MongoDB Alternatives: 7 Options and Their Limits

Key takeaways

No single alternative fits every app. The right one depends on how much of your app you're willing to change. Want to keep your MongoDB code?

Look at Amazon DocumentDB, Azure DocumentDB or FerretDB, and read each one's list of missing features first. Want JSON documents with a new query language? That's Couchbase.

Ready for a new data model? PostgreSQL with JSONB (JSON stored in a binary column type) suits joins and reports.

Amazon DynamoDB suits known key lookups on AWS. Apache Cassandra suits write-heavy work you run yourself.

• "MongoDB-compatible" does not mean MongoDB. Amazon DocumentDB emulates MongoDB APIs on its own engine. Azure DocumentDB's 99.03% figure counts operators only, and FerretDB lists commands it hasn't built yet.
• Every option has a named limit. DynamoDB items stop at 400 KB. Couchbase Community Edition stops at five nodes. FerretDB has no transaction commit command yet.

We checked prices in October 2026 against each vendor's own page. Plans change often, so confirm on the vendor's page before you decide. Last reviewed October 2026.

Why teams look for a MongoDB alternative

MongoDB's release notes page lists the 9.0 series as the current stable release. Three reasons usually start the search.

• The licence. The MongoDB server's licence file is the Server Side Public License (SSPL), Version 1, dated October 16, 2018, with copyright held by MongoDB, Inc. You can read it yourself in the LICENSE-Community.txt file in MongoDB's GitHub repository. Microsoft's Azure DocumentDB page says the SSPL doesn't apply to DocumentDB or Azure DocumentDB, because they don't run the MongoDB server.
• The bill. MongoDB's managed service, Atlas, lists a Free tier at $0, a Flex tier that "can cost between $8 and $30 for a month's usage", and Dedicated clusters from $0.08 an hour, which is $56.94 a month. See the Atlas pricing page, read in October 2026.
• Joins. MongoDB can join collections with $lookup, a left outer join to another collection in the same database. Its own $lookup docs page warns that unindexed or correlated-subquery lookups on large collections can slow queries. They suggest an embedded data model instead.

MongoDB Atlas pricing page, October 2026.

How we picked

Vendor facts come from each vendor's own docs and pricing pages, read in October 2026. We did not run the other tools. User opinions come only from named public sources, with a month and year where the page shows one. Treat them as opinion.

CouchDB, Redis, Firestore, Elasticsearch, RethinkDB, OrientDB and others are left out. We didn't check their current docs for this page.

First, pick a route

Route 1 keeps your MongoDB code. An API (application programming interface) is the set of commands your app sends. These engines accept MongoDB's commands but aren't MongoDB: Amazon DocumentDB, Azure DocumentDB and FerretDB. You'll still need to test for missing features.

Route 2 keeps the documents. Couchbase also stores JSON documents, but you use its own driver and query language, SQL++. Your data shape carries over, and you rewrite the data access code.

Route 3 changes the data model. PostgreSQL stores rows and tables, DynamoDB stores items found by key, and Cassandra stores rows grouped by a partition key. This is the most work. It's also the route where joins, for example in PostgreSQL, become possible.

MongoDB alternatives at a glance

Option	Route	Licence	Who runs it	Starting price (vendor page)	Biggest named limit
MongoDB (baseline)	n/a	SSPL v1 (server)	You, or Atlas	Atlas Free $0; Flex $8 to $30 a month; Dedicated from $0.08 an hour	16 MiB per document
PostgreSQL with JSONB	3	PostgreSQL License	You, or a host	Software free; host prices vary	Any update locks the whole row
Amazon DocumentDB	1	Proprietary	AWS	db.r5.large $0.277 an hour, plus storage and I/O	Emulates APIs; no wildcard or hashed indexes
Azure DocumentDB	1	MIT (engine); Azure service proprietary	Microsoft, or you	Free tier; compute rates not published on the page we read	Some commands unsupported, such as bulkWrite
FerretDB	1	Apache 2.0	You	Software free; hosted price not published	Transaction commit and abort not implemented
Couchbase	2	See note below	You, or Capella	Capella Basic from $0.15 an hour per node	Community Edition: 5 nodes, 4 cores per node
Amazon DynamoDB	3	Proprietary	AWS	$0.625 per million write request units	400 KB per item; no joins
Apache Cassandra	3	Apache 2.0	You	Software free; project publishes no price	No joins in its query language

Sources: MongoDB pricing, Amazon DocumentDB pricing, Couchbase pricing, Amazon DynamoDB on-demand pricing, and each project's own docs. Checked October 2026.

