Recommended Free Tools
Prometheus is the best default for most infrastructure-monitoring teams. It combines metric scraping, multidimensional labels, PromQL, service discovery, local storage and alerting integrations. Add VictoriaMetrics when Prometheus-compatible ingestion must be retained for much longer or consolidated across clusters. Choose InfluxDB for event-like telemetry, TimescaleDB when PostgreSQL and SQL are central, QuestDB for demanding low-latency ingestion, Graphite when an established passive pipeline already works, and OpenTSDB mainly when Hadoop/HBase is already strategic.
There is no neutral, current benchmark that compares all seven under one methodology, so the right choice depends on collection model, cardinality, retention, query needs, operating skills and migration cost.
At-a-glance comparison
| Database | Best fit | Collection and data model | Main trade-off |
|---|---|---|---|
| Prometheus | Infrastructure and Kubernetes metrics | Pull scraping over HTTP; labels; PromQL; local time-series blocks | Long retention and multi-cluster storage usually require an additional compatible backend |
| VictoriaMetrics | Prometheus-compatible long-term storage | Prometheus remote_write plus InfluxDB, OpenTSDB, Graphite, CSV, JSON and native protocols; MetricsQL | Single-node, clustered and enterprise capabilities must be checked separately |
| InfluxDB | Event-oriented telemetry and hosted Influx workflows | Tags, fields, nanosecond timestamps and log-structured storage | Generation, query language, retention and hosted/open-source boundaries need careful validation |
| TimescaleDB | Time-series data inside PostgreSQL | PostgreSQL tables and SQL operations for relational and time-series workloads | Write rate, hypertable design, compression and horizontal scaling are workload-dependent |
| QuestDB | High-ingestion, low-latency specialist workloads | SQL and portable storage | Monitoring integrations, alerting and operational tooling need explicit evaluation |
| Graphite | Existing passive metric pipelines | Dot-separated metric names; Whisper local-disk storage; external collection and alerting | Less expressive dimensions and more surrounding components than a monitoring system such as Prometheus |
| OpenTSDB | Organizations already operating Hadoop/HBase | Tag-based metrics on distributed Hadoop/HBase storage | Introduces Hadoop/HBase complexity and has a less complete query language than Prometheus |
How to evaluate a monitoring time-series database
Collection model
Determine whether agents push samples, the database pulls them, or an existing system writes passively. Prometheus discovers targets and scrapes them, which suits dynamic services. Graphite assumes a passive pipeline. InfluxDB and VictoriaMetrics can accept data through several protocols, useful when different teams already emit different formats.
Labels, tags and cardinality
Monitoring dimensions are valuable only while they remain manageable. Prometheus labels make filtering and aggregation natural, but every unique label combination creates another series. InfluxDB separates tags from fields, while Graphite encodes dimensions in dot-separated names. Before choosing, list dimensions such as service, region, cluster, status and customer, then estimate how many combinations are active and how quickly they change.
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Retention and storage layout
Decide how much data must remain at full resolution, what can be downsampled or deleted, and whether one cluster or many must be queried together. Prometheus is autonomous and local-first; long-term or multi-cluster retention normally adds a compatible storage layer. VictoriaMetrics is designed specifically for this extension. Graphite with clustered storage can remain attractive when historical retention is the dominant requirement.
Queries, alerts and operations
A database that stores samples is not automatically a monitoring system. PromQL, recording rules, service discovery and Alertmanager integrations give Prometheus a complete operational path. Other choices may require separate collectors, rule engines, dashboards and notification services. Include those components when estimating operational complexity and cost.
1. Prometheus: best default for infrastructure metrics
Prometheus is an open-source systems monitoring and alerting toolkit. It stores metrics as time-series data with timestamps and optional key-value labels, using a pull model over HTTP. Its service discovery, PromQL, local storage, recording rules and Alertmanager integrations are especially well suited to dynamic, machine-centric environments such as Kubernetes.
Choose Prometheus when
- You need exporters and integrations for infrastructure and applications.
- Targets appear and disappear dynamically.
