I’ve Tested Redis vs MongoDB: Which Database Should You Use?

Redis Cloud plans (as of October 2026)

	Free	Essentials	Pro
Best for	Prototypes	Production cache	Global real-time apps
Price	$0	From $0.007/hour (about $5/month)	From $0.014/hour, $200/month minimum
Key specs	30 MB, shared, community support	250 MB to 100 GB, 99.99% uptime, SSO and RBAC	Dedicated, 99.999% uptime, active-active

MongoDB Atlas plans (as of October 2026)

	Free (M0)	Flex	Dedicated (M10)
Best for	Learning	Small apps	Production primary data
Price	$0	$8 to $30/month by throughput	From $0.08/hour (about $56.94/month)
Key specs	512 MB, 100 ops/sec cap, no backups	5 GB, billed by ops/sec band	2 GB RAM, 2 vCPU, 10 to 128 GB storage

Our Methodology: We set up both databases ourselves, locally and on their managed free tiers, loaded the same sample data and ran the same reads, writes and queries against each.

On this page

• A Quick Comparison of Redis vs MongoDB
• 1. Best for Pricing: It’s a Tie!
• 2. Best for Raw Speed and Latency: Redis
• 3. Best for Data Modeling and Querying: MongoDB
• 4. Best for Durability and Data Safety: MongoDB
• 5. Best for Scaling and High Availability: MongoDB
• 6. Best for Data Stack Integration: MongoDB
• 7. Best for Ease of Use: Redis
• 8. Best for Support and Learning: MongoDB
• How We Test Databases
• Redis vs MongoDB: Our Winner
• Redis vs MongoDB FAQ

Redis is an in-memory data structure store, now at 8.10; MongoDB is a disk-based document database that shipped 9.0 on Atlas in late September. Both get filed under NoSQL and land in the same redis vs mongodb search, so which came out on top in our latest hands-on testing?

I can confidently say that MongoDB is the clear winner if you want one primary database for your application data, and Redis is the clear winner if you want sub-millisecond reads in front of it.

In this comparison, I’ll look at why, round by round: pricing, speed, data modeling, durability, scaling, data stack integration, ease of use and support.

This piece was updated on 10/07/26. Find out about the changes we’ve made: A full rewrite of our 2017 comparison for Redis 8 (AGPLv3 option; JSON, Query Engine and vector sets in core) and MongoDB 9.0 (GA September 29, 2026, Atlas first), plus a Redis Cloud vs Atlas pricing table and a data stack round.

💡 Key Takeaways

• Redis wins raw speed: sub-millisecond reads, hundreds of thousands of ops/sec per node in its own benchmark docs.
• MongoDB wins as a primary store: durable by default (majority writes, journaled), built for datasets far larger than RAM.
• Pricing is a tie: Essentials starts cheaper (about $5/month); Atlas gives more storage per dollar (512 MB free vs 30 MB).
• MongoDB is the easier warehouse source: certified Fivetran and Airbyte CDC connectors ride on change streams; Redis has none.
• Licensing caveat: Redis 8 adds OSI-approved AGPLv3 alongside RSALv2 and SSPLv1; MongoDB stays on the SSPL (as of October 2026).

A Quick Comparison of Redis vs MongoDB

Redis scores lower overall because it is a narrower tool, not a worse one: it wins the two rounds it was designed for and concedes the five that belong to a general-purpose database.

