“Postgres is a very viable option”
- MongoDB CEO, Q1 FY2026 earnings call.
Postgres wins for time-series at scale
TigerData (from the creators of TimescaleDB) brings full Postgres to high-volume time-series without NoSQL pain or schemaless technical debt. MongoDB NoSQL is an architectural dead-end. Postgres is the future.
Why Not MongoDB
MongoDB is easy to start. But it breaks down fast for time-series at scale. As data grows, developers hit architectural limits that lead to rework, rising costs, and missed SLAs.
No native joins → fragile, inefficient pipelines
MongoDB requires nested aggregations or client-side joins across collections. It’s complex, brittle, and slows down quickly as query logic scales.
Schema-less by default → inconsistent data, painful rewrites
The lack of enforced schema leads to sprawl and drift. Refactoring a data model across collections becomes a massive manual effort.
Limited compression → bloated storage, slow queries
MongoDB stores uncompressed documents. One TigerData customer saw a 7x smaller storage footprint with compression after migrating.
Write path bottlenecks → ingest falls apart under load
MongoDB struggles with sustained high-ingest workloads. One user reduced query latency from 30s to sub-second after switching to TigerData.
No scale efficiency → infrastructure costs balloon
More data means more shards, replicas, and compute. One enterprise customer cut costs from $100K to $55K for 10K sensors after migrating.
What starts as agility turns into tech debt, scaling pain, and missed SLAs.
Why Developers Choose TigerData
TigerData is purpose-built for time-series data at scale.
Developed by the team behind TimescaleDB, it gives you everything Postgres—plus time-series power, without sharding or trade-offs.
- 100% Postgres — composable, relational, and proven
- Hypertables designed for high-ingest time-series workloads
- Real-time joins across streams, lakes, and warehouses
- Up to 54x faster queries vs. MongoDB
- 95% average compression with hybrid row/columnar storage
- Built-in materialized views and strong consistency
MongoDB vs TigerData (creators of TimescaleDB)
On Real-Time Analytic Queries within RTABench, TimescaleDB is 24x times faster than MongoDB.
“With TigerData, our devs already knew how to use it — they knew SQL.”
Edeva
MongoDB vs TigerData for Time-Series
TigerData makes Postgres powerful with fast and affordable database features and worry-free cloud services.
Query Language
Uses proprietary MQL
Steep learning curve
Ingestion at Scale
125KB/1000 doc bucket limits
Backfills create fragmentation
Ingestion latency beyond 10K records/second
Data Relationships
No built-in joins, relationships managed at the application level
Eventual consistency in distributed setups
Schema-less design for unstructured data, schema enforced on read
Analytics & Rollups
No automated materialized views
Manual pipeline maintenance
Large analytics jobs are resource-intensive and slowdown concurrent operations
Storage Efficiency
~70% compression
Cost at Scale
Scaling requires expensive clusters, mandatory 3-node replica sets
Expensive sharding
Millions of signals per second. 7x storage savings. 10–40x faster queries.
Get Started with Tiger Cloud
A mature, reliable, and worry-free
Postgres cloud for time-series data.
Built for developers. Battle-tested at scale.
Full SQL compatibility
No sharding required
Compression, joins, and materialized views out of the box