Couchbase's documentation calls Couchbase Server "an open source" database, but we couldn't confirm the licence terms from a primary page, so we don't name one. Prices are US East (N. Virginia) where a region applies.

The 7 MongoDB alternatives

1. PostgreSQL with JSONB

PostgreSQL home page, October 2026.

At a glance: PostgreSQL License · you run it or rent it from a host · route 3 · software is free

JSONB is a PostgreSQL column type that stores JSON documents in a binary format. Its documentation says most applications should prefer jsonb, and that it supports several index types.

What its docs list

• jsonb is stored in a decomposed binary format, which the docs say makes input slightly slower but processing significantly faster than plain json. It also supports indexing, and GIN indexes can search for keys or key/value pairs across a large number of jsonb documents.
• It speaks SQL, so joins, reports and the tools your analysts already know work as they do on any relational database. Its docs say it supports most of the major features of SQL:2023 and conforms to at least 170 of the 177 mandatory Core features, per its SQL conformance appendix.
• Under its multiversion concurrency control (MVCC) model, the concurrency docs say reading never blocks writing and writing never blocks reading.
• Its change feed, called logical replication, can publish inserts, updates, deletes and truncates, according to its publication docs, so downstream systems can follow it.

Where it falls short

• Any update takes a row-level lock on the whole row, so the docs say to keep JSON documents a manageable size. Large documents that change a lot are a poor fit.
• jsonb does not keep white space, the order of object keys or duplicate keys, and if a key appears twice only the last value is kept. The docs also recommend a somewhat fixed document structure, which is typically not enforced by the database.
• The built-in partitioning described in its docs splits a table by range, list or hash, and the page doesn't mention sharding across servers. Ask any host how it scales writes.
• Your MongoDB queries become SQL, so your data access code has to change.
• A logical replication slot that nobody reads keeps write-ahead log (WAL) files on disk, and in extreme cases could shut the database down to prevent transaction ID wraparound, according to its logical decoding docs.

Fits: reports and joins matter more than a drop-in swap. Less suited if your existing MongoDB code has to keep working.

What users say: On the Hacker News thread "Go ahead, self-host Postgres" (December 2025), one commenter wrote "No issues selfhosting either. I have backups and I know they work." Another said "And Postgres upgrades are not transparent."

2. Amazon DocumentDB

Amazon DocumentDB home page, October 2026.

At a glance: proprietary · AWS managed service · route 1 · db.r5.large at $0.277 an hour standard

Amazon DocumentDB "emulates the MongoDB 3.6, 4.0, 5.0, and 8.0 APIs on a purpose-built database engine", according to AWS's functional differences page. It isn't MongoDB code. AWS says query plans and explain() output may differ.

What its docs list

• AWS describes it as fully managed. Its developer guide says storage keeps six copies of your data across three Availability Zones (separate data centers in one region), and grows in 10 GB steps up to 256 TiB for engine version 8.0 and later (128 TiB for earlier versions). You don't provision extra storage for growth.
• A cluster holds 0 to 16 instances and all writes go through the primary. AWS says that on an instance failure it fails over automatically to one of up to 15 replicas that you create in other Availability Zones.
• Point-in-time recovery restores a cluster to any second in the retention period, up to the last 5 minutes, and automatic backup retention can be set up to 35 days.
• Elastic clusters, the horizontally scaled type, are described as supporting workloads with millions of reads and writes per second and petabytes of storage.
• Change streams are supported on instance-based clusters for all four API versions, per the supported-commands table in AWS's MongoDB APIs page. Explicit multi-statement ACID transactions are supported from DocumentDB 4.0, according to the functional differences page.
• AWS offers a one-month free trial of 750 hours on db.t3.medium, with 30 million I/Os and 5 GB of storage. It is not available in the AWS GovCloud (US) regions or the China (Ningxia) region.

