Database Data v1.0.0 Updated 2026-01-29

TimescaleDB

PostgreSQL for time-series data

21.5k N/A Unknown N/A
74 /100
Fair pick Good confidence (85%)

Quick Verdict

Best For

  • Applications needing reliable data storage
  • Teams requiring scalability
  • Projects with complex queries
  • Data-intensive applications

Consider Alternatives If

  • Simple key-value needs
  • Embedded applications
  • Zero-config requirements

Top Alternatives

Score Breakdown

6 dimensions evaluated with transparent methodology

Performance
84 −16

Optimized query execution with efficient storage

  • Fast query execution
  • Efficient indexing
  • Connection pooling
Why not 100%:
  • −8 Performance varies with query complexity
  • −8 Scaling requires configuration
Developer Experience
69 −31

Good tooling and client libraries

  • Multiple client libraries
  • GUI tools available
  • Good error messages
Why not 100%:
  • −15 Learning curve for optimization
  • −15 Advanced features require expertise
Ecosystem
50 −50

Established ecosystem with integrations

  • Wide hosting support
  • ORM compatibility
  • Monitoring tools
Why not 100%:
  • −25 Ecosystem maturity varies
  • −25 Some integrations need setup
Maintainability
79 −21

Mature with stable release cycle

  • LTS versions available
  • Clear upgrade paths
  • Backward compatibility
Why not 100%:
  • −10 Major upgrades need planning
  • −10 Data migration complexity
Cost Efficiency
73 −27

Various pricing options from free to enterprise

  • Self-hosted option
  • Managed services available
  • Predictable pricing
Why not 100%:
  • −13 Storage costs at scale
  • −13 Compute costs for heavy workloads
Compliance
90 −10

Enterprise security features available

  • Encryption at rest
  • Audit logging
  • Access controls
Why not 100%:
  • −5 Some features require enterprise tier
  • −5 Compliance setup required

Compare Alternatives

How TimescaleDB stacks up against similar technologies

TechnologyOverallPerfDXEcosystem
Current TimescaleDB74846950
postgres Full-featured relational85Compare →
ClickHouse Analytics OLAP77Compare →

Sources & Methodology

How we calculate these scores: transparent and reproducible

Deterministic Scoring

Same inputs always produce the same outputs. We use versioned lookup tables, not LLM opinions. Every score is explainable and reproducible.

Learn how it works →
primary

GitHub

Repository activity, stars, contributors, issue resolution time

contextual

Community Signals

Stack Overflow activity, Discord engagement, developer surveys

secondary

OSV Database

Known vulnerabilities, security advisories, CVE tracking

Data version: 1.0.0 Last updated: 2026-01-29 Confidence: 85% Metrics: 26/01/2026

Frequently Asked