From 5 Database Providers to 1: Why We Built a Multi-Engine Platform

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.

The Real Cost of Multi-Provider Sprawl

The sticker price of a managed database is the easy part. The hidden costs are operational.

Context switching. Your on-call engineer gets paged about a slow query on PostgreSQL. They log into Provider A. Then they get an alert about a Kafka consumer lag spike. That is Provider D, different credentials, different dashboard, different mental model for what the metrics mean. Every provider switch costs 5-15 minutes of reorientation.

Inconsistent tooling. Provider A uses Terraform for infrastructure-as-code. Provider B has a CLI that works differently. Provider C has a REST API with different authentication. Your automation scripts are a patchwork of provider-specific integrations, each with its own failure modes.

Billing complexity. Five invoices, five pricing models, five ways of calculating storage and compute. Some charge per hour, some per minute, some per GB-month. Forecasting database costs across providers requires a spreadsheet that nobody maintains accurately.

Alerting gaps. Each provider has its own monitoring. Your PostgreSQL provider sends alerts to PagerDuty. Your Kafka provider sends to Opsgenie. Your Redis provider emails someone who left the company six months ago. Unified observability requires stitching together multiple metric sources, and the seams always show.

Access control fragmentation. Five providers means five IAM systems. Onboarding a new engineer requires creating accounts on five platforms. Offboarding requires revoking access across five platforms. Audit trails live in five different places.

One Platform, Five Engines

FoundryDB supports PostgreSQL, MySQL, MongoDB, Valkey, and Kafka. Every engine gets the same operational treatment: same API, same CLI, same dashboard, same monitoring, same backup system, same HA model.

Here is what that looks like in practice.

Single CLI for Everything

# Create a PostgreSQL service
fdb services create --name app-db --engine postgresql --version 17 \
  --plan tier-4 --storage 100 --zone eu-helsinki

# Create a MongoDB service
fdb services create --name content-store --engine mongodb --version 7 \
  --plan tier-3 --storage 50 --zone eu-helsinki

# Create a Valkey cache
fdb services create --name session-cache --engine valkey --version 8.1 \
  --plan tier-2 --storage 20 --zone eu-helsinki

# Create a Kafka cluster
fdb services create --name events --engine kafka --version 3.9 \
  --plan tier-4 --storage 200 --zone eu-helsinki

# List all services, regardless of engine
fdb services list

Same command structure, same flags, same output format. The --engine flag is the only difference.

Unified Dashboard

One login. One view of all your services across all engines. CPU usage, memory, connections, storage, replication lag, backup status. All in the same place, with the same visual language.

No more bookmarking five different provider dashboards. No more remembering which provider uses "instances" vs "clusters" vs "deployments."

One API

Every operation goes through the same REST API:

# All these use the same base URL, same auth, same response format
curl -u $API_KEY: https://api.foundrydb.com/v1/services              # List all
curl -u $API_KEY: https://api.foundrydb.com/v1/services/$PG_ID       # PostgreSQL details
curl -u $API_KEY: https://api.foundrydb.com/v1/services/$MONGO_ID    # MongoDB details
curl -u $API_KEY: https://api.foundrydb.com/v1/services/$KAFKA_ID    # Kafka details

# Backups: same endpoint pattern for all engines
curl -u $API_KEY: https://api.foundrydb.com/v1/services/$PG_ID/backups
curl -u $API_KEY: https://api.foundrydb.com/v1/services/$MONGO_ID/backups

# Users: same endpoint pattern for all engines
curl -u $API_KEY: https://api.foundrydb.com/v1/services/$PG_ID/users
curl -u $API_KEY: https://api.foundrydb.com/v1/services/$VALKEY_ID/users

Your Terraform modules, CI/CD pipelines, and automation scripts use one provider plugin, one authentication method, one response schema. When you add a new database engine to your stack, your existing tooling already supports it.

Consistent HA and Failover

High availability works the same way across engines. Add read replicas, configure automatic failover, and monitor replication lag using the same API and dashboard regardless of whether it is PostgreSQL streaming replication, MySQL GTID replication, MongoDB replica sets, or Valkey Sentinel.

# Add a read replica to PostgreSQL
fdb services add-replica --service app-db --zone eu-helsinki

# Add a read replica to MySQL (same command, same flags)
fdb services add-replica --service legacy-mysql --zone eu-helsinki

Unified Monitoring and Alerting

FoundryDB exports metrics to 7 destinations: Prometheus, Datadog, Grafana Cloud, New Relic, Elastic, InfluxDB, and our built-in dashboard. You configure the export once at the organization level, and every service (regardless of engine) sends metrics to the same place.

Alert rules follow the same pattern. A "high CPU" alert on PostgreSQL and a "high CPU" alert on Kafka use the same rule definition, the same notification channels, the same escalation policies.

# Create an alert rule that applies to any service
fdb alerts create \
  --name "High CPU" \
  --metric cpu_usage_percent \
  --threshold 85 \
  --duration 5m \
  --notify slack:#database-alerts

Multi-Engine Stacks

Many applications need multiple engines working together. FoundryDB makes it easy to deploy coordinated stacks.

RAG Pipeline: PostgreSQL (pgvector) + Kafka (ingestion) + Valkey (cache). Deploy all three in one zone, connected over the private network.

E-commerce: PostgreSQL (orders, inventory) + MongoDB (product catalog) + Valkey (session store, cart cache).

Event-driven microservices: Kafka (event bus) + PostgreSQL (per-service databases) + Valkey (distributed locks, rate limiting).

Analytics: PostgreSQL (OLTP source) + Kafka (CDC streaming) + MongoDB (denormalized read models).

Because all services share the same FoundryDB network within a zone, inter-service communication uses private IPs with sub-millisecond latency. No public internet traversal, no additional firewall rules.

The Honest Tradeoff

We believe in being transparent about limitations. FoundryDB runs on UpCloud infrastructure. We are not a multi-cloud platform. If your requirement is to run PostgreSQL on AWS in us-east-1 and MongoDB on GCP in europe-west1, FoundryDB is not the right fit.

What we offer instead: a focused, well-integrated platform on high-quality European infrastructure. Every engine gets first-class support because we are not spreading across three cloud providers. Our PostgreSQL on UpCloud MaxIOPS storage competes with (and often beats) equivalent offerings on hyperscaler providers in throughput and latency benchmarks.

If your priority is reducing operational complexity, consolidating billing, and running on EU infrastructure, the single-provider tradeoff is usually worth it. If your priority is multi-cloud or specific cloud-native integrations (Lambda triggers on DynamoDB, BigQuery federated queries), a hyperscaler-native offering is a better fit.

What Consolidation Looks Like

Here is a before-and-after for a typical mid-size SaaS company:

Before	After
5 provider dashboards	1 FoundryDB dashboard
5 CLI tools	1 fdb CLI
5 Terraform providers	1 Terraform provider
5 billing invoices	1 invoice
5 IAM systems	1 API key system
5 monitoring integrations	1 metric export config
5 backup policies	1 unified backup system
~3 hours/week on provider context-switching	0

The migration path is straightforward: create services on FoundryDB, migrate data using standard tools (pg_dump/pg_restore, mongodump/mongorestore, Kafka MirrorMaker), switch your connection strings, and decommission the old providers one at a time.

Get Started

Deploy a multi-engine stack on FoundryDB today. The quick start guide walks through creating your first service, and you can have all five engines running within minutes. Each engine page (PostgreSQL, MySQL, MongoDB, Valkey, Kafka) covers engine-specific features and configuration.