These are the best alternatives to MongoDB depending on your use case:
- Tinybird
- Amazon DocumentDB
- Azure Cosmos DB API for MongoDB
- FerretDB
- PostgreSQL + JSONB
- Couchbase
- Amazon DynamoDB
- Google Firestore
- Apache Cassandra / ScyllaDB
- Elasticsearch
MongoDB works exceptionally well when you need a flexible document model, rapid iteration, and access to a massive ecosystem. But searching for "MongoDB alternatives" usually isn't about finding a clone, it happens when priorities shift.
Cost surprises, analytics performance, operational complexity at scale, vendor lock-in concerns, or the realization that your "MongoDB workload" is actually search or real-time analytics in disguise.
The useful way to approach this decision: identify what part of MongoDB delivers value (documents, scaling, tooling) and what part is holding you back. Then find the right tool for your actual problem.
2026 market update: most teams evaluating MongoDB alternatives split into two paths: PostgreSQL + JSONB for application data, or MongoDB + analytics layer for serving metrics and dashboards.
Need real-time analytics without MongoDB's aggregation limitations? If your MongoDB is struggling with dashboards, metrics, or analytical queries, try Tinybird. It's a real-time data platform built on ClickHouse® that ingests data from MongoDB via CDC and serves sub-100ms analytical APIs, no infrastructure management required.
10 Best Alternatives to MongoDB
1. Tinybird
Let's start with a fundamentally different approach. If your reason for searching "MongoDB alternatives" is aggregation performance, dashboard latency, or analytical queries timing out, you're not looking for another document database. You need an analytical engine.
Tinybird is a real-time data platform built on ClickHouse® that transforms streaming data into sub-100ms analytical APIs. Instead of forcing MongoDB to do OLAP work it wasn't designed for, Tinybird handles analytics as a specialized layer.
Why MongoDB Struggles with Analytics
MongoDB is an excellent OLTP document store. But when teams use it for:
- Real-time dashboards with complex aggregations
- User-facing analytics embedded in products
- Time-series queries across millions of events
- Cohort analysis and behavioral metrics
Performance degrades rapidly. Aggregation pipelines that worked on 10 million documents become painfully slow at 500 million. The architecture simply wasn't designed for analytical workloads.
How Tinybird Solves This
Rather than replacing MongoDB entirely, the proven pattern is MongoDB for transactional workloads + Tinybird for analytics:
- Ingest via CDC: Use MongoDB Change Streams to capture changes in real-time. Tools like Debezium connect Change Streams to Kafka, which Tinybird ingests natively
- Columnar storage: ClickHouse®'s columnar architecture processes analytical queries 10-100x faster than document stores
- Instant APIs: Any SQL query becomes a production-ready HTTP endpoint with built-in authentication, rate limiting, and documentation
- Sub-100ms latency: Serve thousands of concurrent users with consistent millisecond response times, enabling reliable delivery to any downstream system consuming analytics APIs.
Integrated Data Pipeline
Tinybird provides fully managed connectors for ingesting data from:
- Apache Kafka and Confluent Cloud (perfect for MongoDB CDC via Debezium)
- Amazon S3, Google BigQuery, Snowflake
- HTTP streaming endpoint for direct event ingestion
No external ETL services to build or maintain. Data flows continuously and becomes queryable in milliseconds.
Developer-First Experience
Unlike traditional analytics tools, Tinybird integrates with modern development workflows:
- SQL-based transformations, no proprietary query languages
- Git integration for version control and CI/CD
- CLI for local development
- Instant API publication from any query
Predictable Pricing
Tinybird's base plans have predictable monthly pricing, starting with a free tier and a Developer plan at $25/month, with scalable Enterprise plans. Additional consumption is billed per vCPU—no per-minute billing surprises or complex unit calculations.
When Tinybird Makes Sense
Tinybird is ideal when:
- Your MongoDB aggregations are too slow for production
- You need user-facing analytics with sub-100ms latency
- You want to offload analytical queries without replacing your transactional database
- You're building real-time dashboards, metrics, or embedded analytics
- You need streaming ingestion with instant queryability
2. Amazon DocumentDB
Amazon DocumentDB is AWS's managed document database with MongoDB compatibility. It's designed to provide a familiar MongoDB experience within the AWS ecosystem.
