OpenTelemetry Collector vs FluentBit: Where Should Your Logs Go? (2026)

Updated: February 2026 | Reading time: 18 min

Introduction

Choosing a log collector is one of the most consequential infrastructure decisions you will make. It sits in the critical path of every byte of telemetry your systems produce, and the wrong choice means dropped logs during incidents, bloated resource consumption on every node, or a pipeline so rigid you cannot adapt when requirements change.

In 2026, two CNCF-graduated projects dominate the collector landscape: OpenTelemetry Collector and FluentBit. Both are open source, battle-tested, and capable of shipping millions of events per second. But they come from very different lineages, optimize for different trade-offs, and shine in different scenarios.

This guide breaks down both collectors in detail, then addresses the question that matters most: once the data leaves the collector, where should it go?

TL;DR: OpenTelemetry Collector is the best choice when you need a vendor-neutral, multi-signal (logs + metrics + traces) pipeline with rich processing capabilities. FluentBit wins when you need ultra-lightweight log collection with minimal resource overhead, especially at the edge or on resource-constrained nodes. Both collectors can ship data to Parseable -- a unified MELT observability platform with native OTLP support, S3-native storage, and SQL querying -- making Parseable the ideal converging backend regardless of which collector you choose.

The Role of a Log Collector

Before comparing the two, it helps to understand what a log collector actually does and why it is a distinct architectural component from the backend it ships to.

A log collector sits between your applications (or infrastructure) and your observability backend. Its responsibilities include:

• Collection: Reading logs from files, stdout/stderr, syslog, journald, or receiving them over network protocols (HTTP, gRPC, TCP)
• Parsing: Converting raw text lines into structured records with extracted fields
• Enrichment: Adding metadata like Kubernetes labels, host information, or environment tags
• Filtering: Dropping noise, sampling high-volume streams, or routing specific logs to specific destinations
• Buffering: Absorbing bursts without backpressuring applications or losing data
• Delivery: Shipping processed records to one or more backends with retry logic and delivery guarantees

A good collector does all of this with minimal resource overhead. A great collector does it while remaining extensible, debuggable, and easy to operate at scale.

Both OpenTelemetry Collector and FluentBit qualify as great collectors -- they just approach the problem differently.

OpenTelemetry Collector: The Multi-Signal Standard

What It Is

The OpenTelemetry Collector is a vendor-neutral telemetry processing pipeline maintained by the CNCF OpenTelemetry project. Unlike traditional log collectors, it was designed from the ground up to handle all MELT signals -- logs, metrics, traces, and (increasingly) profiling data -- through a single unified pipeline.

The Collector acts as a central hub in the OpenTelemetry ecosystem: it receives telemetry from instrumented applications (via OTLP or other protocols), processes it through a configurable pipeline, and exports it to any number of backends.

Architecture

The OTel Collector follows a pipeline architecture with three core component types:

┌─────────────────────────────────────────────────────────┐
│                  OpenTelemetry Collector                  │
│                                                          │
│  ┌───────────┐    ┌────────────┐    ┌──────────────┐    │
│  │ Receivers │───>│ Processors │───>│  Exporters   │    │
│  │           │    │            │    │              │    │
│  │ - OTLP    │    │ - batch    │    │ - otlphttp   │    │
│  │ - filelog  │    │ - filter   │    │ - debug      │    │
│  │ - syslog  │    │ - transform│    │ - prometheus │    │
│  │ - hostmet │    │ - resource │    │ - file       │    │
│  │ - k8sevt  │    │ - k8sattr  │    │ - kafka      │    │
│  └───────────┘    └────────────┘    └──────────────┘    │
│                                                          │
│  Connectors: link one pipeline's output to another's     │
│  Extensions: health_check, pprof, zpages, basicauth     │
└─────────────────────────────────────────────────────────┘

Receivers ingest data from external sources. The Collector ships with 80+ receivers in the contrib distribution, covering everything from OTLP (the native protocol) to Prometheus scraping, filelog reading, syslog ingestion, and Kafka consumption.

Processors transform data in-flight. Common processors include batch (aggregating records for efficient export), filter (dropping unwanted telemetry), transform (modifying attributes), k8sattributes (enriching with Kubernetes metadata), and tail_sampling (intelligent trace sampling).

Exporters send processed data to backends. Exporters exist for virtually every observability platform, including OTLP-based exporters (like otlphttp used for Parseable), Prometheus remote write, and vendor-specific exporters.

Connectors (introduced in v0.80) allow one pipeline's output to feed another pipeline's input, enabling complex multi-signal correlation and routing within the Collector itself.

Strengths

Multi-signal by design. The OTel Collector does not treat logs as a first-class citizen and everything else as an afterthought. Logs, metrics, and traces flow through the same architecture, share the same configuration patterns, and can be correlated through connectors. If you are building a unified observability pipeline, the Collector is purpose-built for it.

