The Rise of SaaS in Bioinformatics: Navigating Cloud Pricing Models for Your Enterprise – Part 2

In our previous blog on the rise of Software as a Service (SaaS) in bioinformatics, we explored various cloud pricing models that are reshaping how organizations access and utilize bioinformatics tools. While some models offer clear advantages, others may present significant challenges that warrant careful consideration. In Part 2, we will critically examine three additional pricing models that are emerging in the bioinformatics landscape. By understanding their limitations and potential pitfalls, organizations can make more informed decisions about which pricing structures align with their operational needs and strategic goals.

Comparing Pricing Models in the SaaS Bioinformatics Market

As the market for SaaS bioinformatics vendors grows, several pricing models have emerged. Each model has its own set of advantages and potential drawbacks. Understanding these models is crucial for making an informed decision that aligns with your organization’s needs and budget constraints. Let’s examine the most common approaches and their implications:

Model 1: Pay Per User

In this pricing structure, costs are based on the number of users accessing the platform. Organizations pay a set fee for each user who needs access to the bioinformatics tools and resources.

Pros:

  • Predictable costs based on team size, making budgeting more straightforward.
  • Can be cost-effective for small teams with intensive usage per user.

Cons:

  • Discourages providing access to everyone in a team as it scales that would typically be needed to handle increasing data volumes, potentially limiting organizational growth and efficiency.
  • Misaligned with the trend of involving more people in data analysis, which is becoming increasingly important in collaborative research environments.
  • May not accurately reflect resource usage, as not all users consume the same amount of computational resources.

The pay-per-user model can be appealing for its predictability and simplicity for budgeting. However, it may not align well with the evolving needs of teams involved with data analysis and interpretation, especially in organizations that are growing or seeking to democratize access to data analysis tools. This model can inadvertently create barriers to collaboration and limit the involvement of diverse team members in the data analysis process. Solution providers try to solve for this through the concept of ‘expert’ and ‘non-expert’ users, but not every organization will define what type of user falls into each category the same way, potentially creating an ever expanding number of custom configurations that ultimately becomes hard to track, manage and enforce.

Model 2: Tiered Model Based on Throughput

This pricing approach is based on the amount of data processed. Organizations are charged according to the amount of data that flows through the SaaS platform.

Pros:

  • Can be predictable for organizations with consistent workloads and data volumes.
  • Directly ties costs to the amount of data being processed, which if implemented in the right way can feel fair to many customers.
  • May encourage efficient use of resources and optimization of data processing pipelines.

Cons:

  • May limit using the solution for additional workloads due to concerns that this will bump the license up to the next tier, discouraging usage rather than encouraging it
  • Focuses only on workflow usage, neglecting storage costs which as mentioned previously can be significant in genomics research.
  • Can be challenging to budget for organizations with variable, increasing or unpredictable data processing needs.
  • Is effectively a tax on growth.
  • Not always clear as to what files are and are not included in the throughput calculation (e.g. is it fair or appropriate to include reference files or associated metadata in the total throughput calculation?

The throughput-based model can work well for organizations that have a clear understanding of their data processing volumes and consistent workflows. However, it may create unintended consequences, such as hesitancy to process larger datasets or explore new analyses due to cost concerns. This can potentially limit scientific discovery and/or business growth. In addition, it can also lead to organizations spending unnecessary time and resources to finding creative workarounds to avoid certain file types being included in the throughput calculation.

Model 3: Per Sample Usage Model:

This approach actually enables two flavors of licensing. The first is a hosted pay-as-you-go per sample that is inclusive of all associated costs (e.g. software license, compute, storage, download and unlimited re-analysis) for those organizations that don’t have their own cloud account. The second necessitates a platform architecture that provisions fully in a customer’s cloud account, not just for storage, but also compute. When this happens, the customer is then in full control of their usage commitments with their cloud provider (e.g. AWS), so can take advantage of any credits and discount plans that have been offered or negotiated. This therefore involves direct billing from the cloud provider for compute and storage resources, enabling the SaaS provider to pass on the cost savings resulting from not having those hard costs in the form of a much lower per sample usage (or license) fee. Furthermore, because the SaaS provider no longer has the burden of those hard costs it can offer truly flat fees for unlimited usage, meaning this model scales extremely cost-effectively. Ultimately it aims to bring the best of both worlds: the benefits of an ultra-low burden SaaS enterprise platform, together with the control over costs and IT governance that most life sciences organizations now demand.

Pros:

  • Simple to understand for scientists of all kinds – a sample is a sample no matter its size or type
  • Startups can leverage any credits offered by their cloud provider whilst enterprises can benefit from negotiated discount plans, allowing organizations to benefit from existing relationships and agreements.
  • Keeps systems and data connected to other third party applications being used by the organization, avoiding data silos and integration challenges.
  • The concept of a sample has an impact on both compute and storage costs. Platforms with this model should deliver advanced storage management capabilities with fine grain life cycle policy management that can save up to 80% on storage costs through efficient tiering and dynamic retrieval for future analysis
  • Provides flexibility to scale usage indefinitely without incurring additional license or usage costs within any 12 month period.

This model stands out for its transparency, simplicity and cost effectiveness at all sample volumes.

Making the Right Choice for Your Enterprise

When selecting a SaaS bioinformatics solution, it’s essential to consider various factors that will impact your organization’s efficiency, scalability, and bottom line. Here are key considerations to guide your decision-making process:

Price Transparency: Look for a pricing model that offers clear and predictable costs. Customers should prioritize solutions where they can easily understand how much they will pay based on their actual usage. This means only paying for what you use, with straightforward billing that clearly outlines charges, helping to avoid unexpected fees and enabling better budgeting and financial planning. 

Pricing Scalability: Ensure the pricing model supports your growth plans. Consider not just your current needs but where you expect your organization to be in the next few years. A solution that seems cost-effective now may become a burden as your data volumes and team size increase.

Existing Infrastructure Integration: Look for solutions that integrate seamlessly with your existing infrastructure including your cloud provider’s native resources (such as AWS EC2 and S3). The ability to connect with your current on-prem and cloud data storage, analysis pipelines, and collaboration tools can significantly impact the overall efficiency and adoption of the new system.

Cost Efficiency: Evaluate the long-term cost implications, including hidden fees and markups. While some models may appear cheaper initially, consider the total cost of ownership over time, including potential costs for data transfer, storage, and additional features.

Flexibility: Choose a model that adapts to your changing needs and workloads. The dynamic nature of bioinformatics work means that your computational needs may vary significantly over time. A flexible model allows you to scale up during intensive projects and scale down during quieter periods.

Security: Prioritize solutions that maintain data security and compliance within your existing environment. This way you won’t add new attack surfaces to your security posture.

Conclusion

The shift to SaaS solutions in bioinformatics offers tremendous potential for enhancing efficiency and reducing costs. By carefully evaluating the available pricing models in addition to just platform functionality, you can select a solution that not only meets your current needs but also supports your organization’s future growth and innovation. The right choice will empower your teams, accelerate research, and grow your business.

Ready to see how much your bioinformatics team can save? Take the next step by exploring Basepair’s ROI calculator for cloud storage. This powerful tool will help you quantify the potential savings and efficiency gains for your specific use cases. Don’t leave money on the table – calculate your ROI with Basepair today and make an informed decision for your enterprise’s bioinformatics future.