may be in the middle with purpose-built architectures that use open-source components such as Firecracker to provide the capabilities of containers and the hardware isolation that comes with virtualization.

Hope to see you at TC Sessions: Enterprise. Get there early. We’ll be serving pancakes to start the day. As we like to say, “Come have a short stack with The New Stack!”


By Frederic Lardinois

Amazon acquires flash-based cloud storage startup E8 Storage

Amazon has acquired Isreali storage tech startup E8 Storage, as first reported to Reuters, CNBC and Globes and confirmed by TechCrunch. The acquisition will bring the team and technology from E8 in to Amazon’s existing Amazon Web Services center in Tel Aviv, per reports.

E8 Storage’s particular focus was on building storage hardware that employs flash-based memory to deliver faster performance than competing offerings, according to its own claims. How exactly AWS intends to use the company’s talent or assets isn’t yet known, but it clearly lines up with their primary business.

AWS acquisitions this year include TSO Logic, a Vancouver-based startup that optimizes data center workload operating efficiency, and Israel-based CloudEndure, which provides data recovery services in the event of a disaster.


By Darrell Etherington

Capital One CTO George Brady will join us at TC Sessions: Enterprise

When you think of old, giant mainframes that sit in the basement of a giant corporation, still doing the same work they did 30 years ago, chances are you’re thinking about a financial institution. It’s the financial enterprises, though, that are often leading the charge in bringing new technologies and software development practices to their employees and customers. That’s in part because they are in a period of disruption that forces them to become more nimble. Often, this means leaving behind legacy technology and embracing the cloud.

At TC Sessions Enterprise, which is happening on September 5 in San Francisco, Capital One executive VP in charge of its technology operations, George Brady, will talk about the company’s journey from legacy hardware and software to embracing the cloud and open source, all while working in a highly regulated industry. Indeed, Capital One was among the first companies to embrace the Facebook-led Open Compute project and it’s a member of the Cloud Native Computing Foundation. It’s this transformation at Captial One that Brady is leading.

At our event, Brady will join a number of other distinguished panelists to specifically talk about his company’s journey to the cloud. There, Captial One is using serverless compute, for example, to power its Credit Offers API using AWS’s Lambda service, as well as a number of other cloud technologies.

Before joining Capital One in 2014 as its CTO in 2014, Brady ran Fidelity Investment’s global enterprise infrastructure team from 2009 to 2014 and served as Goldman Sachs’ head of global business applications infrastructure before that.

Currently, he leads cloud application and platform productization for Capital One. Part of that portfolio is Critical Stack, a secure container orchestration platform for the enterprise. Capital One’s goal with this work is to help companies across industries become more compliant, secure and cost-effective operating in the public cloud.

Early bird tickets are still on sale for $249, grab yours today before we sell out.

Student tickets are for just $75 – grab them here.


By Frederic Lardinois

AWS is now making Amazon Personalize available to all customers

Amazon Personalize, first announced during AWS re:Invent last November, is now available to all Amazon Web Services customers. The API enables developers to add custom machine learning models to their apps, including ones for personalized product recommendations, search results and direct marketing, even if they don’t have machine learning experience.

The API processes data using algorithms originally created for Amazon’s own retail business,  but the company says all data will be “kept completely private, owned entirely by the customer.” The service is now available to AWS users in three U.S. regions, East (Ohio), East (North Virginia) and West (Oregon), two Asia Pacific regions (Tokyo and Singapore) and Ireland in the European Union, with more regions to launch soon.

AWS customers who have already added Amazon Personalize to their apps include Yamaha Corporation of America, Subway, Zola and Segment. In Amazon’s press release, Yamaha Corporation of America Director of Information Technology Ishwar Bharbhari said Amazon Personalize “saves us up to 60% of the time needed to set up and tune the infrastructure and algorithms for our machine learning models when compared to building and configuring the environment on our own.”

Amazon Personalize’s pricing model charges five cents per GB of data uploaded to Amazon Personalize and 24 cents per training hour used to train a custom model with their data. Real-time recommendation requests are priced based on how many are uploaded, with discounts for larger orders.


By Catherine Shu

AWS remains in firm control of the cloud infrastructure market

It has to be a bit depressing to be in the cloud infrastructure business if your name isn’t Amazon. Sure, there’s a huge, growing market, and the companies behind Amazon are growing even faster. Yet it seems no matter how fast they grow, Amazon remains a dot on the horizon.

