# Best Document Databases for Small Business - Page 2 *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)* Products classified in the overall Document Databases category are similar in many regards and help companies of all sizes solve their business problems. However, small business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Small Business Document Databases to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Small Business Document Databases category. In addition to qualifying for inclusion in the Document Databases category, to qualify for inclusion in the Small Business Document Databases category, a product must have at least 10 reviews left by a reviewer from a small business. ## Top Document Databases at a Glance | # | Product | Rating | Best For | What Users Say | |---|---------|--------|----------|----------------| | 1 | [MongoDB Atlas](https://www.g2.com/products/mongodb-atlas/reviews) | 4.5/5.0 (848 reviews) | Schema-flexible document storage with managed clustering | "[Flexible Document Model and Fast Development with MongoDB Atlas](https://www.g2.com/survey_responses/mongodb-atlas-review-12863051)" | | 2 | [Elasticsearch](https://www.g2.com/products/elastic-elasticsearch/reviews) | 4.5/5.0 (288 reviews) | Full-text search across centralized log documents | "[Simple UI, Seamless Integrations, and Strong Elasticsearch Performance](https://www.g2.com/survey_responses/elasticsearch-review-12835645)" | | 3 | [Amazon DynamoDB](https://www.g2.com/products/amazon-web-services-aws-amazon-dynamodb/reviews) | 4.4/5.0 (498 reviews) | Serverless document storage with AWS-native event-driven scaling | "[Highly Scalable, Low Latency Database with Seamless AWS Integration](https://www.g2.com/survey_responses/amazon-dynamodb-review-12782261)" | | 4 | [Amazon DocumentDB](https://www.g2.com/products/amazon-documentdb/reviews) | 4.3/5.0 (42 reviews) | MongoDB-compatible document storage on AWS | "[A highly scalable fully managed JSON document database that allows you to encrypt data](https://www.g2.com/survey_responses/amazon-documentdb-review-8010734)" | | 5 | [Google Cloud Firestore](https://www.g2.com/products/google-cloud-firestore/reviews) | 4.2/5.0 (96 reviews) | Real-time document sync with Firebase integration | "[Fast, Serverless Firestore with Great Real-Time Features](https://www.g2.com/survey_responses/google-cloud-firestore-review-12251251)" | | 6 | [InterSystems IRIS](https://www.g2.com/products/intersystems-iris/reviews) | 4.6/5.0 (59 reviews) | Multi-model document storage with translytical performance | "[Unified Programming and Complete Documentation Facilitate Use](https://www.g2.com/survey_responses/intersystems-iris-review-12189169)" | | 7 | [Couchbase](https://www.g2.com/products/couchbase/reviews) | 4.3/5.0 (143 reviews) | High-throughput JSON document storage with N1QL querying | "[Couchbase Delivers Low-Latency Database Access](https://www.g2.com/survey_responses/couchbase-review-13093148)" | | 8 | [Amazon WorkDocs](https://www.g2.com/products/amazon-workdocs/reviews) | 4.1/5.0 (35 reviews) | AWS-native secure document collaboration and sharing | "[I have been using this portal for more than one and half year and its quite impressive.....!!!!!](https://www.g2.com/survey_responses/amazon-workdocs-review-5430290)" | | 9 | [AceBase realtime database](https://www.g2.com/products/acebase-realtime-database/reviews) | 4.6/5.0 (17 reviews) | Self-hosted JSON sync with real-time querying | "[Flexible and Lightweight Realtime NoSQL DB for Modern Apps](https://www.g2.com/survey_responses/acebase-realtime-database-review-7546110)" | | 10 | [RavenDB](https://www.g2.com/products/ravendb/reviews) | 4.4/5.0 (13 reviews) | .NET-native document storage with built-in subscriptions | "[Feature-Rich Database That Simplifies Our Tech Stack](https://www.g2.com/survey_responses/ravendb-review-12049406)" | ## G2 Grid® for Document Databases ![G2 Grid® for Document Databases plotting products by satisfaction and market presence](https://www.g2.com/categories/document-databases/grids.png?focus%5B%5D=56279&focus%5B%5D=8313&focus%5B%5D=17357&focus%5B%5D=67028&focus%5B%5D=20172&focus%5B%5D=1263&focus%5B%5D=16866&focus%5B%5D=978) Highlighted products: MongoDB Atlas, Elasticsearch, Amazon DynamoDB, Google Cloud Firestore, Amazon WorkDocs, Couchbase, Arango, and Redis Software. Underlying data: [Grid® JSON](https://www.g2.com/categories/document-databases/grids.json?focus%5B%5D=mongodb-atlas&focus%5B%5D=elastic-elasticsearch&focus%5B%5D=amazon-web-services-aws-amazon-dynamodb&focus%5B%5D=google-cloud-firestore&focus%5B%5D=amazon-workdocs&focus%5B%5D=couchbase&focus%5B%5D=arango&focus%5B%5D=redis-software&segment=small-business) ## How Many Document Databases Products Does G2 Track? **Total Products under this Category:** 65 ### Category Stats (Jul 2026) - **Average Rating**: 4.23/5 (↓0.01 vs Jun 2026) The average rating of products in this category, based on all submitted ratings *Last updated: July 14, 2026* ## How Does G2 Rank Document Databases Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 3,200+ Authentic Reviews - 65+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. --- **Sponsored** ### Couchbase Couchbase’s operational data platform for AI is a scalable foundation for enterprise operational, analytical, mobile and AI workloads that replaces legacy infrastructure and data services. