Three NoSQL databases now anchor most large-scale cloud applications, and picking the wrong one is an expensive mistake to unwind later. Apache Cassandra, Amazon DynamoDB, and Azure Cosmos DB all promise horizontal scale and high availability, but they get there through very different architectures, pricing models, and operational trade-offs. Cassandra shipped its 5.0 general-availability release on August 7, 2026, with the project’s GitHub repository sitting at roughly 10,082 stars and 4,112 forks as of early September 2026, according to third-party repository tracking. DynamoDB and Cosmos DB, by contrast, are fully managed services with no version number to track at all, just pricing tiers and consistency knobs.
This comparison walks through the current specs, pricing, consistency models, and real deployment patterns for all three databases as they stand in September 2026. It covers where each one wins on cost, where the operational burden actually lands, and how to plan a migration if you decide to switch. For a broader look at cloud data platforms, see our cloud computing hub.
What Cassandra, DynamoDB, and Cosmos DB Actually Do
Apache Cassandra is an open-source, wide-column distributed database originally built at Facebook and donated to the Apache Software Foundation. It runs on a peer-to-peer ring architecture with no single point of failure, and every node in a cluster can accept reads and writes. Teams install and operate it themselves, or pay a vendor like DataStax (through Astra DB) or Amazon (through Keyspaces) to run a Cassandra-compatible service for them. The core project remains free under the Apache 2.0 license, and its latest stable release, Cassandra 5.0, became generally available on August 7, 2026, according to the project’s own download page.
Amazon DynamoDB is a fully managed key-value and document database built and operated exclusively by AWS. There is no self-hosted version and no version number for customers to track. You create a table, choose on-demand or provisioned capacity, and AWS handles partitioning, replication, and failover behind the scenes. It integrates tightly with the rest of AWS, from Lambda triggers to DynamoDB’s native vector search capability for AI-adjacent workloads, and it already competes for document-style traffic against MongoDB, a rivalry covered in depth in our DynamoDB vs MongoDB comparison.
Azure Cosmos DB is Microsoft’s globally distributed, multi-model database service. It started as a document store but now exposes multiple APIs, including a Cassandra-compatible API, a MongoDB-compatible API, and a native NoSQL API. Cosmos DB’s defining feature is its five-tier consistency model, which gives architects far more granular control over the trade-off between latency and data freshness than either Cassandra or DynamoDB offers out of the box. Microsoft shipped a public preview of a dedicated Cosmos DB cost estimator for NoSQL workloads in June 2026, a sign of how much pricing complexity the service has accumulated as it added capacity modes.
Architecture and Data Model Differences
Cassandra organizes data into wide-column tables queried with CQL, a language deliberately modeled on SQL syntax but built for a distributed, denormalized world. There are no joins. You design tables around the queries you plan to run, often duplicating data across multiple tables to avoid expensive scatter-gather reads. Every node in a Cassandra ring is equal, replication happens through a gossip protocol, and you control how many replicas exist per datacenter through a replication strategy, most commonly NetworkTopologyStrategy for production multi-datacenter clusters.
DynamoDB uses a simpler key-value and document model. Every table needs a partition key, and optionally a sort key, and AWS handles the underlying partitioning automatically as traffic grows. There is no manual node management, no replication strategy to configure, and no cluster to patch. That simplicity is the entire value proposition: you trade architectural control for operational hands-off scaling. Global secondary indexes let you query on non-key attributes, though each one effectively doubles your write costs since AWS maintains a separate replicated index.
Cosmos DB sits somewhere between the two. Its native API supports a JSON document model with SQL-like query syntax, but the multi-API layer means the same underlying engine can also speak Cassandra’s CQL, MongoDB’s wire protocol, Gremlin for graph workloads, or a table API for key-value access. That flexibility is unusual among the three databases covered here, and it means a team already running Cassandra queries can, in theory, point that traffic at Cosmos DB’s Cassandra API without a full rewrite, though performance and consistency semantics still differ enough to require testing.