	Redis	MongoDB
Overall rating	4.3/5	4.6/5
Best for	Caching, sessions, real-time counters and queues	Primary application database
Starting price (managed, as of October 2026)	Redis Cloud Essentials from $0.007/hour (about $5/month)	Atlas Flex from $8/month
Free tier	Yes, 30 MB	Yes, 512 MB (M0)
License	RSALv2, SSPLv1 or AGPLv3 (Redis 8)	SSPL v1
Data structures and query features	12 native data type families (strings, hashes, lists, sets, sorted sets, streams, JSON, time series, vector sets, geospatial, probabilistic, arrays)	BSON documents, aggregation pipelines up to 1,000 stages, 64 indexes per collection
Pros	✔️ Sub-millisecond latency ✔️ Rich data types, including vector sets ✔️ Query Engine for search and indexing in core ✔️ Cheap paid entry ✔️ Free Redis Insight GUI	✔️ Datasets bigger than RAM ✔️ Aggregation, $lookup and multi-document ACID ✔️ Durable by default ✔️ Change streams for CDC ✔️ Atlas Search and Vector Search
Cons	❌ Cost scales with RAM ❌ Durability is opt-in (everysec can lose about 1 second) ❌ No joins, limited cross-slot operations ❌ License churn and the Valkey fork	❌ Higher latency than in-memory Redis ❌ 16 MiB document limit ❌ SSPL is not OSI-approved ❌ Dedicated starts at about $56.94/month

What Kind of Team Is Redis or MongoDB Best For?

Redis Is Best for… teams that already have a system of record and need sub-millisecond caching, sessions, rate limiting, leaderboards or a vector cache in front of it.

MongoDB Is Best for… teams that need one flexible, durable primary database for application data that will outgrow RAM and later feed a warehouse.

1. Best for Pricing: It’s a Tie!

The Winner: It’s a Tie!
Redis is cheaper to start, MongoDB is cheaper to grow. Redis Cloud Essentials starts at about $5/month against $8/month for Atlas Flex, but Essentials tops out at 100 GB of RAM-class memory, while an Atlas M60 pairs 64 GB of RAM with up to 4 TB of disk at $3.95/hour.

Redis Cloud’s free tier gives you 30 MB; Atlas M0 gives you 512 MB, 17 times more. The gap comes from what each bill measures: Redis Cloud sells memory, so every gigabyte you store is a gigabyte of RAM you rent, while Atlas sells instances (RAM, vCPUs and disk per tier, plus a throughput band on Flex).

A 2 GB session store costs less on Redis Essentials. A 500 GB order history only makes sense on Atlas, where most of it sits on disk and the working set lives in the WiredTiger cache.

Redis Cloud plan	Key features	MongoDB Atlas plan	Key features
Free ($0)	30 MB; shared; community support; best-effort SLA	M0 ($0)	512 MB; shared; 100 ops/sec cap; no backups
Essentials (from $0.007/hour, about $5/month)	250 MB to 100 GB; single database; up to 99.99% uptime; basic support	Flex ($8 to $30/month)	5 GB; billed by ops/sec band ($8 at 0 to 100 ops/sec, $30 at 400 to 500)
Pro (from $0.014/hour, $200/month minimum, first $200 credited)	Dedicated; multiple databases; up to 99.999% uptime; active-active; auto-tiering	Dedicated M10 (from $0.08/hour, about $56.94/month)	2 GB RAM; 2 vCPU; 10 to 128 GB storage; scales to M700

Prices as of October 2026.

ElastiCache for Valkey, the BSD-licensed Linux Foundation fork of Redis, is the cheaper AWS route: AWS prices it 20% below its other node-based engines and 33% below on serverless, though you get Valkey, not Redis 8’s vector sets or Query Engine. Amazon DocumentDB speaks the MongoDB API without being MongoDB; price it in the AWS calculator and test your aggregation pipelines first. DynamoDB is the other AWS-native option if a key-value model fits.

🤔 Do Redis and MongoDB Offer Free Tiers?

• Redis Cloud Free: 30 MB, shared, community support only.
• MongoDB Atlas M0: 512 MB, capped at 100 ops/sec, no backups.

Both are also free to self-host: Redis Open Source 8 under AGPLv3, MongoDB Community under the SSPL.

If your hot dataset fits in a few gigabytes, Redis Essentials is the cheapest production option here; past that, Atlas wins on cost per gigabyte, and the margin widens as data grows.

2. Best for Raw Speed and Latency: Redis

The Winner: Redis
Sub-millisecond reads from memory. Redis’s own redis-benchmark reference run clears 99.76% of one million SETs in under 1 ms with a p50 around 0.14 ms, and pipelined runs top 1.5 million SETs per second on a laptop.

Most “Redis is 100x faster than MongoDB” claims measure the network, not the database: Redis’s own benchmarking guidance says a naive synchronous loop measures client and network latency.