It seems inconceivable that AWS can continue to hold sway over such a large market for so long, but as we’ve pointed out before, it has been able to maintain its position through true first-mover advantage. The other players didn’t even show up until several years after Amazon launched its first service in 2006, and they are paying the price for their failure to see the way computing would change the way Amazon did.

They certainly see it now, whether it’s IBM, Microsoft or Google, or Tencent and Alibaba, both of which are growing fast in the China/Asia markets. All of these companies are trying to find the formula to help differentiate themselves from AWS and give them some additional market traction.

Cloud market growth

Interestingly, even though companies have begun to move with increasing urgency to the cloud, the pace of growth slowed a bit in the first quarter to a 42 percent rate, according to data from Synergy Research, but that doesn’t mean the end of this growth cycle is anywhere close.


By Ron Miller

AWS expands cloud infrastructure offerings with new AMD EPYC-powered T3a instances

Amazon is always looking for ways to increase the options it offers developers in AWS, and to that end, today it announced a bunch of new AMD EPYC-powered T3a instances. These were originally announced at the end of last year at re:Invent, AWS’s annual customer conference.

Today’s announcement is about making these chips generally available. They have been designed for a specific type of burstable workload, where you might not always need a sustained amount of compute power.

“These instances deliver burstable, cost-effective performance and are a great fit for workloads that do not need high sustained compute power but experience temporary spikes in usage. You get a generous and assured baseline amount of processing power and the ability to transparently scale up to full core performance when you need more processing power, for as long as necessary,” AWS’s Jeff Barr wrote in a blog post.

These instances are build on the AWS Nitro System, Amazon’s custom networking interface hardware that the company has been working on for the last several years. The primary components of this system include the Nitro Card I/O Acceleration, Nitro Security Chip and the Nitro Hypervisor.

Today’s release comes on top of the announcement last year that the company would be releasing EC2 instances powered by Arm-based AWS Graviton Processors, another option for developers, who are looking for a solution for scale-out workloads.

It also comes on the heels of last month’s announcement that it was releasing EC2 M5 and R5 instances, which use lower-cost AMD chips. These are also built on top of the Nitro System.

The EPCY processors are available starting today in seven sizes in your choice of spot instances, reserved instances or on-demand, as needed. They are available in US East in northern Virginia, US West in Oregon, Europe in ireland, US East in Ohio and Asia-Pacific in Singapore.


By Ron Miller

Docker developers can now build Arm containers on their desktops

Docker and Arm today announced a major new partnership that will see the two companies collaborate in bringing improved support for the Arm platform to Docker’s tools.

The main idea here is to make it easy for Docker developers to build their applications for the Arm platform right from their x86 desktops and then deploy them to the cloud (including the Arm-based AWS EC2 A1 instances), edge and IoT devices. Developers will be able to build their containers for Arm just like they do today, without the need for any cross-compliation.

This new capability, which will work for applications written in Javascript/Node.js, Python, Java, C++, Ruby, .NET core, Go, Rust and PHP, will become available as a tech preview next week, when Docker hosts its annual North American developer conference in San Francisco.

Typically, developers would have to build the containers they want to run on the Arm platform on an Arm-based server. With this system, which is the first result of this new partnership, Docker essentially emulates an Arm chip on the PC for building these images.

“Overnight, the 2 million Docker developers that are out there can use the Docker commands they already know and become Arm developers,” Docker EVP of Business Development David Messina told me. “Docker, just like we’ve done many times over, has simplified and streamlined processes and made them simpler and accessible to developers. And in this case, we’re making x86 developers on their laptops Arm developers overnight.”

Given that cloud-based Arm servers like Amazon’s A1 instances are often signficantly cheaper than x86 machines, users can achieve some immediate cost benefits by using this new system and running their containers on Arm.

For Docker, this partnership opens up new opportunities, especially in areas where Arm chips are already strong, including edge and IoT scenarios. Arm, similarly, is interested in strengthening its developer ecosystem by making it easier to develop for its platform. The easier it is to build apps for the platform, the more likely developers are to then run them on servers that feature chips from Arm’s partners.

“Arm’s perspective on the infrastructure really spans all the way from the endpoint, all the way through the edge to the cloud data center, because we are one of the few companies that have a presence all the way through that entire path,” Mohamed Awad, Arm’s VP of Marketing, Infrastructure Line of Business, said. “It’s that perspective that drove us to make sure that we engage Docker in a meaningful way and have a meaningful relationship with them. We are seeing compute and the infrastructure sort of transforming itself right now from the old model of centralized compute, general purpose architecture, to a more distributed and more heterogeneous compute system.”

