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At Comcast we are using vector in production for 4 teams with one team handling close to 8TB of ingest/day using vector. We are currently shipping all our logs to Elasticsearch
Reacted by Zach Sherman, Lee Benson, Ben Johnson, Kirill Fomichev, Bruce Guenter, Slawomir Skowron, Jesse Szwedko, Eliah Rusin, Nobu, Robby Dhillon and 7 more
Reacted by Lee Benson, Ben Johnson, Zach Sherman, Slawomir Skowron, João Trigo Soares, Jesse Szwedko, Eliah Rusin, Nobu, Jeffrey Knight, Pavlos Rontidis and 1 more
At Fundamentei—a site focused on providing Stock market financial information for Brazilian investors—we'll be sending system and container logs to Papertrail/S3.
Reacted by Jesse Szwedko, Ben Johnson, João Vitor Verona Biazibetti, Zach Sherman, elsamhaa-g2-travel and Pavlos Rontidis
Reacted by Jesse Szwedko, Ben Johnson, João Vitor Verona Biazibetti and Zach Sherman
At Riot Games, we use vector to build an observability pipeline for collecting, processing and forwarding (to Datadog) almost of all our company's telemetry. We also love the added benefit of having out of the box monitoring for our observability pipeline itself, and tooling like vector tap.
Reacted by Jesse Szwedko, Pavlos Rontidis, Daniel Hoherd and Benson Fung
As a parser to enrich route53 logs with their resolver endpoints (s3 integration pulling from a ram shared query log)
Intake and processor for audit logs from a couple services that have no other datadog integration (sidecar pod that sends logs to an http listener)
As a generic tcp endpoint for network device logs to pre-process logs into their integration's format (network listener behind an nlb)
The main intake for our kubernetes-based datadog agent logs/metrics/trace to properly tag services (and filter out some unneeded ones) with an NLB behind a vpc endpoint that we share to our eks VPCs.
We can push close to 1M events/sec ingested
Reacted by Benson Fung, Jesse Szwedko and Pavlos Rontidis
At SENEC GmbH we use Vector. SENEC has allowed me to continue my work on the event-processing-framework as I call it from #790 (comment) and also contribute it back as FOSS today. Check it out: https://github.com/ypid/event-processing-framework We plan to keep the framework up-to-date in shorter intervals than almost 4 years ;-)
Reacted by Pavlos Rontidis, Jesse Szwedko and Max Pschonder
At Quad9 we are using it for a large number of tasks inside of our telemetry platform to manage DNS statistics at scale (hundreds of billions per day, with recent architecture to move to >1T per day.) We are aggregating and stripping personal data at the edge of the system in our >260 locations while still getting the required data for our internal needs. We are creating prometheus metrics, using enrichment functions to create summaries of performance, user distributions, and network metrics. We use the dnstap and protobuf sources extensively, and have added many patches for feature enhancement as we have found new uses for Vector (which is almost every week.) It has become the primary input/output tool for our Kafka systems. We replaced 6+ years of custom Go code with Vector in about 2 months, start to finish, and we've expanded the Vector code base with many custom patches and fixes. I've spoken on it at DNS-OARC conference - here's a video that is a bit disjointed, but talks a bit more about our use case.
Reacted by Julien Laurenceau, Dr.
Reacted by Jesse Szwedko, Tshepang Mbambo, Luke Steensen, Nasir Amin, Pavlos Rontidis, Robin Schneider and Jorge Hermo
Reacted by Jesse Szwedko, Nasir Amin, Pavlos Rontidis and Ryan Hendrickson
Kompress.ai uses vector.dev to collect logs (docker_logs, file sources) across its IOT platform and kompress services. Some VRL transform is done during collection, the logs are forwarded to observability service. Use O2 as a self hosted observability service that serves the following requirements: log analytics, dashboards, alerts.
In the future roadmap is to use vector kafka source for metrics that we can use to monitor consumer group latencies and setup alerts.
At Essential Personnel we're using vector to scrape logs (docker_logs) for our customer instances (isolated, per-customer deployments). VRL transformation to standardize the logs. We're going direct to S3 and have automation to analyze log events (e.g. Cloudwatch alarm triggers standardized queries against multiple log sources in S3 and posts to Slack the findings).
We're transitioning to EKS and will be using vector to push these logs as well.
At RELEX we use Vector in the central log pipeline / router to process internal logs and events. It replaced Fluentd with more functions and much better performance (at center the peak could reach 100K logs/sec) on the same hosting spec.
Thanks to the unit testing, input/output support and monitorability, we've been continuously expanding its usage. Now the main pipeline contains nearly a hundred transforms and routes. We also developed a modular configuration with config and env-var templating and per-environment validation. Enrichment by memory tables is the latest addition and challenge.
Reacted by Tshepang Mbambo, Jesse Szwedko, Thomas and Robin Schneider
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Activity
zcapper commentedon Oct 17, 2019
At Checkbox (https://checkbox.ai) we use Vector to ship our system and container logs to S3 and Datadog Logs.
wowi42 commentedon Nov 11, 2019
At Kalvad, we use Vector in production for 3 customers already, sending it to OpenDistro.
