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Loki vs Seq: What are the differences?

  1. Key Difference 1: Storage Architecture - Loki uses a log-based storage architecture where logs are stored as streams of events in an append-only manner. On the other hand, Seq uses a time-series storage architecture where events are stored in a time-ordered sequence, allowing for efficient retrieval based on time ranges.

  2. Key Difference 2: Querying Capability - Loki offers log-based query language and allows users to query logs using labels, log lines, and time ranges. It provides powerful filtering and aggregation capabilities specifically designed for log analysis. Whereas, Seq offers a flexible query language allowing users to query structured log events or metrics. It supports SQL-like syntax and enables filtering, aggregating, and transforming log data.

  3. Key Difference 3: Scalability - Loki is highly scalable due to its use of a distributed storage system like object storage. It can handle massive amounts of log data and can be horizontally scaled to meet growing needs. Conversely, Seq is not primarily designed for massive scalability and is more suitable for smaller-scale log storage and analysis requirements.

  4. Key Difference 4: Data Retention - Loki has a built-in retention mechanism that allows users to define how long log data should be retained based on time or size. This feature helps in managing storage costs and compliance requirements. In contrast, Seq does not have built-in data retention capabilities and relies on external processes or scripts for managing data retention.

  5. Key Difference 5: Integration with Logging Libraries - Loki integrates well with popular logging libraries like Promtail, Fluentd, and Logstash, making it easy to ingest logs into the system. In contrast, Seq integrates with various logging libraries but is mainly focused on ASP.NET Core logging and Serilog.

  6. Key Difference 6: Cost Model - Loki follows a cost-effective model where users only pay for the storage used. Its ability to compress and store logs efficiently contributes to cost optimization. On the other hand, Seq follows a per-instance pricing model and requires licensing based on the number of Seq instances deployed.

In summary, Loki and Seq differ in their storage architecture, querying capability, scalability, data retention, integration with logging libraries, and cost model. Loki excels in log-based storage and analysis at scale, while Seq focuses more on structured log events and metrics with an emphasis on ASP.NET Core logging.

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Pros of Loki
Pros of Seq
  • 5
  • 3
    Very fast ingestion
  • 3
    Near real-time search
  • 2
    Low resource footprint
  • 2
    REST Api
  • 1
    Smart way of tagging
  • 1
    Perfect fit for k8s
  • 5
    Easy to install and configure
  • 5
    Easy to use
  • 3
    Flexible query language
  • 2
    Free unlimited one-person version
  • 2
    Beautiful charts and dashboards
  • 2
    Extensive plug-ins and integrations

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Cons of Loki
Cons of Seq
    Be the first to leave a con
    • 1
      It is not free

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    - No public GitHub repository available -

    What is Loki?

    Loki is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by Prometheus. It is designed to be very cost effective and easy to operate, as it does not index the contents of the logs, but rather a set of labels for each log stream.

    What is Seq?

    Seq is a self-hosted server for structured log search, analysis, and alerting. It can be hosted on Windows or Linux/Docker, and has integrations for most popular structured logging libraries.

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    What companies use Loki?
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    What tools integrate with Loki?
    What tools integrate with Seq?

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    What are some alternatives to Loki and Seq?
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    It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.
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    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.
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