Developers, however, Awad rightly noted, don’t want to have to deal with this complexity, yet they also increasingly need to ensure that their applications run on a wide variety of platform and that they can move them around as needed. “For us, this is about enabling developers and freeing them from lock-in on any particular area and allowing them to choose the right compute for the right job that is the most efficient for them,” Awad said.

Mesina noted that the promise of Docker has long been to remove the dependence of applications from the infrastructure they run on. Adding Arm support simply extends this promise to an additional platform. He also stressed that the work on this was driven by the company’s enterprise customers. These are the users who have already set up their systems for cloud-native development with Docker’s tools — at least for their x86 development. Those customers are now looking at developing for their edge devices, too, and that often means developing for Arm-based devices.

Awad and Messina both stressed that developers really don’t have to learn anything new to make this work. All of the usual Docker commands will just work.

 


By Frederic Lardinois

The right way to do AI in security

Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI.

Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one’s job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.

Arguably, AI’s highest use case currently is to add futuristic sheen to traditional security tools, rebranding timeworn approaches as trailblazing sorcery that will revolutionize enterprise cybersecurity as we know it. The current hype cycle for AI appears to be the roaring, ferocious crest at the end of a decade that began with bubbly excitement around the promise of “big data” in information security.

But what lies beneath the marketing gloss and quixotic lust for an AI revolution in security? How did AL ascend to supplant the lustrous zest around machine learning (“ML”) that dominated headlines in recent years? Where is there true potential to enrich information security strategy for the better – and where is it simply an entrancing distraction from more useful goals? And, naturally, how will attackers plot to circumvent security AI to continue their nefarious schemes?

How did AI grow out of this stony rubbish?

The year AI debuted as the “It Girl” in information security was 2017. The year prior, MIT completed their study showing “human-in-the-loop” AI out-performed AI and humans individually in attack detection. Likewise, DARPA conducted the Cyber Grand Challenge, a battle testing AI systems’ offensive and defensive capabilities. Until this point, security AI was imprisoned in the contrived halls of academia and government. Yet, the history of two vendors exhibits how enthusiasm surrounding security AI was driven more by growth marketing than user needs.


By Arman Tabatabai

On balance, the cloud has been a huge boon to startups

Today’s startups have a distinct advantage when it comes to launching a company because of the public cloud. You don’t have to build infrastructure or worry about what happens when you scale too quickly. The cloud vendors take care of all that for you.

But last month when Pinterest announced its IPO, the company’s cloud spend raised eyebrows. You see, the company is spending $750 million a year on cloud services, more specifically to AWS. When your business is primarily focused on photos and video, and needs to scale at a regular basis, that bill is going to be high.

That price tag prompted Erica Joy, a Microsoft engineer to publish this Tweet and start a little internal debate here at TechCrunch. Startups, after all, have a dog in this fight, and it’s worth exploring if the cloud is helping feed the startup ecosystem, or sending your bills soaring as they have with Pinterest.

For starters, it’s worth pointing out that Ms. Joy works for Microsoft, which just happens to be a primary competitor of Amazon’s in the cloud business. Regardless of her personal feelings on the matter, I’m sure Microsoft would be more than happy to take over that $750 million bill from Amazon. It’s a nice chunk of business, but all that aside, do startups benefit from having access to cloud vendors?


By Ron Miller

Pixeom raises $15M for its software-defined edge computing platform

Pixeom, a startup that offers a software-defined edge computing platform to enterprises, today announced that it has raised a $15M funding round from Intel Capital, National Grid Partners and previous investor Samsung Catalyst Fund. The company plans to use the new funding to expands its go-to-market capacity and invest in product development.

If the Pixeom name sounds familiar, that may be because you remember it as a Raspberry Pie-based personal cloud platform. Indeed, that’s the service the company first launched back in 2014. It quickly pivoted to an enterprise model, though. As Pixeom CEO Sam Nagar told me, that pivot came about after a conversation the company had with Samsung about adopting its product for that company’s needs. In addition, it was also hard to find venture funding. The original Pixeom device allowed users to set up their own personal cloud storage and other applications at home. While there is surely a market for these devices, especially among privacy conscious tech enthusiasts, it’s not massive, especially as users became more comfortable with storing their data in the cloud. “One of the major drivers [for the pivot] was that it was actually very difficult to get VC funding in an industry where the market trends were all skewing towards the cloud,” Nagar told me.