- Operators want a mature metric query and alerting workflow in one system.
Watch-outs
Plan remote or long-term storage for multi-cluster history rather than treating one local Prometheus server as an indefinite archive. Prometheus is also not appropriate as a billing ledger when 100% per-request accuracy is mandatory; monitoring samples and billing records have different guarantees.
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2. VictoriaMetrics: best Prometheus-compatible retention layer
VictoriaMetrics is a fast, scalable monitoring database and long-term store for Prometheus. It accepts Prometheus remote_write and can consolidate InfluxDB, OpenTSDB, Graphite, CSV, JSON and native protocols. MetricsQL provides a Prometheus-compatible query path while allowing teams to centralize retention behind their existing collectors.
Choose VictoriaMetrics when
- Prometheus is already the collection standard but retention is growing.
- Several ingestion protocols must land in one backend.
- You want to keep existing dashboards and alert expressions while moving storage.
Watch-outs
Confirm whether the required capability belongs to the single-node, clustered or enterprise offering. Set explicit retention policies and validate migration, backup and restore procedures before moving production history.
3. InfluxDB: best for event-like telemetry and hosted Influx workflows
InfluxDB models telemetry with tags, fields and nanosecond timestamps and uses log-structured storage. It is a natural fit when records resemble events rather than only scraped infrastructure samples, including IoT and real-time analytics workloads. Commercial scaling and clustering options, as well as managed Influx services, can reduce the amount of infrastructure your team operates.
Choose InfluxDB when
- Tags and fields describe your data more naturally than metric names and labels.
- Telegraf or existing Influx tooling is already deployed.
- A hosted Influx workflow is preferable to operating storage yourself.
Watch-outs
Identify the exact InfluxDB generation before designing schemas or migrations. Query language, retention behavior and the boundary between open-source and hosted features differ by offering, so verify them against your target deployment.
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TimescaleDB keeps time-series workloads in the PostgreSQL ecosystem. That makes it useful when monitoring or telemetry must join with relational entities, use SQL tooling and transactions, or share existing PostgreSQL operations. It is positioned for monitoring, IoT, financial analysis and real-time analytics.
Choose TimescaleDB when
- The application needs relational joins and time-series analysis in one platform.
- Your team already operates PostgreSQL and prefers familiar SQL tools.
- Transactional consistency between metadata and measurements is important.
Watch-outs
Benchmark your actual write rate and query mix. Hypertable partitioning, compression, retention jobs and horizontal-scaling requirements can determine whether a design remains practical as data grows.
5. QuestDB: best for demanding low-latency ingestion
QuestDB targets high-ingestion, low-latency workloads with SQL and portable storage. Its selection guidance emphasizes query language, day-to-day operations, data portability and the boundary between open-source, free and commercial licensing.
Choose QuestDB when
- Ingest latency and sustained write rate dominate the decision.
- Engineers need SQL exploration over high-rate streams.
- Portable storage is strategically important.
Watch-outs
QuestDB is a specialist choice rather than a universal monitoring default. Verify exporters, alert evaluation, dashboard integration, retention automation and on-call procedures with the rest of your observability stack.
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6. Graphite: best for established passive metric pipelines
Graphite is a passive time-series database with query and graphing features; collection, discovery and other monitoring concerns are supplied by surrounding components. It uses dot-separated metric names and Whisper local-disk storage. Clustered Graphite can be attractive when long-term historical storage matters more than a richer monitoring control plane.
Choose Graphite when
- A stable Graphite or StatsD estate already meets operational needs.
- Migration risk and retraining cost outweigh the benefits of changing systems.
- Passive collection and historical graphs are the primary requirements.
Watch-outs
Dot-separated names provide a less expressive dimension model than Prometheus labels. Budget for separate discovery, alerting and collection components when mapping a new environment.
7. OpenTSDB: best when Hadoop/HBase is already strategic
OpenTSDB is a distributed time-series database built on Hadoop and HBase. Its tag-based model and horizontal scalability can fit organizations that already operate that platform and need distributed historical storage.