What DocumentDB Offers
- Managed service: Automated backups, patching, monitoring, and scaling within AWS
- MongoDB driver compatibility: Connect using existing MongoDB drivers and tools
- Integration: Works seamlessly with other AWS services (Lambda, EC2, VPC)
- Storage scaling: Automatically grows storage up to 128 TiB
Compatibility Considerations
DocumentDB implements a subset of MongoDB's API. AWS publishes detailed documentation on functional differences, including:
- Not all aggregation pipeline stages are supported
- Certain index types behave differently
- Transaction semantics have limitations compared to MongoDB 4.0+
Critical point: "Driver compatible" doesn't mean your application will behave identically. Validate your specific queries and aggregations against AWS's compatibility matrix before migrating.
Pricing Structure
DocumentDB uses a combination of:
- Instance hours (varying by instance size)
- Storage (per GB-month)
- I/O requests (per million)
- Backup storage (beyond included retention)
Costs can grow quickly with I/O-heavy workloads or large datasets.
When DocumentDB Fits
- You're already in AWS and want managed operations
- Your MongoDB usage is relatively standard (CRUD, basic aggregations)
- You've validated compatibility with your specific queries
- You prioritize operational simplicity over full feature parity
Limitations
- Compatibility gaps with newer MongoDB features
- No true MongoDB, AWS controls the roadmap
- I/O-based pricing can create cost surprises at scale
3. Azure Cosmos DB API for MongoDB
Azure Cosmos DB offers a MongoDB-compatible API layer on top of its globally distributed database engine, reflecting the scalability and elasticity typical of modern cloud computing environments.
Global Distribution
Cosmos DB's standout feature is turnkey global distribution. Deploy data across multiple Azure regions with configurable consistency levels, from strong to eventual, with automatic failover.
Compatibility Reality
Microsoft publishes feature support matrices by MongoDB version (4.0, 4.2, 5.0, 7.0). Support varies significantly:
- Some aggregation operators are partially supported
- Certain commands have behavioral differences
- Index types and options may not match MongoDB exactly
Always check the compatibility matrix for your specific MongoDB version and features.
Pricing Model
Cosmos DB uses Request Units (RUs) as an abstracted throughput metric. You provision RUs (or use serverless) plus pay for storage. This model can be cost-effective for predictable workloads but complex to estimate for variable traffic.
When Cosmos DB Fits
- You're committed to Azure and need global distribution
- You want Mongo-like syntax with multi-region capabilities
- You've mapped your queries to Cosmos DB's supported features
- Configurable consistency levels matter for your use case
Limitations
- RU-based pricing requires careful capacity planning
- Feature gaps may require application changes
- Vendor lock-in to Azure's implementation
4. FerretDB
FerretDB takes a different approach: it provides MongoDB wire protocol compatibility while using PostgreSQL as the storage backend.
The Value Proposition
- Use MongoDB drivers and tools with a PostgreSQL backend
- Open source with no vendor lock-in
- Leverage PostgreSQL's ecosystem: backups, replication, extensions, mature tooling
- Transparent data storage: Your data lives in standard PostgreSQL tables
How It Works
FerretDB translates MongoDB queries into PostgreSQL operations. Your application connects using standard MongoDB drivers, but data is stored and processed by PostgreSQL.
Current State
FerretDB is actively developed with growing compatibility. However:
- Not all MongoDB features are implemented yet
- Performance depends on how queries map to PostgreSQL
- Some edge cases may behave differently
When FerretDB Fits
- You want to escape MongoDB licensing while keeping the developer experience
- You prefer PostgreSQL's operational model and ecosystem
- Your MongoDB usage is relatively straightforward
- Open source and data control are priorities
Limitations
- Compatibility is incremental, check supported features
- Performance characteristics differ from native MongoDB
- May require query adjustments for complex operations
5. PostgreSQL + JSONB
Many teams searching for MongoDB alternatives end up at PostgreSQL. The reason: you need document flexibility but also want SQL, transactions, joins, and referential integrity.