Where it falls short

• AWS's supported-index table marks wildcard and hashed indexes as not supported, and capped collections, cloneCollectionAsCapped and convertToCapped are marked No for every engine version.
• Change streams are not supported on elastic clusters, the horizontally scaled type.
• Retryable writes arrive in engine version 8.0.2. Earlier versions return an error unless the driver turns them off.
• Only one index build can run on a collection at a time.
• It does not support the admin or local database, or MongoDB system.* collections, and it does not guarantee result ordering unless you sort explicitly.
• Do the sums: one db.r5.large instance running 30 days is 720 hours, or about $199 at $0.277 an hour (our own sum, not a price on the page: 0.277 x 720 = 199.44), before storage and I/O. Billing has more moving parts than a flat tier: instance hours, I/O, storage, backup and data transfer. The pricing page example, read in October 2026, lists $0.10 per GB-month and $0.20 per million I/O requests on standard, or an I/O-Optimized option at $0.3047 an hour and $0.30 per GB-month.

Amazon DocumentDB developer guide, MongoDB APIs page, October 2026.

Fits: an app already on AWS that sticks to the supported features. Test the exact features you use; AWS publishes the list of what is and isn't supported.

3. Azure DocumentDB

Azure DocumentDB product page, October 2026.

At a glance: open source engine, proprietary Azure service · route 1 · free tier, other rates not published on the page we read

Azure DocumentDB is described by Microsoft as "a fully managed, open-source, 99.03% MongoDB-compatible database". It is built on the open source DocumentDB engine, which is governed by the Linux Foundation and, according to the DocumentDB project's GitHub page, built on PostgreSQL. The engine uses the Massachusetts Institute of Technology (MIT) licence, a permissive open source licence, and the service does not run the MongoDB server.

The overview page was last updated on June 12, 2026.

What its docs list

• Microsoft says the open source engine can run on-premises or in any cloud, and that a Kubernetes operator is provided to get started.
• Microsoft says the service supports high-capacity vertical and horizontal scaling, with no shard key required until the database surpasses terabytes. That is Microsoft's description, and we didn't test it.
• Its compatibility page lists all 181 aggregation operators and all 22 update operators as supported.
• The same page lists wildcard, hashed, text, multikey, compound, geospatial and vector indexes. The vector index is marked as available only in DocumentDB.
• The $changeStream stage is marked supported for versions 5.0 to 8.0.
• Microsoft's pricing page lists a free tier cluster, and a savings plan at 20% off per node on one and three year terms.

Where it falls short

• The 99.03% is Microsoft's own measure. It counts operators: 58 of 60 aggregation stages, 181 of 181 aggregation operators, 44 of 45 query and projection operators, and 22 of 22 update operators. Commands and admin operations are excluded, per Microsoft's compatibility page. That does not mean 99% of your app will run.
• Commands marked not supported include bulkWrite, serverStatus, top, dataSize, compact and setDefaultRWConcern.
• Creating a unique index takes an exclusive lock on the collection for the whole build, which blocks reads and writes.
• $changeStreamSplitLargeEvent is marked not supported, even though $changeStream itself is.
• Compute and storage rates are not published on the pricing page text we read, so you can't price a cluster from that page alone.
• The savings plan needs a one or three year term, so the 20% discount requires that commitment.

Fits: Azure, or an engine whose licence you can read in full. Less suited if your app depends on the unsupported commands.

4. FerretDB

FerretDB documentation home page, October 2026.

At a glance: Apache 2.0 · you run it · route 1 · software free, hosted price not published

FerretDB's documentation home page describes it as an open-source alternative to MongoDB. In plain terms, it's a proxy that converts MongoDB 5.0+ wire protocol queries to SQL, and it uses PostgreSQL with the DocumentDB extension as its database engine. The wire protocol is how a driver talks to the server.

What its docs list

• The licence is Apache 2.0, shown on its GitHub page.
• The project README (its front page on GitHub) says it is compatible with MongoDB drivers and popular MongoDB tools, and works as a drop-in replacement for MongoDB 5.0+ in many cases. The "in many cases" is the project's own wording.
• Your data sits in PostgreSQL, which many teams already run.
• The README lists managed FerretDB at FerretDB Cloud, Civo, Tembo, Elestio and Cozystack, and a Go library package that embeds FerretDB into your application.
• The project states its own policy on gaps in its compatibility docs: "We consider all other differences in behavior to be problems" and want to address them. That is the project's stated intent. It also says it uses the same error names and codes as MongoDB.

