Simple, Predictable Pricing

Flexible options for any data size. No surprise consumption costs.

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  • 7-day access to all Molecula’s Cloud features OR
  • Download single-node Self-Managed version
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Molecula's Cloud

Fully managed FeatureBase on AWS

  • From $1,000 /month
  • Standard sizing options
  • Monthly subscription
  • Commit discounts
  • Premium support only available with annual contract
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Self-Managed

FeatureBase Enterprise for on-premise or private cloud

  • From $1,000 /month
  • Standard sizing options
  • Annual contract required
  • Commit discounts
  • Premium support included
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Included in All Paid Plans

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Expert Onboarding

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8×5 Tech Support

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Unlimited Users

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Secured Data

FeatureBase Standard Database Sizes and Pricing*

Memory Allocation
16GB
32GB
64GB
128GB
256GB
512GB
1TB
Monthly Subscription Price**
$1,000
$2,000
$3,000
$5,000
$9,000
$15,500
$25,500
Annual Price**
$12,000
$24,000
$36,000
$60,000
$108,000
$186,000
$306,000
16GB
32GB
$1,000
$2,000
$12,000
$24,000
   
64GB
128GB
$3,000
$5,000
$36,000
$60,000
256GB
512GB
$9,000
$15,500
$108,000
$186,000
   
1TB
 
$25,500
 
$306,000
 

*Each column represents a single FeatureBase database with the associated allocated memory resources. You are able to purchase multiple databases of any size. 

**Pricing is the same for both Molecula’s Cloud and Self-Managed plans. These prices do not reflect commit discounts. 

 

Molecula's Cloud

Experience FeatureBase as a fully-managed cloud database service.

FeatureBase is flexible to fit your needs based on your unique use case requirements. Our Expert Onboarding team will work closely with you to ensure your success.

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Fully Managed

We take away the stress of infrastructure management, backups, disaster recovery, security, upgrades, and ongoing maintenance to deliver an always-on service.

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Scale With Ease

Easily add another database and increase the size of supported data to fit your use cased.

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Push Based Ingest

Simple to connect to your data source via a REST API.

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Secure by Design

In addition to SOC 2 Type 2 compliance, all data is encrypted in motion and at rest, and the service is architected to support access of least privilege principle.

Frequently Asked Questions

In case you don't find an answer here, write to us at [email protected]

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Based on the user’s data volume and data compression ratio realized, users will select a size that meets their storage requirement and will pay a monthly subscription fee. Compute and ingest is included within the pricing so users do not have to worry about paying to use the database.

Users will realize different data compression ratios based on their data structure and cardinality. Although many customers experience significantly greater data compression ratios, we recommend starting with an assumption of 10x. For example, if you have 1 TB of data stored in a traditional columnar data store, we recommend that you start with an instance size that supports the storage of 100GB of data. In this scenario, the user would likely choose the 128 GB storage size. In order to optimize for costs, an alternative to assuming a 10x data compression ratio would be to ingest 10% of your dataset and compare storage sizes between what is stored in FeatureBase and your previous system. You can then use the data compression ratio seen for the subset of your dataset and extrapolate that to determine what size to select.

Yes! We encourage users of FeatureBase to use the database and drive value from it in a way such that pricing is predictable for our customers. For this reason, we don’t charge extra for compute costs. Most databases have several pricing levers such as the hours an instance is in use, storage, and ingest whereas FeatureBase only has one -- your compressed data volume. Most users of FeatureBase are powering continuous analysis where workloads are always “on” and we’ve found that our customers enjoy having a predictable subscription price based on their data volume rather than paying for compute costs separately. Put your data to work!

Annual commit discounts.

FeatureBase’s underlying feature-oriented data format is predicated on roaring bitmaps which have been made more versatile to power a variety of user-facing analytic use cases. Molecula takes a new approach and similar to the transition of data formats from row-oriented to columnar, FeatureBase uses a highly optimized data format that achieves 10-100x data compression ratios while still maintaining the data’s full fidelity. The data format breaks out columnar data by creating a unique row for each feature value and represents those values as 1s when present for a particular record to allow for blazing fast queries. Large-scale analytics transitioned away from row-oriented formats to column-oriented formats because computers are optimized to read arrays and our binary representation of data is the next fundamental advancement in how to store data for large-scale and/or real-time analytics. Based on your data structure and level of cardinality, the data compression ratio realized changes. Higher cardinality workloads will see closer to a 10x compression ratio while lower cardinality workloads will see closer to a 100x compression ratio.

Absolutely! Users of FeatureBase can purchase multiple of a size. If your workload contains 10TB of compressed data, we recommend purchasing five 2TB sizes.

Dedicated Delivery Team to work through migration. Access to FeatureBase Documentation and Training Materials. Dedicated Slack Channel to Delivery Team for Support. 24/7 Enterprise Support of Production instances of FeatureBase.

Requirements and Information Gathering Phase: Molecula will work with the customer to understand their need for FeatureBase (or "to understand their Business Case") by leading them through a series of questions and documentation. Development Phase: Molecula will assist the customer in creating queries, scripts, and data models to complete the data ingestion. Performance and Optimization Tuning Phase: Molecula will take the customer through the process of deployment and will fine-tune the system for optimal output.

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