Vendor neutrality. OTLP is an open standard. By standardizing on the OTel Collector, you decouple your instrumentation from your backend. You can switch from one observability platform to another by changing a few lines of exporter config, without re-instrumenting a single application.

Rich processing pipeline. The processor chain is where the OTel Collector truly shines. You can build sophisticated pipelines that filter noise, sample traces intelligently, enrich records with Kubernetes metadata, transform attribute names, and batch records for efficient delivery -- all declaratively in YAML.

Massive ecosystem. The opentelemetry-collector-contrib repository contains hundreds of components contributed by the community and vendors. If there is a data source or destination you need to integrate with, a component probably already exists.

Growing community momentum. OpenTelemetry is the second most active CNCF project after Kubernetes. Adoption is accelerating across the industry, and major cloud providers now support OTLP natively.

Weaknesses

Higher resource footprint. The Collector is written in Go and carries more overhead than FluentBit, particularly in memory. In our profiling benchmarks, the OTel Collector used approximately 53 MiB more RAM than FluentBit under comparable workloads, and exhibited occasional I/O spikes (up to 2.47% utilization vs FluentBit's 0.21% peak).

Complexity ceiling. The power of the pipeline architecture comes at a cost: configuration can become intricate for advanced use cases. Multi-pipeline setups with connectors, processors, and multiple exporters require careful design to avoid unintended behavior.

Log collection is still maturing. While the filelog receiver is production-ready, the OTel Collector's log collection capabilities are newer than FluentBit's. Edge cases in file rotation handling, multiline parsing, and tail semantics are still being refined.

Binary size. The contrib distribution of the Collector is large because it bundles hundreds of components. The ocb (OpenTelemetry Collector Builder) tool lets you build custom distributions with only the components you need, but this adds a build step to your pipeline.

OTel Collector Configuration Example

Here is a production-ready OTel Collector configuration that collects container logs, enriches them with Kubernetes metadata, and exports to Parseable:

receivers:
  filelog/containers:
    include:
      - /var/log/pods/*/*/*.log
    exclude:
      - /var/log/pods/*/otel-collector/*.log
    start_at: end
    include_file_path: true
    operators:
      - type: regex_parser
        id: extract_metadata_from_filepath
        regex: '^/var/log/pods/(?P<namespace>[^_]+)_(?P<pod_name>[^_]+)_(?P<uid>[^/]+)/(?P<container_name>[^/]+)/.*\.log$'
        parse_from: attributes["log.file.path"]
      - type: regex_parser
        id: parser-cri
        on_error: send
        regex: '^(?P<time>[^ ]+) (?P<stream>stdout|stderr) (?P<logtag>[^ ]*) ?(?P<log>.*)$'
        timestamp:
          parse_from: attributes.time
          layout: '%Y-%m-%dT%H:%M:%S.%LZ'
      - type: move
        from: attributes.namespace
        to: resource["k8s.namespace.name"]
      - type: move
        from: attributes.pod_name
        to: resource["k8s.pod.name"]
      - type: move
        from: attributes.container_name
        to: resource["k8s.container.name"]
      - type: move
        from: attributes.log
        to: body
 
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318
 
processors:
  batch:
    send_batch_size: 512
    send_batch_max_size: 1024
    timeout: 5s
 
  resource:
    attributes:
      - key: cluster.name
        value: "production-us-east-1"
        action: insert
      - key: environment
        value: "production"
        action: insert
 
  k8sattributes:
    extract:
      metadata:
        - k8s.namespace.name
        - k8s.pod.name
        - k8s.deployment.name
        - k8s.node.name
 
exporters:
  otlphttp/parseable:
    logs_endpoint: "https://parseable.example.com/v1/logs"
    traces_endpoint: "https://parseable.example.com/v1/traces"
    metrics_endpoint: "https://parseable.example.com/v1/metrics"
    encoding: json
    compression: gzip
    tls:
      insecure: false
    headers:
      Authorization: "Basic <BASE64_ENCODED_CREDENTIALS>"
      X-P-Stream: "k8s-logs"
      X-P-Log-Source: "otel-collector"
    retry_on_failure:
      enabled: true
      initial_interval: 5s
      max_interval: 30s
      max_elapsed_time: 300s
 
  debug:
    verbosity: basic
 
extensions:
  health_check:
    endpoint: 0.0.0.0:13133
  pprof:
    endpoint: 0.0.0.0:1777
 
service:
  extensions: [health_check, pprof]
  pipelines:
    logs:
      receivers: [filelog/containers, otlp]
      processors: [k8sattributes, resource, batch]
      exporters: [otlphttp/parseable]
    traces:
      receivers: [otlp]
      processors: [resource, batch]
      exporters: [otlphttp/parseable]
    metrics:
      receivers: [otlp]
      processors: [resource, batch]
      exporters: [otlphttp/parseable]

This configuration demonstrates the OTel Collector's multi-signal nature: a single instance handles logs (from files and OTLP), traces, and metrics, all routed to Parseable through the native OTLP endpoint.