Where it falls short

• On the v2.7 compatibility page, abortTransaction and commitTransaction are "Not implemented yet". The newer v2.8 docs are labelled unreleased and show the same two gaps.
• The role management commands, such as createRole, dropRole and rolesInfo, are not implemented.
• bulkWrite, cloneCollectionAsCapped and convertToCapped are not supported.
• Change streams are not documented as supported on the page we read.
• Exact error messages may sometimes differ from MongoDB's, and collection names must be valid UTF-8, which MongoDB does not require.
• The README says the quickstart Docker command is unsuitable for production use because it keeps all data inside and loses it on shutdown. It also advises users not to build FerretDB themselves, and to use the binaries, Docker images or packages it provides.

FerretDB v2.7 compatibility page, October 2026.

Fits: self-hosted and permissively licensed, if you can live without those commands. Less suited if you need multi-statement transactions or change streams today.

5. Couchbase

At a glance: editions: Community and Enterprise · you run it, or use Capella · route 2 · Capella Basic from $0.15 an hour per node

Couchbase Server stores items with a key and a value, inside buckets, scopes and collections. A value can be binary or a JSON document, and its query language is SQL++, which looks like SQL but works on JSON. The data docs say the maximum size of a value is 20 MiB.

What its docs list

• Data is saved as items, each with a unique key and a value that can be binary or JSON. The data docs put the maximum size of a value at 20 MiB.
• A bucket can have up to 3 replicas, and XDCR (cross data center replication) copies data between clusters that may sit in different data centers, per its replication docs.
• Capella, the managed service, lists a Free plan with 1 node and 8 GB, per its pricing page. Basic starts at $0.15 an hour per node, Developer Pro at $0.35 and Enterprise at $0.49.
• The transactions docs describe atomicity, consistency, isolation and durability (ACID) guarantees, meaning all-or-nothing changes. Changes are not visible until commit, which the docs call Read Committed isolation, and transactional reads are stricter (Monotonic Atomic View).
• Transactions offer three durability levels, from majority (the default) to persistToMajority, which the docs call the strongest protection and the least performant.
• Couchbase's docs describe a Kafka connector that streams changes using its Database Change Protocol (DCP). We didn't test it, so check it yourself if you feed analytics.

Where it falls short

• Community Edition is free for clusters of up to five nodes and supports a maximum of 4 cores per node, with limited concurrency and parallelism, per the editions page.
• Its licence was updated to disallow XDCR, now an Enterprise-only feature, and Community Edition does not include the latest bug fixes.
• The docs say to use transactions only on documents under 10 MB, inside one data center, and not with active-active two-way XDCR.
• The default transaction expiry is 15 seconds, and transactions need Network Time Protocol (NTP) to keep time in sync across all cluster nodes.
• The Capella Free plan lists forum support only.
• You'll change drivers and rewrite queries.

Fits: the document model with a different query language. Less suited if you need to keep MongoDB commands.

6. Amazon DynamoDB

Amazon DynamoDB home page, October 2026.

At a glance: proprietary · AWS service · route 3 · $0.625 per million write request units

AWS's DynamoDB introduction page describes it as a serverless, fully managed, distributed NoSQL database with single-digit millisecond performance at any scale. That is AWS's own claim, and we didn't test it.

What its docs list

• On-demand mode is pay per request. The worked examples on the on-demand pricing page, read in October 2026, use $0.625 per million write request units and $0.125 per million read request units, and standard storage at $0.25 per GB.
• A write request unit covers an item up to 1 KB, and a read request unit covers 4 KB. An eventually consistent read costs half a read request unit per 4 KB.
• The free tier lists 25 GB of storage, 25 write and 25 read capacity units, 2.5 million stream read requests and 1 GB of data transfer out.
• Transactions can group up to 100 write actions on up to 100 items, with a 4 MB total. The transactions docs say each item costs two underlying reads or writes, one to prepare and one to commit.
• Global tables replicate item changes to other Regions typically within a second by default, and multi-Region strong consistency is available as an option, per the read consistency docs.
• Strongly consistent reads are available on tables and local secondary indexes, so you can choose per read between cost and freshness.