The real gap is architectural: Redis answers every command from RAM and executes it mostly single-threaded, so a GET never touches disk, while MongoDB serves reads through the WiredTiger cache, falls back to disk when the working set outgrows it, and spreads work across cores.

No current independent head-to-head benchmark exists; the last public one, a 2017 YCSB run of Redis 3.2 against MongoDB 3.2 on Percona’s in-memory engine, showed that split: Redis won the 95/5 read-mostly workload on a single thread, MongoDB did better on writes once it had spare CPU, and Redis used considerably more RAM. Treat the gap as structural rather than a score.

What we ran:

redis-benchmark -q -n 100000 -t set,get -r 100000
redis-benchmark -q -n 100000 -t set,get -r 100000 -P 16

When I ran both on a laptop, pipelining multiplied throughput many times over, as Redis’s docs predict; the unpipelined number is the one a real application resembles.

💭 Notes from our testers
Flipping on AOF with fsync everysec before comparing Redis to MongoDB is the fair test; that is how production runs. The gap also narrows on complex queries: Redis pushes filtering and sorting into your application code, and that time never shows up in redis-benchmark.
Panoply testing team

Top Tip 💡
Always benchmark with the persistence setting you’ll run in production, and report pipelined and unpipelined numbers separately.

For a single key lookup, Redis wins every time. For a filtered, sorted, multi-field query, MongoDB does the work server-side that Redis would leave to you.

3. Best for Data Modeling and Querying: MongoDB

The Winner: MongoDB
Real queries without app-side glue. MongoDB stores documents up to 16 MiB with 64 indexes per collection, aggregation pipelines up to 1,000 stages, $lookup and multi-document ACID transactions.

Developers who run Redis as a primary store describe the same problem: you can’t scan a year of data on every question, so you end up writing an external process, your own query engine, to maintain the answer.

Redis 8 narrowed that gap: JSON, time series, probabilistic types and the Query Engine moved into core, adding secondary indexes, full-text, vector search and aggregations across 12 native data type families. Every index still lives in RAM, and MULTI/EXEC has no rollback. Redis’s creator built LamerNews, a Hacker News clone on Redis alone, with every relational rule in application code.

MongoDB models data as BSON documents and recommends embedding related data in one document, so most queries touch one document. MongoDB’s own guidance: distributed transactions cost more than single-document writes, “should not be a replacement for effective schema design,” and run 60 seconds by default. See our MongoDB best practices guide for embed versus reference.

db.orders.aggregate([
  { $match: { status: "paid" } },
  { $group: { _id: "$customerId", total: { $sum: "$amount" } } },
  { $sort: { total: -1 } }, { $limit: 5 }
])

In mongosh this was one statement; in Redis it meant maintaining a sorted set of customer totals on every write, then ZRANGE with the REV option for the top five.

🤖 What About Vector Search for AI Apps?

Redis 8 ships vector sets (HNSW, cosine similarity, filtered search), and the Query Engine indexes vectors inside JSON and hash documents. MongoDB’s $vectorSearch stage offers ANN and exact search, hybrid ranking with full-text, pre-filtering, up to 8,192 dimensions and automated embedding via Voyage AI. Pick Redis for a low-latency semantic cache, MongoDB when vectors live next to the documents they describe.

Best for: application data with filters, sorts and relationships. Skip if: every query is a key lookup; Redis is faster there.

4. Best for Durability and Data Safety: MongoDB

The Winner: MongoDB
Durable by default. MongoDB’s default write concern is w: majority with journaling, so an acknowledged write already sits on a majority of nodes. Redis’s default appendfsync everysec can lose about one second of writes, and Redis Cluster can drop acknowledged writes during a failover.

Redis does not persist like a database out of the box; each option trades speed for safety:

• RDB snapshots (save 60 1000): quick to restore, but be ready to lose the last minutes of data.
• AOF, appendfsync always: every write hits disk before acknowledgment; safest and slowest.
• AOF, appendfsync everysec: the default, about a one-second loss window.
• AOF, appendfsync no: the OS flushes, about 30 seconds on Linux.
• RDB plus AOF: Redis’s own route to safety “comparable to what PostgreSQL can provide”; 8.10 adds a BACKUP command family.