Cassandra vs DynamoDB vs Cosmos DB: Full Specs Compared
The table below lines up the core technical specs for each database as they stand in September 2026, pulled from official AWS, Microsoft, and Apache documentation.
| Spec | Apache Cassandra | Amazon DynamoDB | Azure Cosmos DB |
|---|---|---|---|
| Data model | Wide-column | Key-value / document | Multi-model (document native, plus Cassandra, MongoDB, Gremlin, Table APIs) |
| Query language | CQL | PartiQL / native API calls | SQL-like query language (native API) |
| Deployment | Self-managed, or via Astra DB / AWS Keyspaces | Fully managed (AWS only) | Fully managed (Azure only) |
| Latest version / release | Cassandra 5.0, GA August 7, 2026 | No version number (managed service) | No version number (managed service) |
| License / maintainer | Apache 2.0, Apache Software Foundation | Proprietary, AWS | Proprietary, Microsoft |
| GitHub stars | ~10,082 (Sept 2026) | Not applicable | Not applicable |
| Consistency levels | Tunable per-query (ONE, QUORUM, ALL, LOCAL_QUORUM, and more) | 2 modes: eventual or strongly consistent reads | 5 levels: Strong, Bounded Staleness, Session, Consistent Prefix, Eventual |
| Multi-region writes | Yes, native to the architecture via multi-datacenter replication | Yes, via Global Tables | Yes, via multi-region write accounts |
| Billing unit | Infrastructure cost (self-hosted) or RRU/WRU (Keyspaces) | Read/write request units (on-demand) or RCU/WCU (provisioned) | Request Units per second (RU/s) |
| Free tier | None for self-hosted; Keyspaces offers a 3-month intro tier | 25 WCU, 25 RCU, 25 GB storage per region, ongoing | 1,000 RU/s + 25 GB storage, lifetime |
| Operational overhead | High for self-hosted (node patching, compaction tuning, repairs) | Minimal, fully serverless option available | Minimal, fully serverless option available |
| Vector search support | Limited, via third-party extensions | Native, launched as a dedicated DynamoDB feature | Native, via the NoSQL API’s vector indexing |
| Best-fit cloud | Any (self-hosted) or multi-cloud via Astra DB | AWS-only workloads | Azure-only workloads, or multi-cloud via Cassandra/Mongo API compatibility |
A few of these rows deserve context. Cassandra’s tunable consistency isn’t a marketing bullet point, it’s a per-query decision that developers make constantly, trading latency for durability on a request-by-request basis. DynamoDB deliberately keeps that choice simple: pick eventual consistency for lower cost and lower latency, or strong consistency when correctness matters more. Cosmos DB’s five levels sit in between, giving teams a default (Session, the most commonly used level in production) plus four other options for edge cases.
Pricing Breakdown for September 2026
Pricing is where the three databases diverge the most, because each one bills on a different unit. DynamoDB and AWS Keyspaces charge per request, Cosmos DB charges per provisioned or consumed Request Unit, and self-hosted Cassandra charges nothing for the software itself, only for the compute, storage, and engineering time to run it.
