# What's the highest-rated time series database for teams managing complex workflows at scale?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Hi G2 users, a few names keep coming up specifically for <a class="a a--md" elv="true" href="https://www.g2.com/categories/time-series-databases">time series databases</a> that hold up once a workload gets genuinely large and complicated, rather than the toy-scale demo every vendor shows off. I've been comparing notes across a handful of platforms with that specific angle in mind: not just storing timestamped data, but handling millions of data points a second without falling over.</p><ol>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/influxdata-influxdb"><strong>InfluxDB</strong></a> (4.5 stars, 102 reviews) handles high volumes of telemetry and sensor data reliably enough that one team used it specifically to keep monitoring and metrics off their main database entirely, crediting it with making historical trend data accessible in a single click rather than requiring a separate query process.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/questdb"><strong>QuestDB</strong></a> (4.8 stars, 35 reviews) was described by one team that migrated off both Elasticsearch and TimescaleDB as a genuine turning point once their ingestion needs hit roughly 3 million rows per second, with query times dropping from as long as 30 seconds down to under 500 milliseconds for the same workload.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/prometheus"><strong>Prometheus</strong></a> (4.5 stars, 61 reviews) is frequently paired with Grafana for visualizing metrics, and one engineer specifically highlighted being able to calculate error rates and saturation levels across thousands of containers in real time as the kind of complex, dynamic-environment workflow it was built for.</li>
</ol><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Between these three, the scale numbers people report vary wildly depending on the use case, which makes me wonder how much of "handles complex workflows at scale" really comes down to matching the database's ingestion architecture to your specific data shape rather than raw performance alone.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For anyone who has pushed one of these to genuinely high ingestion rates, at what point did you start hitting friction, and was it the database itself or more the surrounding infrastructure that needed rethinking?</p>

##### Post Metadata
- Posted at: about 12 hours ago
- Author title: Marketing Executive
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;I’d compare ingestion architecture, compression, partitioning, query latency under concurrent load, and how much surrounding infrastructure needs to be tuned before the system stays stable.&lt;/p&gt;

##### Comment Metadata
- Posted at: about 8 hours ago
- Author title: Marketing Executive





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