Why PostgreSQL Works
PostgreSQL's JSONB data type stores JSON documents with binary efficiency. Combined with GIN indexes, you can query JSON fields with good performance.
The key advantages:
- Full SQL support: Complex queries, joins, window functions, CTEs
- ACID transactions: Strong consistency guarantees
- Hybrid modeling: Mix relational tables with JSONB columns
- Massive ecosystem: Extensions, tooling, managed services, expertise
Indexing JSONB Correctly
JSONB performance depends on proper indexing. PostgreSQL offers two GIN operator classes:
- jsonb_ops: General-purpose, supports more operators
- jsonb_path_ops: Fewer operators but can be faster for containment queries
Critical point: GIN indexes accelerate reads but add write overhead and storage. Design indexes based on your actual query patterns, not "just in case."
When PostgreSQL + JSONB Fits
- You need SQL, joins, and constraints alongside document flexibility
- Your JSON documents have reasonable structure (not infinite schema chaos)
- You want to consolidate systems rather than operate multiple databases
- Ad-hoc reporting and analytical queries matter
Limitations
- Not optimized for purely document-centric workloads
- JSONB without discipline leads to unmaintainable schemas
- Scaling requires more operational effort than managed document stores
6. Couchbase
Couchbase positions itself as a multi-model platform: key-value, document, SQL++ (formerly N1QL), full-text search, and analytics in one system.
The Multi-Model Advantage
Instead of operating separate systems for different access patterns, Couchbase provides:
- Key-value access for sub-millisecond reads
- SQL++ for flexible document queries
- Full-text search integrated into the platform
- Analytics for complex queries without impacting operations
When Couchbase Fits
- Your problem combines multiple access patterns (KV, queries, search)
- You want to reduce operational complexity vs. running separate systems
- Enterprise support and SLAs matter
- You're willing to evaluate with realistic workloads
Considerations
- Enterprise licensing can be expensive
- Value depends on actually using multi-model capabilities
- Different operational model than MongoDB
7. Amazon DynamoDB
DynamoDB is AWS's fully managed key-value and document database, designed for extreme scale and minimal operational overhead.
When DynamoDB Excels
- Predictable access patterns: Queries based on partition key (+ sort key)
- Massive concurrency: Millions of requests per second
- Variable traffic: On-demand capacity adjusts automatically
- Zero operations: AWS handles everything
The Design Requirement
DynamoDB demands upfront design thinking. You must model tables around your access patterns. If your queries don't fit partition key + sort key patterns, you'll need secondary indexes (with additional cost) or face table scans.
Pricing Models
- On-demand: Pay per request (good for variable/unpredictable traffic)
- Provisioned: Pre-allocate capacity (cheaper for steady workloads)
Both include storage costs and data transfer charges.
When DynamoDB Fits
- Access patterns are well-defined and key-based
- You need extreme scale with minimal operations
- You're committed to AWS ecosystem
- You accept query flexibility tradeoffs for performance
Limitations
- Ad-hoc queries are expensive or impossible
- Requires careful partition key design to avoid hot partitions
- No complex joins or relational queries
8. Google Firestore
Firestore is Google's serverless document database, optimized for mobile and web applications with real-time sync capabilities.
Built for App Development
- Real-time listeners: Data syncs automatically to clients
- Offline support: Apps work without connectivity
- Serverless: No capacity planning or server management
- Firebase integration: Authentication, hosting, functions
Query Model
Firestore requires indexes for every query pattern. Google automatically creates single-field indexes, but composite queries need explicit index definitions.
When Firestore Fits
- Building mobile or web apps with real-time requirements
- Developer velocity matters more than query flexibility
- You want sync and offline capabilities built-in
- You're already in the Firebase/Google Cloud ecosystem
Limitations
- Query flexibility is constrained
- Index management adds overhead
- Less suitable for complex backend analytics
9. Apache Cassandra / ScyllaDB
When the problem is massive write throughput, time-series data, or globally distributed operations, Cassandra and its compatible alternatives enter the conversation.