| Pricing dimension | Apache Cassandra | Amazon DynamoDB | Azure Cosmos DB |
|---|---|---|---|
| Software license | $0 (Apache 2.0, self-hosted) | Not applicable, bundled into usage | Not applicable, bundled into usage |
| On-demand write cost | N/A (self-hosted) or Keyspaces WRU pricing | $0.625 per million write request units (US East) | $0.008 per 100 RU/s-hour, provisioned (single-region write) |
| On-demand read cost | N/A (self-hosted) or Keyspaces RRU pricing | $0.125 per million read request units (US East) | Billed on the same RU/s rate as writes; reads and writes both consume RUs |
| Provisioned write cost | N/A | $0.00065 per WCU-hour | $0.008 per 100 RU/s-hour |
| Provisioned read cost | N/A | $0.00013 per RCU-hour | Same RU/s rate as above |
| Multi-region write premium | Replication infrastructure cost only | Additional WRU cost charged per region under Global Tables | $0.016 per 100 RU/s-hour (2x the single-region rate) |
| Managed Cassandra-compatible option | DataStax Astra DB: usage-based, e.g. $0.04 per million vector dimension reads/writes | — | — |
| Serverless option | Astra DB serverless tier | On-demand mode functions as serverless billing | Serverless account type, billed per RU consumed rather than provisioned |
Read those numbers carefully, because the units are not directly comparable. A DynamoDB write request unit covers a write of up to 1 KB. A Cosmos DB Request Unit is a normalized measure of CPU, memory, and IO that varies by operation type, item size, indexing policy, and query shape, so the same logical write can cost a different number of RUs depending on how a container is configured. Microsoft’s own documentation gives a worked example: provisioning 1,000 RU/s costs $0.08 an hour, which works out to roughly $0.022 per million reads and $0.111 per million writes when normalized against a typical workload split. That puts Cosmos DB’s effective per-operation cost in a similar range to DynamoDB’s on-demand pricing for reads, but noticeably higher for writes once you factor in indexing overhead.
Cassandra’s real cost is almost entirely indirect. There’s no per-request bill, but a production-grade cluster typically needs at least three nodes per datacenter for quorum-based replication, plus the engineering time to handle compaction strategy tuning, repair scheduling, and version upgrades. That operational tax is why AWS Keyspaces and DataStax Astra DB exist: they let teams keep CQL compatibility while outsourcing the node management to a vendor, at a usage-based price closer to what DynamoDB and Cosmos DB already charge.
Free Tier and Entry-Level Costs
For teams testing all three databases before committing, the free tiers differ in both generosity and structure. DynamoDB’s free tier is ongoing rather than time-limited: every AWS account gets 25 provisioned write capacity units, 25 provisioned read capacity units, and 25 GB of storage per Region, per payer account, indefinitely, plus 2.5 million DynamoDB Streams read requests a month and 25 replicated write capacity units if you enable Global Tables across two Regions.
Cosmos DB’s free tier is also permanent rather than trial-based: the first 1,000 RU/s of throughput and 25 GB of storage are free for the life of an eligible account, with one free-tier account allowed per Azure subscription. Microsoft’s new cost estimator, released in public preview in June 2026, explicitly models this free-tier allowance alongside manual provisioned, autoscale, and serverless scenarios, which suggests enough customer confusion about Cosmos DB billing that Microsoft decided a dedicated calculator was worth building.
Cassandra has no equivalent because the open-source project isn’t a hosted service. The closest analog is AWS Keyspaces, which offers a three-month introductory tier covering 30 million read request units, 30 million write request units, and 1 GB of storage, limited to one free tier per payer account. After that window closes, billing reverts to standard on-demand Keyspaces pricing. Self-hosted Cassandra, meanwhile, is free forever in licensing terms, but you’re paying for compute and storage from day one on whatever infrastructure you deploy it to, whether that’s bare metal, EC2, or a Kubernetes cluster managed through something like EKS, AKS, or GKE.
Consistency Models Compared: Tunable, Strong, and Five Levels
Consistency is the single hardest concept to compare across these three databases, because each one built a fundamentally different mental model around it. Cassandra’s tunable consistency lets a client specify, on every single read or write, how many replicas must acknowledge the operation before it’s considered successful. Common levels include ONE, QUORUM, ALL, LOCAL_ONE, LOCAL_QUORUM, EACH_QUORUM, ANY for certain writes, and serial levels like SERIAL and LOCAL_SERIAL for lightweight transactions. LOCAL_QUORUM, which requires agreement from a majority of replicas within the local datacenter, is the most common production default because it balances durability against cross-datacenter latency.