Where it falls short

• The maximum item size is 400 KB, which includes attribute names, per the quotas docs. AWS's best-practices page suggests storing a large item as an object in Amazon S3 and keeping the object identifier in the DynamoDB item.
• There is no JOIN operator, according to the AWS introduction page. AWS recommends denormalizing, which means copying data into the shape each query needs.
• A Scan reads the whole table or index before filtering and returns up to 1 MB per call.
• Reads are eventually consistent by default, and reads from global secondary indexes and streams always are. Strongly consistent reads work only on tables and local secondary indexes.
• Change history in DynamoDB Streams is removed after 24 hours, according to AWS's Streams docs.
• It's an AWS service. AWS also offers a downloadable DynamoDB Local for developing and testing applications, and transactions give ACID guarantees only in the Region where the write API was called.

Amazon DynamoDB developer guide, quotas page, October 2026.

Fits: known questions on AWS. Less suited if your documents are large or your questions keep changing.

What users say: On PeerSpot, an August 2024 reviewer (older) wrote "We previously paid around $20,000 a month for MongoDB, and now we're paying just $4,000 monthly for Amazon DynamoDB." A May 2026 reviewer wrote "we lack joins in the table".

7. Apache Cassandra

Apache Cassandra home page, October 2026.

At a glance: Apache 2.0 · you run it · route 3 · software free, project publishes no price

Cassandra is an open source distributed database. It keeps several copies of each partition, and any copy can take a write (multi-master replication).

You pick a consistency level per operation, such as one replica or a majority (called quorum). The latest stable version is 5.0, with 5.0.9 released on August 7, 2026.

What its docs list

• The storage engine docs say it is optimized for write-oriented workloads, using log structured merge (LSM) trees with an append-only approach, so the write path has no read lookups. We didn't benchmark it.
• Its architecture docs describe multi-master replication, so there is no single primary to fail over. With a replication factor of 3, data is written to three distinct replicas.
• Consistency levels include ONE, TWO, THREE, QUORUM (a majority of replicas) and ALL, so you tune the trade-off per operation.
• Version 5.0 adds Storage Attached Indexes, a vector data type, trie memtables and trie SSTables, the Unified Compaction Strategy and Dynamic Data Masking.
• CQL INSERT and UPDATE accept a time to live (TTL) in seconds, and the values are removed automatically after that time. Updates within one partition key are applied atomically and in isolation, per the CQL docs.
• The project publishes no price list, and the software itself is free.

Where it falls short

• Its query language, Cassandra Query Language (CQL), runs no joins or sub-queries, and a SELECT reads one table. The data modeling guide says to group all required columns in one table, which duplicates data.
• The guarantees page says Cassandra picks availability and partition tolerance and compromises on consistency to some extent. Single-table writes are eventually consistent, and its architecture docs say concurrent updates are settled by last-write-wins.
• Conditional writes such as IF NOT EXISTS use a protocol called Paxos, which needs the replicas to agree. The CQL docs say this carries a non-negligible performance cost and should be used sparingly.
• Its guideline is under 100,000 values and 100 MB of disk per partition, though the docs say requirements vary by application.
• The storage engine docs list trade-offs in read performance and write amplification for the append-only design.
• A CQL query that needs a full scan of all partitions has to add ALLOW FILTERING, which the docs say runs the full scan and makes performance unpredictable.

Fits: write volume as the problem and people to run clusters. Less suited if you need ad hoc queries or joins.

What users say: On a Hacker News thread about ACID (all-or-nothing) transactions in Cassandra 6 (August 2026), one commenter said Cassandra at scale has "better cost and availability" than anything else they had tried, "as long as you have a competent team". Another wrote "It's an operational nightmare".

Other options worth knowing

MySQL also stores JSON, and its docs say JSON columns validate the documents stored in them. We didn't compare it in full, because we found fewer MongoDB-specific facts than for the seven above.

What happens to your change feed

If other systems read your MongoDB change streams today, that feed doesn't come along. Each option has its own change data capture (CDC) feed: PostgreSQL has logical replication, DynamoDB has Streams, and Cassandra has a per-table change data capture log, switched on with a table property. Our CDC tools comparison covers tools that read these feeds.

