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It's a split decision.
MongoDB is a document database with a single query API, cloud Atlas service and self-managed option. Redis is a real-time data platform for caching, streaming, search and AI context, with hosted and self-managed deployments. Choose MongoDB when application data belongs in flexible documents and you need its database and search capabilities. Choose Redis when low-latency access, caching, agent memory or multi-region replication is central. Both offer free cloud tiers and self-hosting; compare the limits and operating costs for your workload.
01Side by side
| MongoDB | Redis | |
|---|---|---|
| Pricing model | Atlas hourly billing; Flex from $8 monthly | Cloud hourly billing; Essentials from $5 monthly |
| Free tier | 512 MB; up to 100 operations per second | 30 MB; one shared-cloud database |
| Self-hosting | Self-managed MongoDB; Community Edition available | Redis 8 open source; on-prem and hybrid |
| Integrations | Integrates with 100+ technologies | Works with any LLM, agent framework or cloud |
| Deployment model | Atlas across clouds or self-managed infrastructure | Hosted cloud, on-premises, hybrid or open source |
| Platforms | Web | Web |
| Limits and scaling | Dedicated clusters; Infinite public preview on M10+ | Tiered RAM and flash; multi-region Pro |
| Migration and lock-in | Relational Migrator moves data to MongoDB | Redis says it works without vendor lock-in |
MongoDB Choose MongoDB if
- Choose MongoDB if your app stores operational data as documents.
- Choose MongoDB if you want Atlas search and database capabilities together.
- Choose MongoDB if you need cloud or self-managed deployment.
Redis Choose Redis if
- Choose Redis if caching and real-time access are core workloads.
- Choose Redis if you need agent memory, context or vector search.
- Choose Redis if multi-region active-active replication matters.
02Pricing at real usage
MongoDB Atlas Free (M0) costs $0 and includes 512 MB storage, 32 MB sort memory and up to 100 operations per second. Atlas Flex is listed from $8/month, with a $0.011/hour base rate, 5 GB storage and usage costs of $8–$30/month. Dedicated Atlas starts at $56.94/month; cluster rates rise by tier, and storage and RAM vary. Atlas Infinite is $0.09/hour, available on M10+ dedicated clusters in public preview.
Redis Free includes 30 MB and one shared-cloud database. Essentials starts at $5/month, billed from $0.007/hour, with 250 MB–100 GB RAM and SSD. Pro starts at $0.014/hour with a $200/month minimum; the page also lists the first $200 free. Enterprise offers on-demand or annual pricing without a published price. Redis bills grow with plan and resource use; compare the workload’s memory, storage, uptime and region requirements before estimating spend.
03API, SDKs and integration
MongoDB describes a document model and a single query API for application data. Atlas adds database search, vector search, analytics and stream processing. Developers can use Compass as a GUI, consult MongoDB’s documentation and community forums, and use Relational Migrator for migration into MongoDB. Its homepage says it integrates with 100+ technologies.
Redis combines caching, streaming, search and other data capabilities on one real-time platform. Its homepage says it works with any LLM, agent framework, cloud or existing stack. Redis points developers to documentation and coding tools, and offers cloud, on-premises, hybrid and open-source deployment paths. The vendor pages do not name a language-by-language SDK matrix or CLI details, so verify the client libraries, protocol support and operational tooling your codebase needs before choosing. MongoDB’s query API and Redis’s broader platform framing are different integration starting points; neither alone establishes compatibility with a particular framework.
04Open source, self-hosting and lock-in
Both products can run outside a vendor-hosted cloud. MongoDB offers self-managed software and a Community Edition for local development at no cost; its homepage does not state the Community Edition licence. Enterprise Advanced is a commercial offering with Enterprise Server, management tools and support. Redis offers Redis 8 as free open-source software and supports on-premises and hybrid deployments; check the applicable licence and terms for your intended use.
Redis says it works with existing stacks and deploys without vendor lock-in. MongoDB offers Atlas across clouds and self-managed deployments, plus Relational Migrator to move data to MongoDB. The vendors do not describe equivalent export procedures on their product pages. Before committing, test a full data export and restore, including indexes, schemas, application queries and any search or vector features. Migration effort depends on how much application logic relies on each platform’s data model and services.
Questions people ask
Which is better, MongoDB or Redis?
Neither is the better choice for every workload. MongoDB is a document database with a single query API for application data. Redis is a real-time platform covering caching, streaming, search and agent context. Pick based on the role the system needs to play in your architecture, then compare costs and deployment requirements.
Is Redis faster than MongoDB?
Redis is positioned for real-time data access, caching and agent context, but the product pages provide no comparable benchmark against MongoDB. Performance depends on workload, data model, deployment and configuration. Benchmark representative operations in your own stack before making a latency claim or replacing a database.
Which is good, MongoDB or MySQL?
MongoDB fits applications designed around flexible documents and its document query API. MySQL is a relational database, so it may fit systems built around relational tables and SQL. The right choice depends on data shape, query patterns, consistency requirements and the rest of your stack; this comparison does not establish a universal winner.
Is MongoDB still relevant in 2026?
Yes. MongoDB continues to offer Atlas and self-managed software, with features for operational data, search, analytics, stream processing and AI applications. Whether it fits a new project depends on the workload, operating cost, data model and team experience—not on a general relevance ranking.