6 posts tagged with "mongodb"

Spinning up a document database you can actually look at usually means two jobs. Provision MongoDB. Then, because the shell is not where you want to browse a collection at 2am, install a GUI, point it at the database, paste in a connection string, and remember to turn on TLS. The store you wanted was always one round of plumbing away from being usable.

The Launch a document store stack collapses that into one button. You get a managed MongoDB running on your own infrastructure, EU-resident, with a Mongo Express admin console wired on top, in minutes, with nothing to connect by hand.

DigitalOcean Managed Databases is a popular starting point for teams that want hosted PostgreSQL, MySQL, MongoDB, or Valkey without managing infrastructure. It does the basics well: provisioning, automated backups, and TLS. But as your data platform matures, the limitations become clear. Four engines, minimal monitoring, no AI-oriented features, no pipeline templates, and no way to export metrics to your own observability stack. FoundryDB offers seven engines, built-in AI presets, predictive autoscaling, database forking, and seven metrics export destinations, all on European infrastructure.

Most database dashboards give you a SQL editor and wish you luck. That works fine when you remember the exact name of the pg_stat_user_tables view or the difference between information_schema.TABLES in MySQL versus PostgreSQL. For everyone else, there is the AI Query Console.

FoundryDB's AI Query Console lets you type a question in plain English, translates it to a database-native SQL query using Claude, and executes it against your running service. The entire flow is read-only by design. You cannot accidentally drop a table by asking a question.

Every managed database service on FoundryDB gets automated backups from the moment it starts running. There is nothing to configure, no S3 bucket to provision, and no cron job to maintain. Backups run daily, are encrypted before they leave the server, and are stored in object storage across a separate availability zone.

But automated daily backups are only half the story. For PostgreSQL, MySQL, and MongoDB, FoundryDB continuously archives write-ahead logs (WAL), binary logs (binlog), or oplogs to enable point-in-time recovery (PITR). This means you can restore to any specific second within a 7-day window, not just to the last daily snapshot.

This post covers how backups work across all engines, how to trigger and restore them, and how to use PITR to recover from the kind of mistakes that daily snapshots cannot fix.

If you run a modern application stack, you probably use at least three different database engines. PostgreSQL for your application data. MongoDB or another document store for unstructured content. Valkey (or Redis) for caching and session storage. Kafka for event streaming. Maybe MySQL for a legacy service that nobody wants to migrate.

Each engine runs on a different managed provider. Each provider has its own dashboard, its own CLI, its own billing, its own alerting system, its own way of handling backups, its own access control model. You pay five bills, manage five sets of credentials, and context-switch between five different interfaces when something goes wrong at 2 AM.

We built FoundryDB to solve this problem: one platform for all your database engines, with a single API, a single dashboard, and a single bill.

MongoDB is one of the most popular document databases in the world, but running it in production comes with real operational overhead: replica set initialization, oplog sizing, backup orchestration, WiredTiger cache tuning, and disaster recovery planning. FoundryDB handles all of this out of the box, so you can focus on your application logic.

This post walks through provisioning a MongoDB service, running aggregation pipelines against it, and recovering from data loss using oplog-based point-in-time recovery.