MongoDB journals majority writes by default, and since 8.0 a majority write is acknowledged once a majority of nodes has durably written the oplog entry. Two caveats: w:1 or w:0 writes can roll back if the primary steps down, and a secondary read right after a write may not see it yet.

Our redis.conf:

appendonly yes
appendfsync everysec
save 60 1000

💭 Notes from our testers
With AOF off, I killed the Redis process and restarted it, and every key written since the last snapshot was gone. The same kill against MongoDB brought back every acknowledged write from the journal.
Panoply testing team

Top Tip 💡
Run RDB and AOF together and copy the RDB files off-host on a schedule; BACKUP START and BACKUP SEAL on 8.10+ make that scriptable.

If losing a second of writes is unacceptable, MongoDB is your system of record and Redis holds data you can rebuild.

5. Best for Scaling and High Availability: MongoDB

The Winner: MongoDB
Grows past RAM without a rewrite. Replica sets run up to 50 members (7 voting), Atlas supports up to 70 shards in a single region, and Atlas Infinite, now in public preview, stretches a single HA shard to 128 TB.

What happens when your dataset is ten times your RAM? On MongoDB, most of it moves to disk and the working set stays in cache. On Redis, you buy ten times the RAM, or you shard.

Redis Cluster splits the keyspace into 16,384 hash slots and shards automatically, with hash tags like user:{123}:cart keeping multi-key operations on one slot. Replication is asynchronous, so a failover can lose acknowledged writes, and the cluster becomes unavailable when a majority of masters is unreachable. Redis Cloud Pro answers the cost problem with auto-tiering (RAM plus flash) and the latency problem with active-active geo-distribution and up to 99.999% uptime.

MongoDB shards in 128 MB chunks by default, and Atlas scales from M10 (2 GB RAM) to M700 (768 GB RAM) with up to 4 TB of storage per node. If you’re weighing MongoDB’s sharding against a wide-column store, our Cassandra vs MongoDB comparison covers that trade-off.

Where each one breaks:

• Redis: the RAM ceiling, and multi-key operations that span slots.
• MongoDB: the 16 MiB document limit, and a poorly chosen shard key you can’t easily change.
• Both: the cost and latency of cross-region writes.

Redis scales throughput; MongoDB scales data. Redis Cloud Pro’s active-active writes across regions are the one scaling feature MongoDB doesn’t match here, so if that is your hard requirement, Redis takes this round for you.

6. Best for Data Stack Integration: MongoDB

The Winner: MongoDB
A first-class CDC source for your warehouse. Change streams emit majority-committed inserts, updates and deletes with resume tokens; Fivetran’s connector and Airbyte’s certified MongoDB source v2 build on them. Redis has no first-party Fivetran or Airbyte source.

Operational data that lives only in Redis has no first-party path into Snowflake or BigQuery; MongoDB’s warehouse path is built in. Our setup:

1. Run a replica set or Atlas cluster. A standalone mongod has no change streams.
2. Create a read-only user with readAnyDatabase or per-database read.
3. Size the oplog so a paused sync can resume; an expired cursor forces a full re-sync.
4. Point Fivetran or Airbyte at the cluster. Both take an initial find() snapshot, then follow change streams; enable pre- and post-images on 6.0+.

Fivetran turns deletes into soft deletes flagged _fivetran_deleted and maps only first-level fields to columns, leaving nested objects as JSON. Our MongoDB ETL tools roundup compares the managed options; Debezium is the open-source CDC route via Kafka Connect.

Redis’s integration runs the other way: Redis Data Integration (RDI) uses CDC to pull from MySQL, PostgreSQL, Oracle, SQL Server and MongoDB 6.0 through 8.0 into Redis at roughly 10,000 records per second per core for 1 KB records, at-least-once; Redis Cloud’s managed RDI accepts Atlas as a source. Getting data out of Redis to a warehouse means a third-party tool (CData, CloudQuery) or a consumer you write on Redis Streams.