Which MongoDB alternative should you pick?

• You want to keep your MongoDB code: Amazon DocumentDB on AWS, Azure DocumentDB on Azure, FerretDB if you self-host. Test first, always.
• You want documents but are fine with a new language: Couchbase.
• You need joins, reports and SQL: PostgreSQL with JSONB.
• You know your key lookups and you're on AWS: DynamoDB.
• You have heavy writes and a team to run a cluster: Cassandra.
• You want to keep MongoDB and add analytics: copy its changes into Iceberg or Parquet (see Where OLake Go fits). Our OLTP vs OLAP explainer covers why teams split the two.

If you can't say which route you're on yet, stay on MongoDB until you can. Or try this short quiz.

Which MongoDB alternative fits you?

Question 1 of 5

How much of your app can you change?

This one answer removes most of the list.

Where MongoDB fits

Choose MongoDB if your app uses features the alternatives list as missing or limited.

What its docs list

• ACID transactions across documents. They need featureCompatibilityVersion 4.0 on a replica set (a group of servers holding copies of the same data) or 4.2 on a sharded cluster, and MongoDB says a distributed transaction usually costs more than single-document writes.
• An operation on a single document is atomic. MongoDB says that because embedded documents and arrays can hold related data in one document, many practical use cases don't need multi-document transactions.
• Sharding with hashed and ranged keys, and a changeable shard key (the field that decides which server holds a document).
• Change streams with pre- and post-images, which you can switch on per collection from MongoDB 6.0. There is no pre-image for an inserted document and no post-image for a deleted one.
• Change streams work on replica sets and sharded clusters, and a stream can resume from a resume token as long as the oplog still holds enough history.

Where it falls short

• A document can't exceed 16 MiB, nesting stops at 100 levels, and a collection can have at most 64 indexes, per MongoDB's limits page.
• By default a transaction must run for less than one minute, and it waits up to 5 milliseconds for the locks it needs before it aborts, per MongoDB's transactions production considerations page.
• With a hashed shard key, range queries on that key are less likely to hit one shard and cause more cluster-wide broadcast operations.
• Change streams are restricted to database events, time series collections do not support them, and they only report changes persisted to a majority of data-bearing replica set members.
• Pre- and post-images use storage space and add processing time, and a change stream event must stay within the 16 MB document limit.
• On Atlas, the Free tier includes 512 MB of storage and Flex includes 5 GB, and Dedicated clusters start at $56.94 a month, per the Atlas pricing page linked above.

On a Hacker News thread about MongoDB costs (November 2025), one commenter wrote "MongoDB Atlas was around 500% more expensive than in-house every time I evaluated it". A PeerSpot reviewer in April 2026 wrote "we prefer this because of the reliability of MongoDB Atlas", per PeerSpot's Atlas reviews.

Worked example: one app, five answers

Take an app that sends bulkWrite calls, uses a wildcard index, and stores some 5 MB documents. It's a made-up app, but each cell below comes from the vendor pages cited in the sections above. "Unknown" means we didn't read a page that answers it.

Need	Amazon DocumentDB	Azure DocumentDB	FerretDB	Couchbase	DynamoDB
Wildcard index	Marked "No"	Listed as supported	Unknown	Not MongoDB code, so rewrite	Not MongoDB code, so rewrite
bulkWrite	Unknown	Not supported	Not supported	Not MongoDB code, so rewrite	Not MongoDB code, so rewrite
5 MB document	Unknown	Unknown	Unknown	Fits (20 MiB value limit)	Fails (400 KB item limit)

The result: no compatible engine passes without a change. Two fail on a named item, and the rest need a test. Write your own feature list before you pick.