DynamoDB simplifies this down to two choices. Eventually consistent reads may not reflect the very latest write, but they’re cheaper and faster. Strongly consistent reads guarantee you see every successful write that completed before the read began, at a higher cost and with some availability trade-offs during network partitions or Region-level failures. There’s no dial to turn beyond that binary choice, which is exactly the simplicity DynamoDB is built around.
Cosmos DB takes the middle path with five named levels: Strong, Bounded Staleness, Session, Consistent Prefix, and Eventual, according to Microsoft’s own consistency documentation. Strong gives linearizable reads across all regions at the highest latency cost. Session, the default most applications use, guarantees a single client always sees its own writes, without paying the full cost of global strong consistency. Consistent Prefix guarantees that reads never see out-of-order writes even if they lag behind. Eventual offers the weakest guarantee and the lowest latency, more or less mirroring DynamoDB’s eventually consistent mode. Bounded Staleness lets you cap exactly how stale a read can be, either in number of writes or in elapsed time, which none of the other two databases expose as a direct configuration knob.
Multi-Region Replication and Global Writes
All three databases support writes accepted in more than one Region, but the mechanics differ enough to matter for conflict-heavy workloads. Cassandra has supported multi-datacenter replication since its earliest design, because Facebook built it specifically to survive the loss of an entire datacenter. Every replica can accept writes, replication happens asynchronously through the gossip protocol, and conflict resolution relies on the consistency level chosen per query plus last-write-wins timestamp comparison at the storage layer.
DynamoDB Global Tables replicate a table across the Regions you select, with each Region able to accept both reads and writes. Replication is asynchronous, and AWS resolves conflicting writes using a last-writer-wins strategy based on timestamps. This is straightforward to reason about, but it means an application that needs custom conflict resolution logic, rather than simple last-write-wins, has to build that logic itself on top of DynamoDB Streams.
Cosmos DB multi-region write accounts let every configured Region accept writes simultaneously, and the chosen consistency level directly governs how quickly those writes converge across Regions and what a reader might see in the interim. This is also the one place where Cosmos DB’s pricing penalizes multi-region ambition directly: enabling multi-region writes roughly doubles the provisioned throughput rate, from $0.008 to $0.016 per 100 RU/s-hour, according to Microsoft’s billing documentation. DynamoDB and Cassandra both charge for the additional replicated capacity or infrastructure rather than doubling a flat per-unit rate, which makes Cosmos DB’s multi-region cost easier to predict but structurally more expensive at scale.
Benchmark Data and Documented Performance Characteristics
Independent, apples-to-apples throughput benchmarks across all three databases are hard to find and even harder to trust, since workload shape, item size, indexing policy, and Region choice all swing the results heavily. Rather than repeat an unverified number, it’s more useful to compare what each vendor’s own documentation commits to. AWS documents DynamoDB’s on-demand mode as scaling automatically to handle traffic spikes without any pre-planning, and its developer guide describes the service’s internal partitioning as designed to sustain consistent single-digit-millisecond latency regardless of table size, a claim AWS has stood behind since DynamoDB’s original 2012 launch and continues to reiterate in current documentation.
Microsoft’s Cosmos DB documentation makes a similar latency commitment, backed by service-level agreements that guarantee read and write latency at the 99th percentile, and its consistency levels documentation is explicit that stronger consistency settings trade measurable latency for stronger guarantees, rather than being free.
Apache Cassandra doesn’t publish an SLA in the way a managed service does, since there’s no vendor backing self-hosted deployments, but the project’s own architecture documentation and its release notes describe near-linear throughput scaling as nodes are added to a cluster, a design goal that has held up across more than a decade of large production deployments. The practical takeaway across all three: DynamoDB and Cosmos DB both optimize for predictable low latency out of the box with minimal tuning, while Cassandra can match or exceed either one at very large scale, but only after real operational investment in cluster sizing, compaction strategy, and hardware selection.