💭 Notes from our testers
Our MongoDB CDC sync was mostly permissions and oplog sizing; with those in place, the connector did the rest. The Redis-to-warehouse route meant writing our own Streams consumer and deciding what a “row” even was.
Panoply testing team

Top Tip 💡
Treat MongoDB as the warehouse source and let RDI push the hot subset into Redis, so analytics never depends on cache contents.

If your data ends up in Snowflake or BigQuery, make MongoDB the source of truth.

7. Best for Ease of Use: Redis

The Winner: Redis
Fewer concepts between you and your first read. A handful of commands (SET, GET, EXPIRE, HSET, ZADD) covers most caching jobs, and Redis’s browser playground runs them with nothing installed.

MongoDB isn’t hard; it just asks for more decisions up front: cluster tier, IP access list, database user, document shape. Redis asks for a key and a value.

My Experience Testing Redis

I started with docker run -p 6379:6379 redis:latest, then repeated the setup on Redis Cloud Free. My first three commands in redis-cli covered a session store:

redis-cli SET session:42 '{"user":"ana"}' EX 3600
redis-cli GET session:42
redis-cli TTL session:42

Redis Insight, the free GUI, let me browse keys, inspect TTLs and run the same commands with Redis Copilot suggesting syntax; it also carries the RDI pipeline builder. Two things slowed me down: choosing the right data structure (hash, JSON or sorted set) takes practice, and explaining the three license options (RSALv2, SSPLv1, AGPLv3) to a legal reviewer takes longer than the install.

My Experience Testing MongoDB

Atlas signup took an email, then four steps before my first connection string worked: deploy an M0 cluster, add my IP to the access list, create a database user, paste the string into mongosh. Compass, the free GUI, was the highlight: its visual aggregation pipeline builder let me assemble the $match, $group and $sort stages from round 3 one stage at a time, with an embedded mongosh and a natural-language query box for the parts I didn’t want to type. The friction arrives later: embed or reference is a schema decision you make early and live with.

💭 Notes from our testers
Redis felt instantly usable for caching; I had a session store running before I had finished reading the data types page. MongoDB’s Compass made exploring unfamiliar data easier than browsing Redis keys, though, and that matters once someone else has to understand your data.
Panoply testing team

Best for: developers who want a cache or queue working this afternoon. Skip if: your team will spend more time exploring data than caching it.

8. Best for Support and Learning: MongoDB

The Winner: MongoDB
Published response times at every tier. Atlas Sev-1 response goals run from 8 hours on Developer to 2 hours on Pro, 30 minutes on Enterprise and 15 minutes on Platinum, and Basic support is free for every Atlas user.

The free MongoDB tier comes with a support plan. The free Redis tier does not.

MongoDB Support

• ✔️ Every Atlas user gets Basic support at no cost.
• ✔️ Each paid plan publishes a Sev-1 response goal, so you know what you are buying.
• ✔️ MongoDB University at learn.mongodb.com offers free courses, with on-demand access included in paid Atlas support.
• ✔️ Compass and mongosh ship with thorough documentation.
• ❌ Paid support is priced as a percentage of Dedicated cluster spend, with no public list price.

Redis Support

• ✔️ Four plans cover the range: Developer (business hours, non-production), Business (24×7), Enterprise (adds a TAM and RCAs) and Premium Enterprise.
• ✔️ Redis University and a no-install playground cover the learning side.
• ✔️ Essentials includes basic support.
• ❌ Redis Cloud Free is community support only.
• ❌ Redis does not publish response-time targets on its support page.

Teams that need a contractual response time on day one will find MongoDB’s plans easier to evaluate. Redis’s Business and Enterprise plans cover 24×7 and a TAM, but without a published response goal you cannot compare them on paper.