What to test before you switch

1. Your feature list. Write down every command and operator your app uses, then check each against the vendor's compatibility page. If you're on Route 1, do this before anything else.
2. Your biggest document. Compare it with the item limit: 400 KB on DynamoDB, 20 MiB on Couchbase, 16 MiB on MongoDB, 1 GB per field in PostgreSQL.
3. Your consistency assumptions. MongoDB's default write concern is majority, except on some replica sets with arbiters, where it is { w: 1 }. DynamoDB reads are eventually consistent by default, and Cassandra single-table writes are eventually consistent.
4. Your change feed. List who reads change streams today and how long history must be kept.
5. Your real bill. List prices use different meters (monthly tier, hourly instance, request), so price your own traffic in the vendor calculator.
6. What to ask each vendor. For any compatibility, speed or scale claim (Azure's 99.03%, DynamoDB's single-digit milliseconds, DocumentDB's "fast, reliable" wording), ask what was measured, on which versions, and whether your commands were in the test. We tested none of them.

A safe order for moving

This order is our own suggestion and does not come from a vendor.

1. Audit. List every command, operator and index your app uses, and the biggest document it stores.
2. Test in parallel. Run the same queries against the new engine with copied data, then compare answers.
3. Move reads first. Send read traffic to the new system while MongoDB stays the source of truth.
4. Move writes last. Switch writes only after you've tested a rollback.

Where OLake Go fits

OLake Go is not a MongoDB alternative. It is for the team that keeps MongoDB for the app and wants analytics somewhere else. We build it, and it is open source. It reads MongoDB 4.0 or higher with full refresh, incremental sync or change capture (CDC), which needs a replica set, and writes Apache Iceberg tables or Parquet files that analysts can query outside MongoDB. Our docs cover self-hosted MongoDB and Atlas, and a data filter can limit which rows a stream replicates. Setup steps are on our MongoDB connector page. You run it yourself, with Docker, Helm on Kubernetes or the OLake UI; the quickstart walks through it.

Q1. What is the best MongoDB alternative?

It depends on how much you can change. Same MongoDB code: test Amazon DocumentDB, Azure DocumentDB or FerretDB. Joins and SQL: PostgreSQL with JSONB. Known key lookups on AWS: DynamoDB.

Q2. Is there a free MongoDB alternative?

Sort of. PostgreSQL, FerretDB and Cassandra are free software, but you still pay to run them. Atlas also has a free tier at $0 with 512 MB, Azure DocumentDB offers a free tier cluster, and Couchbase Community Edition is free for up to five nodes.

Q3. Is Amazon DocumentDB the same as MongoDB?

No. AWS says it emulates the MongoDB `3.6`, `4.0`, `5.0` and `8.0` APIs on its own engine, and that query plans may differ. Its docs list what's missing, such as wildcard and hashed indexes.

Q4. Is FerretDB a drop-in replacement for MongoDB?

Not fully. Its `v2.7` compatibility page lists transaction commit and abort, role commands and `bulkWrite` as unimplemented or unsupported. It fits only if your app avoids those.

Q5. Can PostgreSQL replace MongoDB?

For many apps, yes. `JSONB` stores documents inside a table and supports indexes. You'll have to rewrite queries as SQL, and the docs say any update locks the whole row, so keep documents a manageable size.

Q6. Why do teams leave MongoDB?

The usual reasons are the SSPL licence on the server, Atlas cost, and joins. Some teams stay and fix their data model instead.

Q7. Should I move to Azure Cosmos DB for MongoDB?

Microsoft's Cosmos DB for MongoDB overview page now tells people migrating a MongoDB app to consider Azure DocumentDB, so check the current docs before starting a new project on Cosmos DB.

Q8. Do I have to leave MongoDB at all?

No. If the app works and you only need reports or search, keep MongoDB and copy its changes elsewhere. OLake Go does this for MongoDB, with change capture, into Iceberg or Parquet. Other options are in our [data replication tools](/compare/best-data-replication-tools/) comparison.

Q9. Do I lose change streams if I switch?

Yes, you move to the new system's equivalent. Amazon DocumentDB supports change streams on instance-based clusters, Azure DocumentDB marks `$changeStream` supported, FerretDB does not document them, and DynamoDB Streams keep records for `24 hours`.

Conclusion

Start with your own feature list, pick your route, and test before you trust any "compatible" label. If you only need reports or search, keep MongoDB and copy its changes into a second system.

OLake Go

Replicate databases, Kafka, and S3 into Apache Iceberg with OLake Go, an open source EL engine built for Iceberg from the ground up.

SignupJoin SlackExplore OLake GitHub

Contact us at hello@olake.io