Cost at Scale: What 10,000 Writes Per Second Actually Costs
Per-unit pricing is abstract until you run it against a real workload. Using only the published list prices above, here’s what a sustained 10,000 writes per second, single-Region workload costs across a 30-day month on each platform, assuming 1 KB items and no secondary indexes. This is a list-price illustration, not a vendor-published benchmark, and actual bills vary with item size, indexing, reserved capacity discounts, and Region.
| Workload assumption | Amazon DynamoDB (on-demand) | Azure Cosmos DB (provisioned) | Self-hosted Cassandra |
|---|---|---|---|
| 10,000 writes/sec sustained | ≈25.92 billion WRUs/month → $16,200/month at $0.625 per million WRU | Roughly 10,000 RU/s provisioned → ≈$584/month at $0.008 per 100 RU/s-hour (before indexing overhead, which typically adds RU cost per write) | No per-request charge; cost is fixed infrastructure (typically a multi-node cluster sized for the sustained throughput plus headroom) |
| Multi-Region (2 Regions) version of the same workload | Roughly doubles, since Global Tables bill replicated write capacity per additional Region | ≈$1,168/month, since multi-region write throughput bills at $0.016 per 100 RU/s-hour, double the single-Region rate | Infrastructure cost roughly doubles for a second datacenter’s worth of nodes, plus cross-region bandwidth |
| Where the estimate breaks down | Item sizes above 1 KB, transactional writes, and any Global Secondary Indexes all add cost on top of this baseline | Indexing policy, consistency level, and query complexity change the RU cost per operation, often by a meaningful margin | Real infrastructure cost depends heavily on instance type, storage tier, replication factor, and whether the team is already running Kubernetes or bare-metal capacity that can absorb the workload |
The gap narrows or widens fast depending on item size and indexing choices, which is exactly why Microsoft built a dedicated Cosmos DB cost estimator in 2026 rather than leaving customers to do this math by hand. AWS offers similar calculators for both DynamoDB and Keyspaces. For Cassandra, the honest answer is that the “cost” question isn’t really a pricing-page lookup, it’s a capacity-planning exercise that depends on how efficiently a team can run its own cluster.
Ecosystem, Drivers, and Local Development Tooling
None of these three databases exist in isolation, and the surrounding tooling often matters as much as the core engine when a team picks one. Cassandra’s driver ecosystem is mature and broad, with official and community-maintained clients for Java, Python, Node.js, Go, C++, and more, largely because the project has existed since 2008 and accumulated language support over a long period. Object mapping and query builder libraries exist for most major frameworks, though teams still need to hand-tune schema design since there’s no automatic query optimizer the way a relational database has.
DynamoDB ships SDK support across every language AWS supports generally, from the JavaScript and Python SDKs to Java, Go, and .NET, and AWS provides DynamoDB Local, a downloadable emulator that lets developers run and test against a local instance without touching a real AWS account or incurring any charges. That local development story is one of the more underrated reasons teams already on AWS default to DynamoDB, since the entire develop-test-deploy loop stays inside tooling AWS already maintains.
Cosmos DB provides a comparable local emulator for Windows, along with SDKs for .NET, Java, Node.js, and Python, plus the added benefit that the same emulator can test against whichever API a team has chosen, whether that’s the native NoSQL API, the Cassandra API, or the MongoDB API. That multi-API emulator support is unique to Cosmos DB among the three databases here, and it’s a meaningful advantage for teams evaluating more than one query model before committing to a schema design.
Setting Up Each Database: A Quick-Start Code Comparison
The syntax differences are as telling as the pricing differences. Here’s how you create a basic table in each system.