How We Test Databases

We tested Redis and MongoDB across seven key areas, weighted by how much each matters to developers and data engineers choosing a database:

• Performance and latency: 20%. Read and write latency and throughput with production-like persistence settings.
• Data modeling and querying: 20%. How much of a real query the database does for you.
• Durability and reliability: 15%. Default write safety, data-loss windows and failover behavior.
• Pricing and value: 15%. Free tiers, entry paid plans and cost per GB as data grows.
• Scalability and high availability: 10%. Sharding, replication and managed uptime SLAs.
• Data stack integration: 10%. CDC, ETL connectors and warehouse paths.
• Ease of use and support: 10%. Setup, tooling, docs and support plans.

Prices and licenses are current as of October 2026.

That weighting is why MongoDB finishes at 4.6 against Redis’s 4.3: Redis takes performance and ease of use outright, but the heavier modeling, durability and integration rounds go to MongoDB. Most importantly, we’ve tested both Redis and MongoDB ourselves: we installed them locally, spun up the free managed tiers, loaded the same sample data, and ran the same commands you’ve seen in this article.

Redis vs MongoDB: Our Winner

MongoDB is our winner, and the database most teams should build on. It won five of our eight rounds (data modeling, durability, scaling, data stack integration and support), it is durable by default, it grows past RAM without a rewrite, and change streams make it a first-class source for your warehouse.

Redis won the rounds it was built for, and won them clearly: raw speed, ease of use and the cheapest paid entry at about $5/month, plus vector sets and active-active on Pro that MongoDB doesn’t match. The best stack we tested uses both. MongoDB holds the source of truth; Redis holds sessions, rate limits, leaderboards and hot reads, kept in sync by cache-aside code or by Redis Data Integration with MongoDB as the CDC source.

I recommend giving both a test run on their free tiers, Redis Cloud’s 30 MB and Atlas M0’s 512 MB, with your own data and your real query shapes before you commit.

Redis vs MongoDB FAQ

Is Redis faster than MongoDB?

Yes, for in-memory key lookups. Redis’s own benchmark docs show sub-millisecond latency and 100,000-plus ops/sec per node without pipelining, while MongoDB reads pass through the WiredTiger cache and disk. The gap narrows on filtered, multi-field queries that Redis would push into your code, and MongoDB uses multiple cores where Redis executes commands mostly single-threaded.

Can Redis replace MongoDB?

Only when your dataset fits in RAM, you have modeled it around Redis’s data types, and you have switched on AOF persistence. Redis 8’s Query Engine adds secondary indexes, full-text and vector search, but there are still no joins and MULTI/EXEC cannot roll back. For a primary store that will outgrow memory, MongoDB is the safer pick.

Can MongoDB be used as a cache?

Yes, with TTL indexes to expire documents automatically and the WiredTiger cache to keep hot data in RAM, but every read still goes through a disk-backed storage engine and you pay for an instance rather than memory. Redis answers from RAM in under a millisecond and costs about $5/month on Essentials, so it is the better cache.

Are Redis and MongoDB open source?

As of October 2026, Redis 8 is available under AGPLv3, which is OSI-approved, alongside RSALv2 and SSPLv1. Redis 7.4 to 7.8 were RSALv2/SSPLv1 only, 7.2 and earlier are BSD, and Valkey is the BSD fork. MongoDB is source-available under the SSPL, which OSI does not approve.

Does Redis support ACID transactions?

Partly: MULTI/EXEC runs a group of commands atomically with no interleaving, but a failed command inside the block does not roll the others back, and durability depends on your fsync setting. MongoDB has offered multi-document ACID transactions on replica sets since 4.0 and on sharded clusters since 4.2, with a 60-second default lifetime.

How do I get Redis or MongoDB data into Snowflake or BigQuery?

For MongoDB, point Fivetran or Airbyte’s certified connector at a replica set or Atlas cluster; both snapshot the collections, then follow change streams. For Redis, no first-party Fivetran or Airbyte source exists, so you use CData or CloudQuery or write a consumer on Redis Streams. Redis Data Integration moves data into Redis, not out of it.

Should I use Redis and MongoDB together?

Yes, and it is a common production pattern: MongoDB as the source of truth, Redis for sessions, rate limits, leaderboards and hot reads. Keep them in sync with cache-aside code, or let Redis Data Integration pull changes from MongoDB 6.0 to 8.0 over CDC at roughly 10,000 records per second per core.