Choose OpenTSDB when
- Hadoop/HBase is an established, supported part of your production platform.
- Distributed historical storage is more important than a modern monitoring control plane.
Watch-outs
Do not introduce Hadoop/HBase solely for a new monitoring deployment unless those operational dependencies are required elsewhere. Its query language is less complete than Prometheus, and the platform adds substantial administration.
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Kubernetes and dynamic services
Start with Prometheus because pull scraping, service discovery, labels, exporters and alerting align directly with dynamic workloads. Add VictoriaMetrics when retention or cross-cluster storage outgrows local operation.
Long-term Prometheus storage
VictoriaMetrics is the clearest candidate in this shortlist. Preserve Prometheus-compatible ingestion and query habits, then validate retention, clustering, backups and licensing for the exact edition you plan to run.
High-cardinality data
Do not select by marketing claims alone. First remove accidental dimensions, separate low-value event attributes from indexed tags or labels, and test realistic series counts. Prometheus, InfluxDB and other systems make different trade-offs in indexing and storage; a workload test is essential.
SQL and relational joins
TimescaleDB is the natural first evaluation when telemetry must be joined with PostgreSQL data or governed with familiar SQL and transaction tooling. QuestDB deserves a parallel test when ingestion latency is the primary constraint.
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Graphite and OpenTSDB can be the most economical choices when migration would replace a functioning pipeline or platform. Include the cost of rewriting collectors, dashboards, alerts and operational runbooks before assuming a newer database is cheaper.
A practical rollout plan
- Inventory signals. Record sample frequency, retention tiers, labels or tags, expected series growth, payload size and query patterns.
- Map the control plane. Identify collectors, service discovery, rule evaluation, dashboards, notification routes and on-call ownership.
- Build a representative test. Replay peak ingestion, cardinality growth, restarts and the longest dashboard queries using production-shaped data.
- Measure operations. Test backup, restore, upgrades, node loss, retention deletion and remote-cluster failure before migration.
- Plan coexistence. Run dual writes or remote storage where possible, compare alert results, and define a rollback point before changing the primary path.
Performance, reliability and cost considerations
- Performance: Separate ingestion tests from query tests. A system that accepts samples quickly may still struggle with wide historical aggregations.
- Reliability: Document what happens during collector, network and storage outages. Prometheus servers are autonomous, which helps local monitoring during an outage; external retention layers add another dependency.
- Cost: Count storage, replicas, egress, managed-service fees, support and operator time. Free or open-source software still has infrastructure and maintenance costs.
- Licensing: Confirm current open-source, free and commercial terms for the edition and deployment model you will actually use.
Troubleshooting selection and migration problems
Alerts differ after moving systems
Check timestamp precision, scrape intervals, missing samples, label-to-tag mapping and rule evaluation windows. Compare raw series before comparing dashboard panels.
Storage grows faster than forecast
Find the dimensions creating new series, reduce accidental uniqueness, shorten high-resolution retention and move older data to the planned long-term tier.
Queries time out
Limit the time range, aggregate earlier, add recording rules or precomputed rollups, and inspect whether a dashboard is issuing many overlapping queries.
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Data disappears after a restart
Verify persistent volumes, retention settings and restore procedures. A local-first deployment without durable storage should never be treated as historical archive.
Migration tooling cannot read the source
Use an officially supported protocol or an intermediate export format, preserve timestamps and dimensions, and run a sample import before scheduling a full backfill.
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Frequently Asked Questions
Is a time-series database suitable for billing records?
Not automatically. Monitoring samples can miss events or be aggregated, so billing systems should use a ledger designed for complete, auditable per-request accuracy.
Should I benchmark all seven databases before deciding?
A focused test of the two or three architectures that match your data model is usually more useful than a synthetic seven-way ranking. Use the same workload, retention policy and failure scenarios.
Can I migrate without replacing every dashboard at once?
Often yes. Keep the existing collector and dashboard path, add a compatible remote store or dual-write path, compare results, and move individual workloads only after validation.
Quick Recap
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