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=306&secure%5Bchosen_at%5D=2026-07-19T02%3A18%3A15Z&secure%5Bdisplayable_resource_id%5D=306&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=306&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1263&secure%5Bresource_id%5D=306&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdocument-databases%2Fsmall-business%3Fpage%3D2%26segment%3Dsmall-business&secure%5Btoken%5D=2fe7b48bb28f4139c5cb0dbdc9bd417e6411002ca59b8365469de0c1f1019c02&secure%5Burl%5D=https%3A%2F%2Fwww.couchbase.com%2F%3Futm_source%3Dg2%26utm_medium%3Ddisplay&secure%5Burl_type%5D=custom_url) --- ## What Is Document Databases? [NoSQL Databases](https://www.g2.com/categories/nosql-databases) ## What Software Categories Are Similar to Document Databases? - [Graph Databases](https://www.g2.com/categories/graph-databases) - [Key Value Databases](https://www.g2.com/categories/key-value-databases) - [Database as a Service (DBaaS) Providers](https://www.g2.com/categories/database-as-a-service-dbaas) --- ## How Do You Choose the Right Document Databases? ### What You Should Know About Document Databases Software ### What are Document Databases Software? Document databases are a class of non-relational databases (NoSQL databases). Document databases store related data in a document format. They are used to design, query, and store the data in a document format (JSON document, XML, YAML, or binary formats such as BSON and PDF). The software is used for storing, retrieving, and managing document-oriented information also known as semi-structured data. Document databases software, also known as document-oriented databases software, is a subclass of key-value stores, which is a NoSQL database concept. In a key-value store or key-value database, data is managed (stored, received) by using associative arrays. This type of data structure is called a “dictionary”. Dictionaries are a collection of objects, and objects are the central data storage repository that store different fields that contain the data. Some of the key examples include MongoDB, Amazon DynamoDB, Google Cloud Firestore, Couchbase Server, Apache CouchDB, among several others. Many of these databases such as MongoDB and Couchbase server are open source in nature. To call the data when required, a key is used, which acts as the unique identifier for the record within the entire database. When talking about document databases, it’s important to identify what exactly is a “document”. A document stores or encodes all the data in a standard format. These formats include JSON, XML, YAML, and others.  Document databases differ greatly from traditional relational SQL databases. The major cause of difference between the two types of databases is that relational databases store data models as a relation—tables, rows, and an object could be a part of numerous tables. However, document databases store all the related information of an object within a single instance of the database, and each object can be stored uniquely. Document databases do not have any restrictions as relational databases do. **CRUD operation** The core operations for document databases are abbreviated as CRUD—create, retrieve, update, and delete. These are the four basic operations that all document databases support. **What is a key?** As stated earlier, a key acts as a unique identifier that is representative of the document. It is used to retrieve the data from the document database. There is usually an index of keys available, which makes it easier for the user to refer to and call back the data represented by that particular key. In case a user needs to add or delete a document within the document database, a key can be used for the same. **Data retrieval ** Although a key-to-document method is enough for data retrieval, the document database offers an API that users can use to query data based on content. The set of query language or query APIs vary significantly between different data model implementations. In this, document databases make use of the metadata of the content to classify the content and differentiate it from one another. **Data organization** There are several ways to arrange documents within document databases software. A document can exist as single or multiple collections. **Hierarchy:** Documents are grouped in a tree-like structure and have a typical path. **Collections:** Group of documents within the software.  **Data tags:** Documents or additional data located outside the content. **Why use document databases?** Since the data is stored in a format that is very close to the application development code used by developers, there is much less translation required for the data to be used by an application. These types of databases give developers the freedom and the flexibility to rework various documents in the format suited for that application. In turn, their application needs to change over time, the document database can also be modeled in the same data format as required by the application. **When can a user opt for document databases?