Cassandra, using CQL, requires you to define a keyspace and replication strategy explicitly:
CREATE KEYSPACE app_data
WITH replication = {
'class': 'NetworkTopologyStrategy',
'us-east': 3,
'eu-west': 3
};
CREATE TABLE app_data.user_sessions (
user_id UUID,
session_id TIMEUUID,
payload TEXT,
PRIMARY KEY (user_id, session_id)
) WITH CLUSTERING ORDER BY (session_id DESC);DynamoDB, via the AWS CLI, only needs a partition key and sort key definition, with capacity mode chosen separately:
aws dynamodb create-table
--table-name UserSessions
--attribute-definitions
AttributeName=UserId,AttributeType=S
AttributeName=SessionId,AttributeType=S
--key-schema
AttributeName=UserId,KeyType=HASH
AttributeName=SessionId,KeyType=RANGE
--billing-mode PAY_PER_REQUESTCosmos DB, using the Azure CLI, requires a database and container, with the partition key and throughput mode set at creation time:
az cosmosdb sql container create
--account-name my-cosmos-account
--database-name AppData
--name UserSessions
--partition-key-path "/userId"
--throughput 1000None of these three commands are difficult on their own, but the replication strategy declaration in the Cassandra example is the clearest sign of the extra design decisions self-hosted deployments require up front, decisions DynamoDB and Cosmos DB both abstract away by default.
5 Real-World Workloads and Which Database Fits Best
The right database depends heavily on the shape of the workload, not just the cloud a team already uses. These five common patterns illustrate where each one tends to win.
- Gaming session state and leaderboards. Low-latency key lookups, unpredictable traffic spikes during launches or live events, and a need for near-zero operational overhead make DynamoDB’s on-demand mode a strong default, since capacity scales automatically without a game studio needing a dedicated database team.
- Global e-commerce shopping carts. Carts need to be readable and writable from whichever Region a shopper is closest to, with fast convergence and minimal data loss. Cosmos DB’s Session consistency level, paired with multi-region write accounts, fits this pattern closely, since it guarantees a shopper always sees their own most recent cart changes.
- IoT telemetry and time-series ingestion. High-volume, append-heavy writes from thousands or millions of devices favor Cassandra’s wide-column model, where clustering columns naturally support time-ordered data and the write path is optimized for sustained high throughput across a large cluster.
- Ad-tech real-time bidding. Sub-10-millisecond reads at massive concurrent scale, often across multiple clouds to reach different ad exchanges, make a case for Cassandra deployed through DataStax Astra DB, which keeps CQL compatibility while adding the managed operational layer bidding infrastructure needs.
- Social feed and messaging backends. Feeds need flexible document structures, secondary indexing for query patterns that evolve over time, and reasonable multi-region latency. Cosmos DB’s native NoSQL API, with its SQL-like query language over JSON documents, tends to fit this pattern better than DynamoDB’s stricter key-value model, particularly when query patterns aren’t fully known at launch.
Use-Case Recommendations: When to Choose Each Database
Beyond specific workloads, a handful of broader project constraints tend to settle the decision quickly.
- Choose DynamoDB if the application already lives entirely on AWS, the access patterns are simple key-value lookups, and the team wants to avoid managing any database infrastructure at all.
- Choose Cosmos DB if the application runs on Azure, needs fine-grained control over the consistency-versus-latency trade-off, or benefits from having multiple API surfaces (document, Cassandra-compatible, MongoDB-compatible) available under one billing account.
- Choose self-hosted Cassandra if the team already has the operational expertise to run distributed databases, needs true multi-cloud portability without vendor lock-in, or is operating at a scale where the per-request pricing of a managed service would cost more than dedicated infrastructure.
- Choose AWS Keyspaces or DataStax Astra DB if the team wants CQL compatibility and Cassandra’s data model without taking on cluster operations, effectively splitting the difference between self-hosted Cassandra and a fully proprietary managed database.
- Choose based on existing query patterns when a team already has significant code written against one of the three query languages; rewriting application-layer query logic is usually more expensive than the infrastructure cost difference between any of these databases.
Migrating Between Cassandra, DynamoDB, and Cosmos DB
Migrations between these three databases fall into two categories: moving to a managed Cassandra-compatible service without rewriting queries, or fully re-architecting to a different data model.