Very happy with it
Edit: we switched it to some HTTP logs system based on Elixir and warp10
Edit 2 (2023/04/04): We moved to quickwit with Kafka, it is very stable, and amazingly cheap
raghu999 commentedon Aug 19, 2020
At Comcast we are using vector in production for 4 teams with one team handling close to 8TB of ingest/day using vector. We are currently shipping all our logs to Elasticsearch
suvl commentedon Sep 17, 2020
At NOS we'll be shipping all our logs from home devices to Kafka using Vector. Project's due to get into production next week.
andreyvital commentedon Sep 18, 2020
At Fundamentei—a site focused on providing Stock market financial information for Brazilian investors—we'll be sending system and container logs to Papertrail/S3.
46 remaining items
karuppiah7890 commentedon Aug 16, 2023
We at Togai use Vector in production to collect logs
mr-karan commentedon Aug 17, 2023
We at Zerodha are happy users of Vector. Have written about our setup here: https://zerodha.tech/blog/logging-at-zerodha/
su-shivanshmathur commentedon Jun 13, 2024
We at Hyperswitch are using Vector for a lot of use-cases including pushing logs from Envoy/Squid to logging service , Ingestion pipeline , etc.
ncalibey commentedon Aug 22, 2024
At Timescale we use Vector shipping logs to customer and scraping k8s, cgroup, and storage data!
spnngl commentedon Sep 6, 2024
At Wiremind we use vector for collecting logs on multiple kubernetes clusters.
nzxwang commentedon Sep 11, 2024
At Riot Games, we use vector to build an observability pipeline for collecting, processing and forwarding (to Datadog) almost of all our company's telemetry. We also love the added benefit of having out of the box monitoring for our observability pipeline itself, and tooling like
vector tap.ramseymcgrathfd commentedon Nov 12, 2024
At Fanduel we run Vector:
We can push close to 1M events/sec ingested
ypid-work commentedon Apr 16, 2025
At SENEC GmbH we use Vector. SENEC has allowed me to continue my work on the event-processing-framework as I call it from #790 (comment) and also contribute it back as FOSS today. Check it out: https://github.com/ypid/event-processing-framework We plan to keep the framework up-to-date in shorter intervals than almost 4 years ;-)
hznor commentedon Apr 22, 2025
Using Vector in production, it rocks 👍
johnhtodd commentedon Jun 6, 2025
At Quad9 we are using it for a large number of tasks inside of our telemetry platform to manage DNS statistics at scale (hundreds of billions per day, with recent architecture to move to >1T per day.) We are aggregating and stripping personal data at the edge of the system in our >260 locations while still getting the required data for our internal needs. We are creating prometheus metrics, using enrichment functions to create summaries of performance, user distributions, and network metrics. We use the dnstap and protobuf sources extensively, and have added many patches for feature enhancement as we have found new uses for Vector (which is almost every week.) It has become the primary input/output tool for our Kafka systems. We replaced 6+ years of custom Go code with Vector in about 2 months, start to finish, and we've expanded the Vector code base with many custom patches and fixes. I've spoken on it at DNS-OARC conference - here's a video that is a bit disjointed, but talks a bit more about our use case.
louis030195 commentedon Sep 25, 2025
Trying to use vector in production at mediar.ai / https://github.com/mediar-ai/terminator
to monitor our Windows computer use agents across our clusters
#23846
(also was exploring it in 2019 to monitor worldwide datacenters @ovh)
sudhirbits commentedon Oct 5, 2025
Kompress.ai uses vector.dev to collect logs (docker_logs, file sources) across its IOT platform and kompress services. Some VRL transform is done during collection, the logs are forwarded to observability service. Use O2 as a self hosted observability service that serves the following requirements: log analytics, dashboards, alerts.
In the future roadmap is to use vector kafka source for metrics that we can use to monitor consumer group latencies and setup alerts.
watford-ep commentedon Nov 20, 2025
At Essential Personnel we're using vector to scrape logs (docker_logs) for our customer instances (isolated, per-customer deployments). VRL transformation to standardize the logs. We're going direct to S3 and have automation to analyze log events (e.g. Cloudwatch alarm triggers standardized queries against multiple log sources in S3 and posts to Slack the findings).
We're transitioning to EKS and will be using vector to push these logs as well.
jiping-s commentedon Jan 20, 2026
At RELEX we use Vector in the central log pipeline / router to process internal logs and events. It replaced Fluentd with more functions and much better performance (at center the peak could reach 100K logs/sec) on the same hosting spec.
Thanks to the unit testing, input/output support and monitorability, we've been continuously expanding its usage. Now the main pipeline contains nearly a hundred transforms and routes. We also developed a modular configuration with config and env-var templating and per-environment validation. Enrichment by memory tables is the latest addition and challenge.