Being a tech innovator can sometimes feel like walking a tightrope. Many innovators rely on public cloud service providers like Amazon Web Services or Google Cloud, especially if they’re trying to integrate data-hungry technologies like AI. Yet these services often come with an eye-watering price tag. That’s the bind: you can’t innovate without the cloud, but you can’t overspend on cloud storage or usage, either.
The good news is that these opposing forces are much easier to balance than you might think. This blog post describes four cloud cost optimization strategies you can use right now to lower your cloud spend. Here’s how to step down from that tightrope so you’re standing on firmer ground.
Usage-based pricing is great in theory – you only pay for what you actually need. The problem emerges when you have a large-scale environment with poor visibility. It’s easy to lose control of costs. Innovations like generative AI and large language models (LLMs) require substantial compute resources and massive training data sets, which leads to astronomical cloud costs.
In addition, the complexity of cloud billing – especially at scale – combined with the ease of purchasing new or add-on services can make it difficult to budget accurately or reduce costs effectively. Using the cloud cost optimization strategies below can help reduce compute and storage utilization expenses while improving operational and billing efficiency.
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Purely financial means, like managing reserved instances, discounts, and savings plans, offer an easy first step to optimize costs, as they require no infrastructure changes.
Reserved instances (RIs) allow users to reserve cloud capacity in a specific region for a set amount of time – one or three years. In exchange, the cloud platform provides that service at a discount, which can be as much as 75%. Generally, you’ll get the highest discount when you sign up for a three-year term for a standard RI.
Not all platforms offer RIs. Google Cloud, for instance, offers committed use discounts (CUDs) instead. CUDs are similar to RIs in that they’re based on one-year or three-year terms. To get the discount, you can buy hardware or software license commitments in advance. It’s possible to save up to 57% on machine types or GPUs and up to 70% on memory-optimized machine types.
Cloud cost optimization tools can help companies streamline specific cost-cutting tasks, like identifying and allocating cloud expenses, right-sizing compute resources, and reducing data lake storage utilization. Some examples of the features these tools may offer include:
The best cloud cost optimization tools help organizations identify and eliminate efficiencies with little to no upfront costs. For example, providers like Granica offer an outcome-based pricing model, which means you’re only charged a small percentage of whatever you save on your total cloud spend.
It’s true that cloud data lake storage is expensive at the petabyte scale, but that doesn’t mean you should archive most of your data into cold storage. Hot and warm data – data that you need to access easily and frequently – might be more expensive to manage than cold data, but it’s also far more valuable.
These days, most products and apps are starved for quality data, especially innovative products that make use of AI or machine learning. Tiering or archiving hot and warm data to colder, lower-cost tiers, however, can land companies in a vicious innovation starvation cycle:
Improve efficiency and shrink monthly costs by up to 80% with Granica Crunch, the world’s only data lake compression service.
Get a demoIt might seem counterintuitive, but it’s actually best to keep as much high-value hot and warm data in the cloud as you can reasonably afford.
Data compression is an important cloud cost optimization strategy that can reduce the size of high-priority cloud data lake files to lower storage costs. The key is to ensure your compression tools don’t affect performance or drive up compute costs (negating storage savings).
To optimize costs even further, you can use data tiering, which involves identifying your most valuable and relevant data for hot storage so you can place lower-priority data in long-term cold storage to save money.
Effective data tiering and compression lead to a more reliable and affordable AI-based product, which results in a higher ROI. You can then reinvest those higher returns into more valuable AI training sets or other strategic initiatives.
Paying for data stored in cloud data lakes is expensive, but storage fees usually aren’t the biggest contributing factor behind inflated cloud costs. The real culprit is inefficient CPU utilization.
There’s no one-size-fits-all cloud cost optimization strategy for solving this problem. Instead, it requires you to identify every last idle or orphaned resource. If a resource is entirely unused or is used inefficiently, then it’s just expensive dead weight.
Rather than draining valuable processing power, you can save or reallocate it to other resources. Ideally, you should either reconfigure idle and orphaned resources for use across an organization or delete them entirely.
A right-sizing strategy, however, is more than just a one-time action. It involves a continual auditing process that requires frequent assessments of company data resources and CPU usage needs. Although right-sizing is a core feature of many cloud cost optimization tools, cash-strapped organizations can deploy this approach manually with additional time and effort.
One effective cloud cost optimization strategy is to perform a right-sizing audit each time you integrate a new feature into your product. For instance, if you’re looking to integrate AI, now is the perfect opportunity to take a closer look at your workflows and resources.
Tagging is another great way to gain the visibility needed to keep cloud costs down, and many cloud providers like AWS include this feature in their platforms. Tag all of your instances to make sure they’re being used, and to understand exactly how they’re being used.
Granica is an AI infrastructure platform that helps organizations build a data foundation for trusted AI. Our Granica Crunch service optimizes cloud costs by:
Granica offers the industry’s first platform for improving AI dataset efficiency. Our Crunch cloud cost management service is compatible with Amazon S3 and Google Cloud Storage data lakes and can reduce your data lake costs by up to 80%. Granica’s online savings calculator can estimate your total cost reduction from a vanilla S3 or GCP baseline. From there, you can test a demo version of Crunch or deploy it immediately. There’s no upfront risk; if Crunch doesn’t reduce the cost of vanilla S3 or GCS, you pay nothing.
To get started, book a demo with our cloud cost optimization experts today.