The Rise of SaaS in Bioinformatics: Navigating Cloud Pricing Models for Your Enterprise – Part 1

In recent years, the bioinformatics industry has witnessed a significant shift towards standardized cloud solutions offered by Software as a Service (SaaS) companies. This trend is reshaping how enterprises approach genomics data processing, offering numerous advantages while addressing the ongoing industry focus on cost optimization. For executives and professionals in the bioinformatics space, understanding the various pricing models available is crucial for making informed decisions that align with your organization’s budget and growth plans. This comprehensive guide will explore the benefits of SaaS bioinformatics solutions, compare different pricing models, and provide insights to help you choose the right approach for your enterprise.

The Advantages of Moving to a SaaS Bioinformatics Solution

Adopting a SaaS bioinformatics solution offers several key benefits that can transform how your organization handles genomic data processing and analysis. Let’s delve into these advantages:

No More Waiting in the Enterprise Queue: One of the most significant benefits of SaaS bioinformatics solutions is the ability for teams across the organization to run routine workloads without waiting in internal queues. This democratization of access significantly improves efficiency, multi-team collaboration and reduces the bottlenecks that can often result from channeling everything through bioinformatics teams. Researchers, lab technicians, and data scientists alike can all access the tools and process the data they need when they need to. This leads to faster turnaround times and more productive workflows across the entire business.

Rapid Analysis: From the wet lab to the dry lab, no queues mean that all teams can quickly analyze and make a first pass at interpreting the data, collaborating with bioinformatics on an informed question as opposed to routine activities. Ultimately this can accelerate the pace of discovery, innovation, and business servicing. This speed is crucial in the fast-paced world of genomics companies, where timely data processing can accelerate times to market.

Independent Scaling: Each team can scale their data processing independently, preventing bottlenecks. This flexibility is particularly valuable in organizations with diverse research groups working on different projects with varying computational demands. With SaaS solutions, a team working on a computationally intensive project can scale up their resources without impacting the work of other groups, ensuring optimal resource allocation across the enterprise.

Cost Savings: SaaS solutions often provide more cost-effective options compared to maintaining in-house infrastructure. By leveraging cloud resources, organizations can avoid the substantial capex and ongoing opex costs associated with building in-house. Additionally, the pay-as-you-go model of many SaaS platforms ensures that you only pay for the resources you actually use, leading to more efficient budget allocation.

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: Markup on Compute:

This pricing model operates on a pay-as-you-go basis with the cloud provider adding significant markup on compute and data costs. Users are charged for each analysis (even if it is a re-analysis on the same sample) based on the computational resources they use, plus an additional percentage markup and usually require the user to figure out data storage on their own.

Pros:

  • Easy for bioinformaticians to understand
  • Low barrier to entry
  • Transparent pricing based on actual usage.

Cons:

 

  • The not uncommon 5-10X markup can create the perception of being ‘unfair’ as AWS and other cloud providers are very transparent on how much they charge for compute.
  • This model often involves building a compute layer on top of AWS, e.g, pods. While ostensibly it gives you more flexibility when developing pipelines as you can choose a given number of CPU cores and memory, it may be inefficient for production level workflows as ultimately the jobs will run on AWS. The lack of transparency and predictability to compute resources of AWS may lead to higher costs and less optimized performance.
  • May deter customers due to an inability for the model to scale cost effectively to large-scale or long-term projects.
  • Focuses only on workflow orchestration without providing a way to cost effectively manage the data life cycle policies  that can contribute significantly to the cost of cloud computing in genomics research.

Model 2: Annual License with Ratio of Compute & Storage Credits:

This approach involves an often substantial upfront license fee combined with a mandatory ratio of additional compute and/or storage spend using the provider’s own cloud account.

Pros:

  • Simple to understand
  • Works well for medium levels of usage

Cons:

  • Requires data movement into the solution provider’s cloud account. This can lead to additional cloud costs due to duplicate storage and egress fees from cloud providers.
  • Data upload always adds an unnecessary (albeit) small security risk and means it becomes disconnected from third party applications and in house tools running elsewhere, potentially creating data silos.
  • Substantial upfront investment (often $100k+)  is often a barrier for smaller organizations or those with budget constraints, especially before the value has even been established
  • Lack of transparency: the compute & storage is often marked up vs what the provider is actually being charged by their own cloud provider
  • Does not scale cost effectively with increasing volume as compared to being in full control over usage commitments with cloud providers such as AWS.

This model is often targeted at larger enterprises that can afford the significant upfront investment. It can provide a sense of commitment and potentially better support from the provider. However, the fact that it doesn’t scale cost effectively means eventually it can discourage usage even in the Enterprise. Furthermore, the trend amongst life sciences organizations when moving more of their workloads to the cloud is to keep everything in their own accounts, not to be forced to move it to a hosted solution to get the benefits of a platform.

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.

Stay tuned for Part 2!