Cassandra's Architecture
Cassandra is a wide-column distributed database designed for:
- Write-heavy workloads: Optimized for high-velocity inserts
- Partition-based access: Data distributed by partition key
- Tunable consistency: Balance consistency vs. availability per operation
- No single point of failure: Peer-to-peer architecture
ScyllaDB: Cassandra-Compatible, Higher Performance
ScyllaDB implements CQL compatibility with a focus on lower latency and better hardware utilization. It also offers DynamoDB-compatible APIs.
When Cassandra/Scylla Fits
- Events, telemetry, IoT, time-series workloads
- Write throughput matters more than query flexibility
- You can model data by partition with predictable access
- Global distribution with tunable consistency
Limitations
- Queries must align with partition key design
- Not suitable for ad-hoc, flexible queries
- Requires careful data modeling upfront
10. Elasticsearch
If your "MongoDB alternative" search is really about full-text search, relevance scoring, faceted navigation, or autocomplete, Elasticsearch is the specialized tool.
What Elasticsearch Does Best
- Full-text search with powerful analyzers
- Relevance scoring and ranking
- Faceted navigation and aggregations
- Near real-time indexing (documents searchable within ~1 second)
What Elasticsearch Is Not
Elasticsearch isn't a transactional database. It's near real-time, not ACID-compliant. Don't use it as your system of record.
Common Architecture Pattern
- MongoDB (or another database): System of record for transactional data
- Elasticsearch: Search projection, synced via application logic or CDC
When Elasticsearch Fits
- Search, autocomplete, relevance are core requirements
- You need facets, filters, and aggregations on text data
- You understand it's a search engine, not a general database
Decision Framework: Choosing Your MongoDB Alternative
Step 1: Define Your Actual Problem
Don't replace MongoDB just because. Identify the specific pain:
- Aggregation/analytics too slow → Tinybird, ClickHouse® (OLAP layer)
- Cost surprises on Atlas → DocumentDB, self-hosted, or managed alternatives
- Need SQL and joins → PostgreSQL + JSONB
- Key-based access at scale → DynamoDB
- Full-text search → Elasticsearch
- Write-heavy time-series → Cassandra / ScyllaDB
- Stay Mongo-compatible on AWS/Azure → DocumentDB, Cosmos DB
- Open source, no lock-in → FerretDB, PostgreSQL
Step 2: Evaluate Total Cost
Don't just compare sticker prices:
- Infrastructure costs (compute, storage, networking)
- Operational costs (team time, on-call burden)
- Migration costs (code changes, testing, validation)
- Opportunity costs (what your team isn't building)
Step 3: Validate Compatibility
For "MongoDB-compatible" options:
- Check feature matrices by version
- Test your actual queries in staging
- Identify gaps before production migration
Step 4: Consider the Architecture Pattern
The lowest-risk approach is often not replacing MongoDB entirely:
- Keep MongoDB for transactional workloads
- Add specialized systems for analytics (Tinybird), search (Elasticsearch), or time-series (Cassandra)
- Connect via CDC using MongoDB Change Streams
When Tinybird Is the Better Choice for Analytics
After reviewing all the options, one pattern becomes clear: most teams searching for MongoDB alternatives are actually struggling with analytics performance, not document storage.
MongoDB handles transactional document workloads exceptionally well. The pain starts when teams push it beyond its design, dashboards, real-time metrics, user-facing analytics, cohort analysis, time-series aggregations. That's not a MongoDB problem. It's using the wrong tool for the job.
The Real Problem: OLTP vs. OLAP
MongoDB is an OLTP vs OLAP (Online Transaction Processing) database. It's optimized for:
- Fast reads and writes of individual documents
- Flexible schema evolution
- Horizontal scaling for operational workloads
But analytical queries, aggregations across millions of rows, time-series calculations, complex filtering, real-time dashboards, require OLAP (Online Analytical Processing) architecture. Columnar storage. Vectorized execution. Compression optimized for scan-heavy workloads.
Forcing OLTP databases to do OLAP work is why aggregation pipelines slow down, why Atlas bills spike, and why teams start searching for alternatives.
Why Tinybird Solves This Better Than Any Other Option
Tinybird is a real-time analytics platform purpose-built for the exact workload MongoDB struggles with, leveraging ClickHouse®, the fastest database for analytics, to deliver sub-100ms query performance at scale.