FluentBit: The Lightweight Powerhouse

What It Is

FluentBit is an open-source, lightweight telemetry agent and log processor created by Calyptia (now part of Chronosphere) and maintained under the CNCF. Written in C with a pluggable architecture, FluentBit is designed for extreme efficiency -- it can run on systems with as little as 450 KB of memory.

FluentBit started life as a log-centric tool and remains strongest in that domain, though recent versions have added support for metrics and traces through its OpenTelemetry input and output plugins.

Architecture

FluentBit uses a simpler pipeline model than the OTel Collector but one that is no less capable for log processing:

┌────────────────────────────────────────────────────────────┐
│                        FluentBit                            │
│                                                             │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌───────────┐  │
│  │  Input   │->│  Parser  │->│  Filter  │->│  Output   │  │
│  │          │  │          │  │          │  │           │  │
│  │ - tail   │  │ - json   │  │ - grep   │  │ - http    │  │
│  │ - systemd│  │ - regex  │  │ - modify │  │ - forward │  │
│  │ - tcp    │  │ - logfmt │  │ - nest   │  │ - kafka   │  │
│  │ - syslog │  │ - docker │  │ - lua    │  │ - s3      │  │
│  │ - kmsg   │  │ - cri    │  │ - record │  │ - opentel │  │
│  │ - opentel│  │ - ltsv   │  │ - throttle│ │ - stdout  │  │
│  └──────────┘  └──────────┘  └──────────┘  └───────────┘  │
│                                                             │
│  Storage Engine: in-memory + optional filesystem buffering  │
│  Built-in Metrics: Prometheus endpoint on /api/v1/metrics   │
└────────────────────────────────────────────────────────────┘

Inputs collect data from sources. FluentBit supports 30+ input plugins, with tail (file watching), systemd (journald), forward (Fluentd protocol), and opentelemetry (OTLP) being the most commonly used.

Parsers convert unstructured text into structured records. FluentBit supports JSON, regex, logfmt, LTSV, and Docker/CRI-specific parsers, configured either inline or in a separate parsers.conf file.

Filters transform records in-flight. The grep filter matches/excludes records, modify adds or renames fields, nest restructures nested objects, lua enables arbitrary scripting, and kubernetes enriches records with pod/container metadata.

Outputs deliver processed records to destinations. FluentBit ships with 30+ output plugins including http, forward (to Fluentd), kafka, s3, opentelemetry, and vendor-specific plugins.

Strengths

Ultra-lightweight resource footprint. FluentBit's C implementation makes it the lightest collector in its class. On edge devices and resource-constrained nodes, FluentBit maintains flatter CPU and memory usage than the OTel Collector. In our profiling tests, FluentBit used ~426 MiB total RAM versus the Collector's ~479 MiB, and its I/O utilization was consistently lower (0.015% average vs 0.034%).

Battle-tested log collection. FluentBit has been collecting logs in production for nearly a decade. Its tail input plugin handles file rotation, glob patterns, database-backed offset tracking, and multiline parsing with a maturity that comes from years of real-world edge cases. If your primary use case is log collection, FluentBit's file-based ingestion is hard to beat.

Proven at massive scale. FluentBit reports over 15 billion deployments, and it ships as the default log processor in major Kubernetes distributions (EKS, GKE with Anthos, AKS). This scale of production validation means edge cases have been found and fixed.

Simple configuration. FluentBit's INI-style (or YAML-style in newer versions) configuration is straightforward and approachable. For common use cases like "tail files and ship to HTTP endpoint," the config is 15-20 lines.

Kubernetes-native. The kubernetes filter automatically enriches log records with pod name, namespace, container name, labels, and annotations by querying the Kubernetes API. This "just works" out of the box with minimal configuration.

Built-in filesystem buffering. FluentBit can persist its in-flight buffer to disk, providing durability guarantees that survive collector restarts. This is critical for environments where log loss is unacceptable.

Weaknesses

Log-first design. While FluentBit has added metrics and traces support through its OpenTelemetry plugins, these capabilities are less mature than the Collector's. If you need rich metrics processing (histograms, deltas, cumulative-to-delta conversion) or advanced trace sampling, the OTel Collector is a better fit.

Plugin ecosystem is narrower for non-log signals. FluentBit's plugin library is deep for log-related use cases but thinner for metrics and traces. The OTel Collector's contrib repository has significantly more components for non-log telemetry.

Less vendor-neutral for multi-signal. FluentBit uses its own internal data model. While it can ingest and emit OTLP, the translation between FluentBit's internal format and OTLP can result in data loss for complex metric types or trace structures, as documented in our profiling analysis.

