MongoDB alternatives: a data-backed comparison
Explore the most widely used database tools to find the MongoDB alternatives that fit your specific needs.
Best MongoDB alternatives in 2025
Supabase
Best for: Companies are planning to scale their technical infrastructure over time without the overhead of traditional enterprise solutions.
- Allows client apps to react to data changes instantly
- Includes built-in authentication and authorization
- Offers file storage with edge-based CDN delivery
- Auto-generated APIs and client libraries speed up development
- Open-source foundation allows teams to self-host and extend the platform
- Built-in row-level security and policies simplify permission management
- Relatively new and may lack enterprise-grade SLAs
- Less flexibility if your data model requires a non-relational store
- Scaling large clusters may require manual tuning and careful indexing
- Some advanced analytics and caching capabilities are limited, requiring external services
BrowserStack
Best for: Micro development teams who need comprehensive cross-browser testing capabilities without enterprise-level complexity.
- Provides real-device and cross-browser testing without maintaining internal labs
- Supports both manual and automated testing via Selenium, Appium, and Playwright
- Integrates with CI/CD tools for automated test execution
- Includes debugging tools like video recordings, logs, and screenshots
- Enables local testing of dev and staging environments
- Limited testing minutes in lower-tier plans
- High concurrency usage may require enterprise-level subscriptions
- Device availability can vary during peak usage times
- Desktop browser testing lacks deep customization options
- Native app testing may require more setup compared to emulators/simulators
CircleCI
Best for: Small and medium-sized development teams who need automated CI/CD pipelines without complex enterprise setup requirements.
- Automated parallel builds and tests that catch failures immediately
- Flexible YAML-based configuration for defining complex pipelines
- First-class Docker and VM support ensures consistent build environments
- Built-in caching reduces build times and speeds up iterations
- Detailed dashboards surface pipeline performance metrics and failure trends
- Usage-based pricing scales to match team size and usage patterns
- Requires deeper configuration knowledge for optimal performance
- Can become expensive for teams with very high concurrency needs
- Steeper learning curve compared to simpler CI/CD solutions
- Limited out-of-the-box GUI for pipeline creation, relying heavily on YAML
- Less suitable for teams looking for an all-in-one code hosting and CI/CD platform
Postman
Best for: Development teams and API-focused companies who need comprehensive tools for testing, documenting, and collaborating on API development.
- Robust all-in-one platform for API design, testing, and monitoring, streamlining the full API lifecycle
- Strong collaboration features with team workspaces, ideal for coordinated development
- Supports multiple protocols, including REST, GraphQL, and gRPC, for flexibility across projects
- Auto-generates documentation and mock servers to speed up development and testing
- Access to a large public API network makes it easy to discover and reuse existing APIs
- Doesn’t offer built-in database or authentication tools, requiring external integrations
- Many advanced features, especially for teams, are behind a paywall
- It might be overkill for smaller projects or basic use cases
- Less accessible for non-technical users or teams working in no-code environments
Gitlab
Best for: Micro development teams who need comprehensive DevOps capabilities without enterprise-level complexity.
- Unified interface for Git repos, CI/CD pipelines, issue tracking, and container registry
- Auto DevOps detects project type and configures pipelines
- Built-in security scanning and compliance tools
- Built-in package and container registry keeps artifacts close to code and enforces access control
- Value stream analytics and pipeline dashboards show cycle times and highlight bottlenecks
- Fine-grained permissions and group-level management
- Self-hosted and SaaS options are available
- The feature set can overwhelm teams that only need basic source control or CI/CD
- Auto DevOps may require customization to fit edge-case workflows
- Self-managed installations demand resources for maintenance, upgrades, and high availability
- Some advanced features require higher-tier plans, increasing costs
- Performance can be affected without careful runner and database tuning
Criteria for evaluating MongoDB alternatives
When evaluating MongoDB alternatives, focusing on key factors will determine the tool's effectiveness for your team. The most critical evaluation criteria can be weighted as follows.
Core functionality
When evaluating MongoDB alternatives, teams should focus on how well the database supports core backend needs, such as data modeling, querying, indexing, and scaling. Flexibility in handling structured, semi-structured, or unstructured data is key, depending on your use case. Alternatives should also support consistent performance under load, data consistency models (like ACID compliance), and efficient storage handling.
Teams managing user data, transactions, or analytics workflows need alternatives that offer strong querying, schema support, and real-time capabilities. Consider whether the tool allows for multi-region deployment, high availability, and backup options.
User experience and support
Ease of use varies widely across database platforms. Teams should evaluate how quickly developers can get up and running through clear documentation, guided onboarding, or a user-friendly dashboard. Tools that offer hosted services often reduce setup friction. Consider whether the platform supports popular programming languages, has an active community, and offers responsive customer service.
Built-in tutorials, code examples, and CLI or GUI tooling also improve the learning curve. For teams with limited DevOps resources, strong support and predictable operations are especially crucial when replacing a foundational component like MongoDB.
Integration capabilities
Databases rarely operate in isolation. Look for alternatives that offer strong integration options, including both native and third-party options. API access should be well-documented, flexible, and secure, enabling seamless connections to your application backend, analytics tools, or DevOps workflows.
Built-in integrations with popular platforms (like AWS, Vercel, or CI/CD tools) can reduce time spent on infrastructure. The depth of sync matters too; real-time subscriptions, webhooks, and change data capture can enhance responsiveness and simplify the architecture. A good alternative should seamlessly integrate into your existing stack without requiring extensive custom development.
Value for money
Cost is a key factor, especially as data volume and traffic scale increase. Look at the pricing structure; does it charge by reads/writes, storage, active users, or compute time? Compare feature availability across tiers to avoid being locked into expensive plans for basic functionality.
Some tools offer generous free tiers or flat-rate pricing, which can be more predictable for growing teams. Also consider hidden costs, such as infrastructure setup, monitoring, support, or third-party tools required to fill any gaps. The total cost of ownership should include not only the platform fee but also the time spent managing and integrating it.
Industry-specific requirements
Some teams require more than generic database features; they often rely on tools specifically tailored to their industry. For example, healthcare or fintech apps typically require compliance with regulations such as HIPAA or PCI, which not all platforms support. Others may require built-in data validation, audit logging, or support for complex workflows, such as multi-user document editing or geospatial queries.
Industry-specific templates, SDKs, or deployment options can also speed up development. When evaluating MongoDB alternatives, teams should consider whether a tool provides features that directly support their domain-specific needs, especially in a regulated or high-risk environment.
How to choose the correct alternative
You can use the following approach when selecting the best-suited tool for your team.
Assess your team's requirements
- Define your primary data model (SQL, NoSQL, or hybrid)
- Identify performance and scalability needs
- Map out integration points with your current stack
- Consider internal expertise with specific technologies
- Evaluate compliance or industry-specific needs
Test drive before committing
- Set up a proof of concept or sandbox environment
- Run real workloads or mimic production scenarios
- Involve developers and ops teams in testing
- Track issues, performance, and feature gaps
- Gather feedback from all stakeholders
Evaluate long-term fit
- Check the vendor’s product roadmap and release history
- Assess scalability for data volume and user growth
- Look into support for multi-region or edge deployments
- Review update cycles, maintenance needs, and SLAs
- Consider data portability if you need to migrate later
Consider support and training resources
- Review the available onboarding and documentation
- Check for customer success programs or SLAs
- Look for official tutorials, SDKs, and integrations
- Explore active forums, community support, or Discord/Slack channels
- Evaluate access to certified partners or consultants if needed