Cloud orchestration cost optimization

The move to the cloud promised to save users money and give them transparent insights into their usage and costs.

However, the opposite has happened. A 2025 report from AAG stated that around 82% of respondents found cloud spending challenging. A cloudzero report from 2024 states that more than 20% of respondents had no clear idea of their cloud costs, with reports for large users sometimes consisting of page after page with row upon row of usage data.

This is compounded by many companies and teams using multiple cloud providers for a hybrid cloud strategy to provide redundancy in case one provider experiences outages or issues. Instead of taking advantage of the flexibility of cloud-native computing, many engineers still build as if they are using fixed on-premise servers, over-provisioning instances far beyond the capacity they need. All these factors mean that users overspend on idle and duplicated services and incur ingress and egress costs between providers.

Ignoring financial costs, complex cloud orchestration also brings costs in terms of energy impact, latency, and speed issues as multiple software services pass bits and bytes back and forth across continents.

Most crucially, cloud applications write and access vast quantities of data, which is rarely stored in the same place it is accessed and processed, causing yet more latency and cost.

It’s hard to get accurate cost comparisons between cloud providers, but according to cast.ai, a company specializing in cloud optimization, a rough comparison is the following for on-demand rates.

For spot instances, with comparable services on Azure and GCP.

Common Cloud Cost Optimization Techniques

To combat rising expenses, organizations employ various common techniques, including:

• Rightsizing resources involves continuously monitoring resource utilization and performance metrics to ensure you use appropriately sized instances and services. By matching infrastructure to workloads’ demands, organizations can eliminate the waste associated with over-provisioning and ensure they pay only for the capacity they need.
• Leveraging pricing models and purchase options: Cloud providers offer pricing models beyond standard on-demand rates. For example, AWS offers Reserved Instances (RIs) and Savings Plans that provide discounts in exchange for committing to a certain level of usage. For fault-tolerant workloads that can handle interruptions, Spot Instances offer access to spare cloud capacity at reduced prices, but availability can fluctuate.
• Implementing auto-scaling to automatically adjust the number of compute resources allocated to an application based on real-time demand, scaling up during peak times and scaling down during lulls to match capacity when needed. Automating shutdown schedules for non-production environments during off-hours also prevents paying for unutilized resources.

Limitations of Traditional Optimization

While conventional optimization techniques are valuable, they often have limitations, especially in data-intensive environments, such as:

• Increased operational complexity. Effectively managing rightsizing, reservations, spot instances, and tagging is complex and time-consuming in multi-cloud or hybrid cloud setups.
• Challenges with dynamic workloads. Accurately forecasting usage for Reserved Instances or Savings Plans is difficult for applications with variable or unpredictable demand patterns. This can lead to over-committing (paying for unused reserved capacity) or under-committing (missing out on potential savings and relying on more expensive on-demand pricing).
• Inability to address data transfer costs. Most traditional methods focus on optimizing compute and storage resource costs. However, they fail to tackle the fundamental issue driving significant expense - the cost and performance impact of moving large volumes of data between storage locations and compute services, often across different regions or cloud providers.

The Solution: Bringing distributed compute and data together

Cloud cost optimization has become a challenge for businesses seeking to leverage cloud benefits without breaking the budget. However, the right solution to cloud cost optimization may be shifting to how you think about computing.

Bacalhau offers an open-source solution that enables users to run compute and processing jobs where they generate and store data. Instead of running computations in one location that requests data from another, processes it, and sends it back to another, with Bacalhau, you can run the whole pipeline in one place.

With WASM and Docker support, you can run jobs with different programming languages, support GPUs and edge devices, and still use the same cloud services you already use for compute or storage.

This brings the crucial flexibility you need, such as location, security, and device support, without the expensive overhead.

Bacalhau runs where and when you need it, bringing compute and data together on the infrastructure you already pay for. It reduces compute processes sitting idle waiting for something to do, as it’s the infrastructure you already use.

One-line install with many possibilities

The cloud promised reduced costs and complexity, but if countless reports and solutions for managing spiraling costs and complexity are any indication, this hasn’t been successful.

When it comes to data processing, Bacalhau offers a simple and flexible solution to reduce cloud orchestration costs. Try the open-source version today, and if you want to know more about the hosted version, speak to the team to find out more.

Conclusion: Reduce costs, not flexibility

The cloud promised reduced costs and complexity, but if countless reports and solutions for managing spiraling costs and complexity are any indication, this hasn’t been successful.
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When it comes to data processing, Bacalhau offers a simple and flexible solution to reduce cloud orchestration costs. Try the open-source version today, and if you want to know more about the hosted version, speak to the team to find out more.

What's Next?

To start using Bacalhau, install Bacalhau and give it a shot.

However, if you don’t have a network and you would still like to try it out, we recommend using Expanso Cloud. Also, if you would like to set up a cluster on your own, you can do that too (we have setup guides for AWS, GCP, Azure, and many more 🙂).

Get Involved!

We welcome your involvement in Bacalhau. There are many ways to contribute, and we’d love to hear from you. Please reach out to us at any of the following locations:

• Expanso’s Website
• Bacalhau’s Website
• Bacalhau’s Bluesky
• Bacalhau’s Twitter
• Expanso’s Twitter
• TikTok
• Youtube
• Slack
• LinkedIn

Commercial Support

While Bacalhau is open-source software, the Bacalhau binaries go through the security, verification, and signing build process lovingly crafted by Expanso. You can read more about the difference between open-source Bacalhau and commercially supported Bacalhau in our FAQ. If you would like to use our pre-built binaries and receive commercial support, please contact us or get your license on Expanso Cloud!