Since our last update of infrastructure as a service pricing trends, adoption of cloud compute continues to grow. A growing number of enterprises across verticals are pursuing cloud strategies and taking advantage of cloud services.

However, it can be difficult to properly assess how competitive cloud providers are with one another because their non-standardized packaging makes it effectively impossible to compare services on an equal footing.

To this end we offer the following deconstruction of cloud pricing for base on-demand infrastructure. This analysis is intended not as a literal expression of cost per service; this is not an attempt to estimate the actual component costs for compute, disk, and memory per provider. Instead, this analysis compares base, retail, hourly instance prices across the individual service offerings.

What this attempts to highlight is how providers may be differentiating from each other via their pricing models. In other words, it’s an attempt to answer the question for a given hourly cost, who’s offering the most compute, disk or memory?

As with previous iterations, a link to the aggregated dataset is provided, both for fact checking and to enable others to perform their own analyses, expand the scope of surveyed providers, or both.

Before we continue, a few notes

Assumptions

• No special pricing programs (beta, etc.)
• No operating system premium. Prices are based on Linux OS.
• No reserved/committed use instances. Charts are based on virtual machine price/hour costs.
• No specialized packages (i.e. no high memory, compute optimized, etc.)
• Prices are based off the lowest cost US-based region.

Objections & Responses

• “This isn’t an apples to apples comparison.”: This is true. The providers do not make that possible.
• “These are list prices. Many customers don’t pay list prices.”: This is also true. Many customers do, however. But in general, take this for what it’s worth as an evaluation of posted list prices.
• “This does not take bandwidth and other costs into account.”: Correct, this analysis is server only. No bandwidth or storage costs are included. Those should be examined separately.
• “Why isn’t price/hour on the y-axis instead of x-axis?” That’s absolutely a valid way to view the relationship between these variables, but in this analysis we’ve made a deliberate choice to frame cost as the independent variable to explore the question from the perspective of ‘given $x, how much disk/memory/compute can I get from each provider?’
• “This survey doesn’t include [provider X]”: The link to the dataset is provided. You are encouraged to fork it.

Notes About Vendors and Processes

• CenturyLink (dropped in the Q2 2020 iteration): We have numerous conversations about cloud, and in these conversations customers do not include CenturyLink in their evaluations of cloud providers. While CenturyLink continues to have a public cloud offering, for the purposes of this analysis we are no longer including them among top tier cloud service providers.
• Tencent (under review): Given the news about Tencent’s pledged investment in cloud infrastructure, we attempted to include them this iteration. Unfortunately the price list and documentation that we were able to access left many gaps in being able to assess the comparative nature of their offering, and our attempts to reach their support teams were unfruitful. Given that this analysis is designed to compare public-facing pricing of cloud offerings, we determined that based on their publicly available information we did not have sufficient information to include them this round. We will continue to work on getting our questions addressed and hope to include them in a future iteration.
• Outliers: Most providers max out the general instance shapes at or below 48 cores. However, both AWS and Google now offer shapes with 64 and 96 cores; these have been excluded from the analysis for the time being. We will continue to watch the trend of larger instances, but for now these sizes are outliers that we’ve excluded.

Notes from previous iterations are available in the footnotes. ¹

IaaS Pricing

A quick note on how to read the charts: the simplest explanation is that the steeper the slope, the better the pricing from a user perspective. The more quickly cores, disk, and memory are added relative to cost, the less a user has to pay for a given asset.

With that, here is the chart depicting the cost of disk space relative to the price per hour.

The primary story from this chart is that for most providers, storage is not a primary focus from a differentiation standpoint.

• In previous iterations we broke out pricing to compare trends between HDD and SSD storage. AWS has now converted their legacy m1 instances to SSD, and IBM is in the process of converting data centers to SSD (thus making local SSD availability dependent on the data center selected.) Given the convergence to SSD, it didn’t make sense to continue to facet the analysis on this factor.
• AWS’ m1 offering used to be an HDD offering. They have since updated to SSD but did not change the corresponding price point.
• IBM’s flat line reflects their maximum offer of 25GB of storage in their base pricing. As their overall price increases for instances with higher compute and memory, there is no corresponding increase in disk space.
• Several providers do not include local storage in their base offering. Alibaba, Google and Oracle require separate storage in their base pricing and thus are omitted from this chart.