Moving self-hosted Cassandra to AWS Keyspaces or Cosmos DB’s Cassandra API is the lowest-friction path, since both services accept standard CQL. The main work is validating that consistency-level behavior, secondary index support, and lightweight transaction semantics match closely enough for the application to function correctly, since neither managed service implements the Cassandra protocol with 100% behavioral parity to open-source Cassandra.
Moving between DynamoDB and Cosmos DB, or from either into Cassandra, generally requires a full data model redesign, because the underlying access patterns differ so much. A DynamoDB table optimized around a single partition key and sort key doesn’t map cleanly onto Cassandra’s wide-column clustering model without rethinking primary key design, and Cosmos DB’s document-oriented containers often need denormalization work before they perform well under Cassandra’s query patterns. Teams running heterogeneous environments frequently lean on managed migration services like AWS DMS or Azure’s equivalent tooling to handle the initial bulk data transfer and ongoing change data capture during a cutover window, while application code is updated in parallel. Teams that also run analytical workloads alongside these operational databases often pair the migration with a look at warehouse options like Redshift, Snowflake, and BigQuery, since NoSQL operational data frequently gets streamed into a warehouse for reporting.
A practical migration checklist looks like this: audit every current access pattern and query shape first, map each one to the target database’s native query model, run a shadow write phase where both databases receive writes simultaneously, validate read consistency and latency under real production traffic, then cut reads over gradually by percentage rather than all at once. Teams that skip the shadow-write validation step are the ones most likely to discover a missing index or an unsupported query pattern in production rather than in testing.
Pros and Cons of Each Database
Apache Cassandra
- Pro: No licensing cost, full control over infrastructure, and true multi-cloud or on-premises portability.
- Pro: Tunable consistency gives the most granular control of the three databases over the latency-durability trade-off.
- Con: Highest operational overhead by far, requiring dedicated expertise in compaction tuning, repair scheduling, and capacity planning.
- Con: No built-in cost predictability the way a managed service’s pricing calculator provides.
Amazon DynamoDB
- Pro: Minimal operational overhead, automatic scaling, and deep integration with the rest of the AWS ecosystem.
- Pro: Straightforward, well-documented pricing with a generous, ongoing free tier.
- Con: AWS-only, creating meaningful lock-in for multi-cloud strategies.
- Con: Only two consistency options, which can be limiting for applications needing fine-grained staleness control.
Azure Cosmos DB
- Pro: Five consistency levels and multiple API surfaces give architects the most flexibility of the three options.
- Pro: Lifetime free tier with 1,000 RU/s and 25 GB of storage is generous for small production workloads.
- Con: Multi-region write pricing doubles the base rate, making global deployments noticeably more expensive.
- Con: RU-based billing is harder to estimate in advance than DynamoDB’s per-request model, which is why Microsoft built a dedicated cost estimator in 2026.
Security, Compliance, and Operational Overhead
All three databases support encryption at rest and in transit as a baseline expectation in 2026, but the operational burden of maintaining that posture differs sharply. DynamoDB and Cosmos DB both handle patching, encryption key rotation, and network isolation as part of the managed service, with access controlled through AWS IAM policies or Azure role-based access control respectively. Neither requires a customer to apply security patches to underlying database software, since there is no customer-managed software layer to patch.
Self-hosted Cassandra puts that burden entirely on the operating team. Every node needs OS-level patching, the Cassandra process itself needs version upgrades tracked against the project’s release cadence, and network-level access control has to be configured manually, typically through a combination of firewall rules and Cassandra’s own internal authentication and authorization system. Managed Cassandra-compatible services like AWS Keyspaces close most of that gap by applying AWS’s standard IAM-based access model on top of Cassandra’s data model, while DataStax Astra DB applies its own role-based access controls and audit logging as a managed layer.
For regulated industries, all three databases support the encryption and audit logging most compliance frameworks require, but Cosmos DB and DynamoDB both carry vendor compliance certifications inherited from AWS and Azure’s broader compliance programs, which can simplify audits compared to a self-hosted Cassandra cluster where the operating team has to independently document and maintain that compliance posture.
