NoSQL database software, five data models to pick from

What to look for, and what to ignore

What NoSQL Database Software actually costs

NoSQL database pricing is notoriously difficult to predict. Most vendors use consumption-based models where your bill depends on throughput (reads and writes per second), storage (GB stored), data transfer (GB moved in and out), and optional features like backups, encryption, and multi-region replication. The result is that two teams running the same database can pay wildly different amounts based on their access patterns. The figures below are taken from each vendor's published pricing and pricing calculator.

For a typical SaaS application serving 10,000 daily active users with moderate data volumes (50–200 GB), expect to spend $200–$1,000 per month on a managed NoSQL database. MongoDB Atlas M30 or DynamoDB on-demand mode will land at the lower end for most workloads. Multi-region deployments, heavy write workloads, and large datasets push costs toward $2,000–$10,000/month. If you are evaluating self-managed open-source versus a managed service, factor in the fully loaded cost of the engineering time required to operate the cluster. For most teams under 500 nodes, the managed service is cheaper when you include labor.

How to compare them

Data model fit for your primary access patterns

A document database excels at queries that retrieve a single entity with all its related data. A key-value store excels at high-throughput, low-latency lookups by key. A graph database excels at relationship traversal. Choosing the wrong model for your access patterns means fighting the database instead of working with it.

Ask: Map your top 5 read and write patterns. For each, ask: does this pattern require multi-entity joins, relationship traversal, time-range aggregations, or single-entity lookups? Match the dominant pattern to the database model that serves it natively.

Write throughput and read latency at your expected scale

Vendor benchmarks are always run on ideal hardware with ideal data distributions. Your production workload will have skewed key distributions, variable document sizes, and concurrent competing queries that vendor benchmarks do not reflect.

Ask: Request benchmark results for workloads similar to yours, or run your own with YCSB (Yahoo Cloud Serving Benchmark) or a custom load generator. Measure P50, P95, and P99 latency (averages hide the tail) at 80% of your expected peak throughput.

Consistency model and its implications for your application

Eventual consistency means that a write may not be immediately visible to subsequent reads from different nodes. Strong consistency means reads always see the latest write but at a cost of higher latency and lower throughput. Your application must be designed for whichever model the database uses.

Ask: What is the default consistency level? Can it be tuned per operation? What happens to read/write latency when you increase the consistency level? Does the database support transactions across multiple documents/partitions, and what is the performance cost?

Operational maturity of the managed service

A managed service is only valuable if it handles the hard operational tasks reliably: automated backups, point-in-time recovery, zero-downtime upgrades, automatic scaling, and responsive support when things go wrong.

Ask: What is the SLA (and has the vendor actually paid out credits when they missed it)? How are version upgrades handled, and is there a maintenance window with potential downtime? What is the RPO and RTO for disaster recovery? Does the service automatically scale, or do you need to manually adjust capacity?

Total cost of ownership at your projected scale over 3 years

NoSQL database costs are non-linear. A database that costs $200/month at launch may cost $20,000/month at 10x your current scale, depending on how the pricing model scales with throughput and storage growth. Conversely, a database that looks expensive at small scale may have the most favorable economics at large scale.

Ask: Build a 3-year cost model with three scenarios: current scale, 5x scale, and 20x scale. Include compute, storage, data transfer, backup, multi-region replication, and support costs. Compare managed service costs against self-managed costs including engineering labor.

Ecosystem, community, and talent availability

You will need to hire engineers who know the database, find answers to operational questions, and integrate with your existing toolchain. A database with a small community means expensive hiring, sparse Stack Overflow answers, and limited third-party tooling.

Ask: How many relevant job postings exist for this database? How active are the community forums and GitHub repositories? What monitoring, migration, and management tools exist in the ecosystem? MongoDB, Redis, and Cassandra have the largest communities; niche databases may require specialized and expensive talent.

Before you buy

1. 1Document your top 10 read and write access patterns with expected throughput (operations per second) and latency requirements (P99 target in milliseconds). This is the single most important input for choosing the right NoSQL database type and vendor.
2. 2Determine which data model fits your primary access patterns: document (flexible entities with nested data), key-value (high-speed lookups by key), column-family (write-heavy with wide rows and time-series data), graph (relationship traversal and pattern matching), or time-series (sequential, timestamped data with aggregation queries).
3. 3Estimate your data volume growth over 3 years: current size in GB, expected growth rate, and data retention requirements. This determines whether you need horizontal scaling and influences cost projections significantly.
4. 4Decide between managed service and self-managed. If you do not have a dedicated database engineering team (at least 1–2 engineers with NoSQL production experience), choose a managed service. The operational savings will exceed the price premium.
5. 5Build a 3-year total cost of ownership model with three scale scenarios (current, 5x, 20x). Include compute, storage, data transfer, backups, multi-region replication, professional services, and engineering labor for operations.
6. 6Evaluate cloud provider lock-in tolerance. DynamoDB and Cosmos DB are deeply tied to AWS and Azure respectively. MongoDB, Cassandra, and Redis run on any cloud or on-premises. Your multi-cloud strategy should inform this decision.
7. 7Assess your team's NoSQL expertise. If this is your first NoSQL deployment, MongoDB has the lowest learning curve and the largest pool of available talent. Cassandra and DynamoDB require more specialized knowledge.
8. 8Test consistency model requirements with your product team. If your application can tolerate eventual consistency (user profiles, product catalogs, activity feeds), you have more options. If you need strong consistency (financial transactions, inventory management), your options narrow to databases with strong consistency modes or NewSQL alternatives.
9. 9Run a proof of concept with production-realistic data volumes and traffic patterns for at least 2 weeks. Measure P50, P95, and P99 latency; the average will look fine. Test failure scenarios: what happens when a node goes down, when the network partitions, when storage fills up?
10. 10Verify driver support and community ecosystem for your primary programming languages. Test the drivers in your actual application framework; isolated scripts will not surface connection pooling, retry logic, and serialization issues.
11. 11Confirm backup, recovery, and disaster recovery capabilities. What is the RPO (recovery point objective) and RTO (recovery time objective) of the managed service? Can you restore to a specific point in time, or only to the last nightly snapshot?
12. 12Negotiate contract terms before committing. Annual commits with managed NoSQL vendors typically reduce costs 20–40% versus on-demand pricing. Push for the ability to scale capacity down as well as up during the contract term.