** Document databases software is used to store large volumes of data in a key-value, making it easy for the user to access the data. Considering the significant amount of data to be processed, some of the key uses of the software include content management, user profiles for a company, catalogs, and several other documents. ### What are the Common Features of Document Databases Software? The need for document databases has become imminent with the rise of unstructured data. The following section covers the core features of document databases software that can help users in several ways: **Document databases software are NoSQL:** NoSQL database software was created to meet the needs of the internet era, with the rise of unstructured data. NoSQL document databases have been attributed with increasing the pace of app development and supporting data scaling and new application structures and paradigms. Since document databases are NoSQL in nature, several elements can be indexed and called faster by application developers. The data structure in this software is designed for unstructured data or big data, allowing it to plow through large amounts of data while being able to maintain its efficiency and flexibility.  **Schema support:** Document databases software can support several different schemas of data because there are no restrictions in the structure of the data. The schema is flexible and can be used for different types of document formats to process queries faster. **Richness of indexing:** Several document databases support ad hoc queries, indexing, full-text search, and real-time data collections to ensure that users can access, analyze, and transform data as required.  **Distributed database:** Document databases software are distributed as their central principle, unlike monolithic relational databases. Since the documents are individual and independent, they can be located or distributed on multiple servers across the globe. This is very useful for companies such as e-commerce that have locations across the globe. It also supports replication and self-healing capabilities to ensure that all applications support high availability. The software also supports data sharding (a data partitioning technique) to ensure scalability across numerous independent servers. ### What are the Benefits of Document Databases? The inclusion of document databases software within a firm can help manage thousands of documents that exist within a company. Some of the key benefits of document database software include: **Easy availability:** The data is not spread out or linked over different databases but rather is available in a single database. This is one of the main benefits of document databases. Although interlinking of documents is possible, it is not usually recommended since it would make the database relational in nature and also add to the complexity of managing the database.  **No foreign keys:** Having no foreign keys indicates that there is no relationship formed between the data. Without the existence of this dynamic, documents can be created, managed, and deleted independently making it much faster to process data for several applications querying it. **Open formats:** One of the key benefits of using document databases is that they support several open formats. The process can use XML, JSON, and several other formats for the data. **Supports scalability:** As the amount of data generated increases every minute, the database software being used by customers also needs to ensure flexibility and scalability. Document databases allow users to easily add datasets to scale up, which means more future-proof features. **New integration support:** Since document databases are much more flexible and scalable compared to traditional relational databases, integrating new data into the database software is easy. There is no need for consistency in data formats, and large amounts of unstructured data or big data can be stored. **Fast query nature:** One of the key features of document databases software is its nature to improve the speed of queries. Using document databases can enable several app developers to store and query requested data in the same document-model format that is being used in the code being developed. For example, in the healthcare field where time is of the essence, a user can immediately get access to health records instead of facing any delays or issues.  ### Who Uses Document Databases Software? Some of the main users of document databases software have been listed below: **Database administrator (DBA):** Key persona handling the software. The schema is determined by the DBA. They are also responsible for setting up different user IDs and rights for those who can access the database. This persona also monitors the database, ensures security is maintained, ensures backup and recovery plans are active, tracks errors or failures, provides database support, and several other requirements. **Software developers:** Programmers and software developers would need access to data when developing a software application or making changes to one. This persona will have access to the document database to ensure that the software application development process goes smoothly. In addition, document databases have a long list of supported programming languages which includes Perl, Java, C, C++, Python, and Javascript. **Managers:** Managers can use the database temporarily or whenever they require new information. This persona doesn't use it daily as the other personas, only when the requirement arises.  **Other users:** This includes users such as analysts and scientists who do not write a code, but use the document databases software to query some information as and when required. They have interactions with the database as per their data requirements. #### Software Related to Document Databases Software Related solutions that can be used together with document databases software include other key NoSQL document databases as follows: [XML databases software](https://www.g2.com/categories/xml-databases) **:** XML database software are a subclass of document databases, wherein the database primarily works with XML documents. [Graph databases](https://www.g2.com/categories/graph-databases) **:** Graph databases use graphs and graph structures for database queries. The graph is used to connect the data stores to nodes and edges, where edges form the relationship between nodes. [Columnar databases software](https://www.g2.com/categories/columnar-databases) **:** Under this type of database software, a column store is used to store data. Data can be read quickly when it's in a columnar format. Since the data in the column is of a uniform type, it provides for storage opportunities and storage optimizations within the database. ### Challenges with Document Databases Software Document databases solutions can come with their own set of challenges.  **Consistency issues:** A major challenge that comes with document databases is data consistency and limitations to the checking process. Since the data is not related to other data points as in relational database service there are chances of duplicated data, redundant data, unrelated data being collected together, among several other possibilities. This could hamper the performance of the database. **Security challenges:** Since document databases are primarily focused on the numerous unstructured data stores available from several sources which include web applications, it leads to several points to be exposed where data hackers can get through and breach system security. This could lead to data leaks and unintended personnel getting their hands on critical data. **Issue with atomicity:** In [database management systems (DBMS) software](https://www.g2.com/categories/database-management-systems-dbms), atomicity is one of the ACID transactions. Atomicity is the guarantee that each transaction of data is treated as a single unit that either completely succeeds or fails; there is no in-between. A single command is given to make changes to the data, and all subsequent queries will also reflect these changes. However, in document databases, a change that affects two data collections will need to be run twice which does not follow the principle of atomicity. **Data loss issues:** A key challenge with document databases is data loss. Data loss issues could arise due to wrong configurations since a single node is not being used. ### How to Buy Document Databases Software #### Requirements Gathering (RFI/RFP) for Document Databases Software When choosing a document databases software, some important criteria need to be considered. Factors such as flexibility, usability, functionality, security are key criteria that cannot be compromised. Having features such as dashboards and visualizations is a great benefit to ensure ease of analyzing the data storage and keeping track of several queries. Other important features to look out for are support and development—the hours customer support is available, if they are open to solving queries, and continuous information on updates on the latest new additions and developments in the document databases software, among several other features. As a business grows, scalability is an important criterion to keep in mind. With tons of unstructured data or big data being generated, the document databases software should be able to manage millions of columns of data. Another key feature to ensure that the document databases software has is integration support. Application developers with several different software and this software should be able to easily call data from the document database as required. How these integrations are managed and how the company ensures all these software connect with the document databases software is critical for the smooth flow of data. Checking on what programming languages are supported by the document database is a good factor to look into. #### Compare Document Databases Software Products **Create a long list** In this step, buyers should keep their options open to consider the full range of products. Buyers have the freedom to explore numerous offerings that this software market has. The long list can be made much more concise and smaller by addressing the goals. **Create a short list** Buyers can make much more granular comparisons on this step. In addition to this, buyers can use the G2 reviews to further narrow this list down. **Conduct demos** Once the list has been reduced to a couple of vendors, buyers may begin to request a demo. During the demo, buyers should seek out information that is related to their non-negotiable terms. This is a good stage where the buyer can delve more deeply into understanding how secure their document database will be, high-performance support availability, what the features are—latency in loading document databases, after-service support, staff training, and other additional features that can be provided when opting for their document databases solution.  #### Selection of Document Databases Software **Choose a selection team** Choosing the right team to work together to decide the right document databases software is a critical part of the process since several personas would need to access the database applications as per requirements. The team should include a mix of different personas who have the required skills, the interest, and the time. Some roles include database admins, application developers, key management leaders, IT heads, and others. **Negotiation** A buyer can choose to negotiate to trim costs. The buyer needs to note that if in the future there is a requirement for scaling, there would be additional costs or an increase to the subscription pricing. It is a good practice to check with the document database vendor if they offer any cloud support, training, and other factors. Keeping such factors in mind will help the buyer to put forward better negotiation tactics for the specific functions that matter. **Final decision** Once all the steps are complete, the final decision is made weighing all factors and scenarios. Having a trial run of the software is a good place to start by using smaller document databases. A small group of database admins can pass on their views to the team making the final decision. --- ## What Are the Most Common Questions About Document Databases? *AI-generated · Last updated: June 3, 2026* ### Which Document Databases platforms support ACID transactions and data consistency guarantees at scale Based on G2 reviews, these products are most often mentioned for consistency, transactions, or reliable scaling needs. - [MongoDB](https://www.g2.com/products/mongodb) — flexible schema with replication and sharding. - [Amazon DynamoDB](https://www.g2.com/products/amazon-web-services-aws-amazon-dynamodb) — transactional workloads with managed high availability. - [Elasticsearch](https://www.g2.com/products/elastic-elasticsearch) — resilient distributed indexing and scalable search. - [MongoDB Atlas](https://www.g2.com/products/mongodb-atlas) — managed clusters with backups and autoscaling. ### Which Document Databases platforms offer the fastest query performance and automatic sharding capabilities Based on G2 reviews, these products stand out for fast retrieval, scaling behavior, and distributed data handling. - [MongoDB](https://www.g2.com/products/mongodb) — fast reads with built-in sharding. - [Amazon DynamoDB](https://www.g2.com/products/amazon-web-services-aws-amazon-dynamodb) — low-latency access with automatic scaling. - [Elasticsearch](https://www.g2.com/products/elastic-elasticsearch) — near real-time search across large datasets. - [MongoDB Atlas](https://www.g2.com/products/mongodb-atlas) — indexed queries and easy shard management. ### Finding Document Databases platforms that handle unstructured JSON data and eliminate the need for complex schema redesigns According to verified users, this is one of the clearest strengths of document databases. Reviewers repeatedly describe storing JSON-like or semi-structured data without rigid tables, which helps teams move faster when requirements change. Across the recent reviews, buyers highlight easier iteration, fewer schema migrations, simpler API alignment, and less time spent reshaping application data. They also mention tradeoffs: governance can become harder without strong validation, joins may feel less natural than in relational systems, and indexing still matters for performance. For teams working with evolving application data, logs, profiles, events, or mixed document structures, flexibility is the most consistent benefit described. ### What are document databases Document databases are systems built to store data as flexible documents rather than fixed tables. In the recent G2 reviews for this category, users most often describe working with JSON-like or BSON-style records that can evolve without constant schema redesign. That makes them useful when application data changes frequently, when teams want faster development cycles, or when they need to store semi-structured and unstructured information more naturally. Reviewers also connect document databases with horizontal scaling, replication, indexing, aggregation, and easier alignment between application objects and stored data. At the same time, users note they can require careful indexing, data governance, and planning for relationships or strict consistency needs. ### How do teams use Document Databases for real-time analytics G2 reviewers mention using document databases for real-time analytics by storing operational data in flexible document formats and then querying it quickly with indexes, aggregations, or search features. Recent reviews describe teams analyzing logs, event streams, dashboards, API data, customer activity, and application records without repeatedly redesigning schemas as inputs change. Buyers also highlight faster iteration because data can stay close to the structures used in code, which reduces transformation work before analysis. Across the review set, real-time analytics use cases are usually tied to quick retrieval, scalable clustering or sharding, and support for evolving datasets. The main caution reviewers raise is that sustained performance depends on thoughtful indexing and query design.