At the time of its launch, Pixeom also based its technology on OpenStack, the massive open source project that helps enterprises manage their own data centers, which isn’t exactly known as a service that can easily be run on a single machine, let alone a low-powered one. Today, Pixeom uses containers to ship and manage its software on the edge.

What sets Pixeom apart from other edge computing platforms is that it can run on commodity hardware. There’s no need to buy a specific hardware configuration to run the software, unlike Microsoft’s Azure Stack or similar services. That makes it significantly more affordable to get started and allows potential customers to reuse some of their existing hardware investments.

Pixeom brands this capability as ‘software-defined edge computing’ and there is clearly a market for this kind of service. While the company hasn’t made a lot of waves in the press, more than a dozen Fortune 500 companies now use its services. With that, the company now has revenues in the double-digit millions and its software manages more than a million devices worldwide.

As is so often the case in the enterprise software world, these clients don’t want to be named, but Nagar tells me that they include one of the world’s largest fast food chains, for example, which uses the Pixeom platform in its stores.

On the software side, Pixeom is relatively cloud agnostic. One nifty feature of the platform is that it is API-compatible with Google Cloud Platform, AWS and Azure and offers an extensive subset of those platforms’ core storage and compute services, including a set of machine learning tools. Pixeom’s implementation may be different, but for an app, the edge endpoint on a Pixeom machine reacts the same way as its equivalent endpoint on AWS, for example.

Until now, Pixeom mostly financed its expansion — and the salary of its over 90 employees — from its revenue. It only took a small funding round when it first launched the original device (together with a Kickstarter campaign). Technically, this new funding round is part of this, so depending on how you want to look at this, we’re either talking about a very large seed round or a Series A round.


By Frederic Lardinois

Vizion.ai launches its managed Elasticsearch service

Setting up Elasticsearch, the open-source system that many companies large and small use to power their distributed search and analytics engines, isn’t the hardest thing. What is very hard, though, is to provision the right amount of resources to run the service, especially when your users’ demand comes in spikes, without overpaying for unused capacity. Vizion.ai’s new Elasticsearch Service does away with all of this by essentially offering Elasticsearch as a service and only charging its customers for the infrastructure they use.

Vizion’s service automatically scales up and down as needed. It’s a managed service and delivered as a SaaS platform that can support deployments on both private and public clouds, with full API compatibility with the standard Elastic stack that typically includes tools like Kibana for visualizing data, Beats for sending data to the service and Logstash for transforming the incoming data and setting up data pipelines. Users can easily create several stacks for testing and development, too, for example.

Vizion.ai GM and VP Geoff Tudor

“When you go into the AWS Elasticsearch service, you’re going to be looking at dozens or hundreds of permutations for trying to build your own cluster,” Vision.ai’s VP and GM Geoff Tudor told me. “Which instance size? How many instances? Do I want geographical redundancy? What’s my networking? What’s my security? And if you choose wrong, then that’s going to impact the overall performance. […] We do balancing dynamically behind that infrastructure layer.” To do this, the service looks at the utilization patterns of a given user and then allocates resources to optimize for the specific use case.

What Vizion has done here is take some of the work from its parent company Panzura, a multi-cloud storage service for enterprises that has plenty of patents around data caching, and applied it to this new Elasticsearch service.

There are obviously other companies that offer commercial Elasticsearch platforms already. Tudor acknowledges this, but argues that his company’s platform is different. With other products, he argues, you have to decide on the size of your block storage for your metadata upfront, for example, and you typically want SSDs for better performance, which can quickly get expensive. Thanks to Panzura’s IP, Vizion.ai is able to bring down the cost by caching recent data on SSDs and keeping the rest in cheaper object storage pools.

He also noted that the company is positioning the overall Vizion.ai service, with the Elasticsearch service as one of the earliest components, as a platform for running AI and ML workloads. Support for TensorFlow, PredictionIO (which plays nicely with Elasticsearch) and other tools is also in the works. “We want to make this an easy serverless ML/AI consumption in a multi-cloud fashion, where not only can you leverage the compute, but you can also have your storage of record at a very cost-effective price point.”


By Frederic Lardinois