Unlike DocumentDB or Cosmos DB (which are still document databases with the same analytical limitations), Tinybird uses ClickHouse®, a columnar OLAP engine designed from the ground up for analytical queries at massive scale.
Here's what that means in practice:
- Queries that take seconds in MongoDB often run in milliseconds in Tinybird
- Aggregations across billions of rows return in real-time
- Concurrent users don't kill performance, serve thousands of dashboard users without degradation
- No infrastructure management, fully serverless, auto-scaling, zero ops burden
The Integration Is Seamless
You don't have to rip out MongoDB. The proven architecture pattern:
- Keep MongoDB as your system of record for transactional workloads
- Stream changes to Tinybird via CDC (Change Streams → Debezium → Kafka → Tinybird)
- Query Tinybird for all analytical use cases, dashboards, metrics, user-facing analytics
Data stays synchronized in real-time through scalable real-time streaming data architectures. Your application code barely changes. MongoDB does what it's good at. Tinybird handles analytics.
From Query to Production API in Seconds
This is where Tinybird truly differentiates. Every SQL query you write instantly becomes a production-ready HTTP API endpoint.
- Built-in authentication and rate limiting
- Auto-generated documentation
- Horizontal scaling handled automatically
- Sub-100ms latency guaranteed
For teams building user-facing analytics, embedded dashboards, or real-time product metrics, this eliminates months of backend development. Write a SQL query, publish it as an API, embed it in your product. Done.
Predictable Costs, No Surprises
MongoDB Atlas billing surprises are a common pain point, especially when analytical queries spike I/O costs or require larger clusters.
Tinybird offers predictable base pricing with monthly plans:
- Free tier to start without cost
- Developer plan at $25/month for small workloads
- Scalable Enterprise plans for production deployments
Additional consumption is billed per vCPU. No per-minute compute billing. No I/O-based surprises.
Start in Minutes, Not Months
The fastest way to see if Tinybird solves your MongoDB analytics pain:
- Sign up for the free tier at tinybird.co
- Connect your data source, Kafka, S3, or direct HTTP ingestion
- Write a SQL query against your data
- Publish it as an API with one click
Most teams have their first production API running in under an hour. No infrastructure provisioning. No cluster configuration. No operational burden.
If your MongoDB alternative search is really about analytics performance, Tinybird is the answer.
Frequently Asked Questions (FAQs)
Is there a true drop-in replacement for MongoDB?
No perfect 1:1 replacement exists. DocumentDB and Cosmos DB offer partial compatibility, but always have feature gaps. FerretDB aims for compatibility but is still developing. The safest approach: validate your specific queries and features against compatibility documentation.
Can I use PostgreSQL instead of MongoDB?
Yes, for many use cases. PostgreSQL + JSONB provides document flexibility with SQL power. It works best when you need hybrid relational/document modeling, strong transactions, and ad-hoc queries. It's less ideal for purely document-centric applications at massive scale.
What's the best MongoDB alternative for analytics?
For analytical workloads, Tinybird (built on ClickHouse®) is purpose-built for real-time analytics with sub-100ms latency. Rather than forcing MongoDB to do analytics, ingest data via CDC and query in a columnar OLAP engine designed for aggregations.
How do I migrate data from MongoDB?
Migration approaches depend on your target:
- Compatible services (DocumentDB, Cosmos DB): Use mongodump/mongorestore or native migration tools
- PostgreSQL: Transform documents to relational schema or JSONB
- Analytics platforms (Tinybird): Set up CDC with Change Streams → Debezium → Kafka → Tinybird for continuous sync
Should I replace MongoDB or add a specialized layer?
For most teams, adding a specialized layer reduces risk. Keep MongoDB for what it does well (transactional documents), and add:
- Tinybird for real-time analytics
- Elasticsearch for search
- Cassandra for time-series/events
This pattern uses CDC to keep systems synchronized without replacing your operational database.
Is self-hosted MongoDB cheaper than Atlas?
Infrastructure costs can be 50-70% lower self-hosted. But include operational costs: setup, maintenance, backups, upgrades, on-call, and expertise. For small teams, managed services often have lower total cost of ownership when engineering time is valued properly.