In the context of memory per dollar, competition among providers historically has not particularly differentiated. However, Oracle is pricing more aggressively on this front. In offering more memory per dollar across all of their instances, Oracle differentiates their offering from the competition on this metric. The other providers are competitively grouped, with varying rank order amongst the providers across their various instances.

In the cloud, defining precisely what is meant by “CPU” is complicated by the fact that unlike physical hardware, every cloud instance included here is powered by a virtual compute instance. In years past we attempted to compare available compute units rather than virtual CPUs (vCPUs). As some providers have moved to stop providing explicit conversions in favor of treating vCPU implementation as a single hardware hyperthread, we have reevaluated the best way to provide an apples-to-apples comparison across providers. Without having direct visibility into a provider’s mix of physical hardware instances and how they map to available instance types, it’d be speculative on our part to try to estimate equivalent compute units. As such, we’ve fallen back on the most basic metric which we have access to, which is why the analysis now compares vCPU.

According to Oracle’s public documentation, 1 OCPU is equivalent to 2 vCPUs, and on that basis Oracle remains a pricing leader for compute, offering the highest amount of vCPU compute capacity per dollar spent. Other providers are clustered with no clear competitive standouts. This grouping continues to indicate that fewer providers are choosing to differentiate based on the pricing of their compute units.

IaaS Price History

Besides taking apart the base infrastructure pricing on a component basis, one common area of inquiry is how provider prices have changed over time. It is enormously difficult to capture changes across services on a comparative basis over time, as it’s a delicate balancing act to maintain historic comparability of this analysis while also representing the ever-changing offerings of vendors.

When we began tracking infrastructure pricing, available compute shapes commonly topped out at 16 virtual cores; now it’s common to see 32 and 48 vCPU (and sometimes more, as noted above). Historically we’ve attempted to base the analysis on services that have been offered from the initial snapshot moving forward so as to be as consistent as possible. When the goal is to compare how providers are competing on price, it’s beneficial to control for changes to instance sizes to help isolate trends in price changes. Our pricing trend snapshot thus controls for larger instances, in this case defined as those that are greater than 16 vCPU.

Please keep in mind the objective of this chart: the intent is to understand price changes on a per provider basis over time, and it is less useful to attempt to compare average prices across providers. While this chart is less useful in comparing specific price points between providers as noted above, it is interesting to look at overall trends.

IBM continues their trend of slight price reductions, but otherwise pricing when controlled for instance size has remained flat across providers for another iteration of this analysis.

The vendor notes above and in the footnotes ¹ provide additional context, but at a high level:

• HP is included through 2014
• Rackspace is included through 2016
• CenturyLink was added 2016 and included through 2019
• Oracle and Alibaba were added in 2018
• AWS and Microsoft include new generation instances as of 2018

Implications

The theme of our conclusions for this analysis has remained largely unchanged over the last few years: base infrastructure pricing is no longer the primary point of differentiation amongst cloud providers.

The variability in pricing patterns for a given resource (disk, compute, memory) has largely converged across the industry, especially compared to early days of this analysis. Now there are fewer instances where a distinct pricing strategy for a given resource notably differentiates one provider from the others.

Furthermore, the abatement of downward pricing pressure continues. When controlling for instance size, prices for standard infrastructure have largely leveled out over the last few years. Providers are largely choosing not to compete using price-based differentiation on their base compute offerings.

Pricing is essentially commoditized, but to the degree that there is still competition left in the space, Oracle has staked the most assertive position amongst providers.

Increasingly we see competition instead focused on:

1. competitive pricing of adjacent services, like data ingress and egress costs. It’s difficult to sufficiently differentiate on price among levers that have been commoditized, and as such we’re seeing examples of companies like Oracle choosing adjacent cost areas to price aggressively.
2. the unique products and services offered, as opposed to the base compute primitives themselves.
3. wider infrastructure capability, such as GPUs, bare metal offerings, and ARM support.

Standard base infrastructure is reaching a commodity status. While pricing of these instances is still important, it is no longer the primary point of competition for most cloud providers.

Disclosure: AWS, Google, IBM, Microsoft, and Oracle are current RedMonk clients. Alibaba, CenturyLink, HP/HPE, Rackspace, Samsung (Joyent), and Tencent are not currently clients.

Data: Here is a link to the dataset used in the above analysis.