The Verdict: Which Database Wins in 2026
There isn’t a single winner here, and the pricing and architecture data above make that clear rather than obscuring it. DynamoDB wins on operational simplicity and cost predictability for teams already committed to AWS, with on-demand pricing at $0.625 per million write request units and $0.125 per million read request units in US East, plus a free tier generous enough to run small production workloads at zero cost indefinitely.
Cosmos DB wins for teams that need the flexibility of five consistency levels and multiple query API surfaces, particularly on Azure, but that flexibility comes at a real price: multi-region writes cost double the single-region rate, at $0.016 versus $0.008 per 100 RU/s-hour, and RU-based billing requires more upfront capacity planning than DynamoDB’s simpler request-based model, which is exactly why Microsoft shipped a dedicated cost estimator in June 2026.
Cassandra wins on cost at true scale and on portability, since the $0 license cost and multi-cloud flexibility become decisive once a workload is large enough that per-request billing from either managed service would cost more than dedicated infrastructure and a competent operations team. For most teams starting a new project in September 2026, the practical decision tree is simple: default to the managed service on whichever cloud you already run on, and only take on Cassandra’s operational cost when you have a specific, verified reason, such as multi-cloud requirements or a scale threshold where the math clearly favors self-hosting.
Frequently Asked Questions
Is DynamoDB built on Cassandra?
No. DynamoDB and Cassandra share some conceptual DNA, since both trace back to distributed systems research popularized in Amazon’s original Dynamo paper, but they are separate codebases with different query languages, consistency models, and operational models. DynamoDB is a proprietary AWS service with no relationship to the Apache Cassandra project’s source code.
Which is cheaper, DynamoDB or Cosmos DB?
It depends on the workload shape. DynamoDB’s on-demand pricing of $0.625 per million writes and $0.125 per million reads is easier to estimate upfront. Cosmos DB’s RU-based pricing can come out cheaper for read-heavy workloads but more expensive for write-heavy or multi-region workloads, where the RU cost per write tends to run higher and multi-region writes double the base rate.
Can Cassandra run natively on AWS or Azure?
Yes, in three ways: self-hosted on EC2 or Azure VMs with full manual control, through AWS Keyspaces as a Cassandra-compatible managed service, or through Azure Cosmos DB’s Cassandra API, which accepts standard CQL against Cosmos DB’s underlying engine.
What is the AWS equivalent of Cosmos DB?
The closest functional equivalent is DynamoDB for key-value and document workloads, though Cosmos DB’s multi-API flexibility doesn’t have a single direct AWS counterpart. Teams needing Cosmos DB’s Cassandra-compatible API specifically would compare it against AWS Keyspaces rather than DynamoDB.
Does DynamoDB support SQL queries?
DynamoDB supports PartiQL, a SQL-compatible query language, alongside its native API calls, but it is not a relational database and doesn’t support joins across tables the way a SQL database does.
How many consistency levels does Cosmos DB offer?
Five: Strong, Bounded Staleness, Session, Consistent Prefix, and Eventual, as documented in Microsoft’s official Cosmos DB consistency levels reference. Session is the default most applications use in production.
What is AWS Keyspaces?
AWS Keyspaces is Amazon’s fully managed, Cassandra-compatible database service. It accepts standard CQL, bills on read and write request units similar to DynamoDB’s on-demand model, and includes a three-month introductory free tier covering 30 million read and write request units plus 1 GB of storage.
Which database is best for multi-cloud deployments?
Self-hosted Apache Cassandra or DataStax Astra DB are the strongest fits for genuine multi-cloud portability, since both run identically regardless of underlying cloud provider. DynamoDB and Cosmos DB are each tied to their respective clouds, though Cosmos DB’s Cassandra and MongoDB API compatibility gives it more flexibility than DynamoDB if a future migration off Azure becomes necessary.