Prometheus vs InfluxDB vs Grafana Cloud: A Practical 2025 Comparison
Three tools, three very different assumptions about how you'll operate them. Here's when each wins, and when it really doesn't.
The three operating assumptions
Each tool makes a different bet about how you run metrics. Get the bet right and the tool feels invisible. Get it wrong and you fight the tool for years.
- Prometheus assumes you will run it yourself and that your cardinality is bounded.
- InfluxDB assumes you want high write throughput and don't mind managing a purpose-built TSDB.
- Grafana Cloud assumes you want someone else to run the thing and you will pay per-ingested-sample.
Prometheus: pull, local, bounded
Pull-based scraping is the feature and the footgun. It works beautifully for statically-labelled infrastructure (Kubernetes pods, EC2 instances) and badly for short-lived jobs.
PromQL is excellent for rate/error/duration math. Storage is local TSDB on disk; horizontal scale means federation or a stack like Thanos/Mimir. High cardinality, tens of thousands of unique label combinations, is where Prometheus starts to hurt.
InfluxDB: push, line-protocol, flexible
InfluxDB is a first-class time series database with its own query language (Flux, or SQL in the v3 engine) and line-protocol ingest. It handles higher cardinality than Prometheus and supports multi-tenant setups natively.
The tradeoff is you're running a database. Replication, upgrades, retention tiering, backups, all on you. For teams without a dedicated data-infra person, it's more operational overhead than it looks.
Grafana Cloud: push, managed, metered
Grafana Cloud takes the Prometheus query model (PromQL, Loki for logs, Tempo for traces) and runs it for you. Prometheus-scraped metrics can be remote-written to it, so migration is usually painless.
Cost scales with active series and ingested samples. At small scale it is cheaper than running the stack yourself. At large scale, tens of millions of active series, it gets expensive fast, and you should have a self-hosted-Mimir conversation ready.
Two questions that pick for you
- Do you want to run the database? If no, Grafana Cloud. If yes, keep going.
- Is your cardinality bounded? If yes, Prometheus. If your labels include anything high-cardinality (user IDs, session IDs, request IDs), look hard at InfluxDB or a different architecture that keeps those out of metrics entirely.
Almost every “our metrics stack is on fire” postmortem eventually traces back to a label that shouldn't have been a label.
Every 'our metrics stack is on fire' postmortem eventually traces back to a label that shouldn't have been a label.
Red flags in your current setup
A series count that grew 10x faster than your traffic this year. That is cardinality, not volume. Chase the labels.
A query that takes more than 10 seconds to return on an hourly window. Either the cardinality is out of control or the storage tier is too slow for the question being asked.
A team that treats the metrics backend as sacred. Metrics stacks are swappable in 6-12 months of patient work. Nobody should be architecting around yours as if it is forever.