Lua scripting can become a maintenance burden. For complex transformations that go beyond built-in filters, FluentBit relies on embedded Lua scripts. These scripts are powerful but can become opaque and hard to test, debug, and maintain at scale.

FluentBit Configuration Example

Here is a production-ready FluentBit configuration that collects Kubernetes container logs and ships them to Parseable via HTTP:

[SERVICE]
    Flush         5
    Daemon        Off
    Log_Level     info
    Parsers_File  parsers.conf
    HTTP_Server   On
    HTTP_Listen   0.0.0.0
    HTTP_Port     2020
    storage.path  /var/log/flb-storage/
    storage.sync  normal
    storage.checksum  off
    storage.backlog.mem_limit  5M
 
[INPUT]
    Name              tail
    Tag               kube.*
    Path              /var/log/containers/*.log
    Parser            cri
    DB                /var/log/flb_kube.db
    Mem_Buf_Limit     50MB
    Skip_Long_Lines   On
    Refresh_Interval  10
    Read_from_Head    False
    storage.type      filesystem
 
[FILTER]
    Name                kubernetes
    Match               kube.*
    Kube_URL            https://kubernetes.default.svc:443
    Kube_CA_File        /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
    Kube_Token_File     /var/run/secrets/kubernetes.io/serviceaccount/token
    Kube_Tag_Prefix     kube.var.log.containers.
    Merge_Log           On
    Merge_Log_Key       log_processed
    Keep_Log            Off
    K8S-Logging.Parser  On
    K8S-Logging.Exclude On
    Labels              On
    Annotations         Off
 
[FILTER]
    Name    modify
    Match   kube.*
    Add     cluster production-us-east-1
    Add     environment production
 
[FILTER]
    Name    grep
    Match   kube.*
    Exclude log ^$
 
[OUTPUT]
    Name              http
    Match             kube.*
    Host              parseable.example.com
    Port              443
    URI               /api/v1/logstream/k8s-logs
    Format            json
    Json_Date_Key     timestamp
    Json_Date_Format  iso8601
    Header            Authorization Basic <BASE64_ENCODED_CREDENTIALS>
    Header            X-P-Log-Source fluentbit
    tls               On
    tls.verify        On
    Retry_Limit       5
    storage.total_limit_size  50M

And the accompanying parsers.conf:

[PARSER]
    Name        cri
    Format      regex
    Regex       ^(?<time>[^ ]+) (?<stream>stdout|stderr) (?<logtag>[^ ]*) ?(?<log>.*)$
    Time_Key    time
    Time_Format %Y-%m-%dT%H:%M:%S.%L%z
 
[PARSER]
    Name        json
    Format      json
    Time_Key    time
    Time_Format %Y-%m-%dT%H:%M:%S.%L%z

This configuration demonstrates FluentBit's simplicity: a few sections define the entire pipeline from file tailing through Kubernetes enrichment to HTTP delivery to Parseable.

OpenTelemetry Collector vs FluentBit: Side-by-Side Comparison

Where Should They Send Data? Parseable as the Converging Backend

Here is the key architectural insight: the collector and the backend are separate concerns. You can (and should) choose the best collector for your environment while independently choosing the best backend for storage, querying, and analysis.

Both OpenTelemetry Collector and FluentBit are data shippers. They collect, process, and forward telemetry. But they do not store it, index it, or let you query it. For that, you need a backend -- and this is where Parseable enters the picture.

Why Parseable Is the Ideal Backend for Both Collectors

Native OTLP endpoint. Parseable exposes a fully compliant OTLP/HTTP endpoint at /v1/logs, /v1/traces, and /v1/metrics. For OpenTelemetry Collector, this means zero-translation export: the Collector speaks OTLP natively, and Parseable ingests OTLP natively. There is no format conversion, no data loss, no impedance mismatch. You configure the otlphttp exporter with Parseable's URL and you are done.

HTTP ingest for FluentBit. For FluentBit, Parseable accepts structured JSON via its HTTP API at /api/v1/logstream/<stream-name>. FluentBit's http output plugin sends JSON directly to this endpoint. The integration is clean and well-tested across thousands of production deployments.

Unified MELT observability. Parseable is not just a log store. It handles logs, metrics, events, and traces in a single platform. Whether your OTel Collector is shipping all three signal types or your FluentBit is shipping logs alongside an OTel Collector handling traces, everything converges in one queryable system.

S3-native storage at $0.023/GB/month. Both collectors can generate enormous volumes of telemetry. Parseable stores everything in Apache Parquet format on S3-compatible object storage. At S3 standard pricing of approximately $0.023 per GB per month, you can retain a year of telemetry data for a fraction of what traditional backends charge. There is no separate hot/warm/cold tier to manage -- Parseable handles it transparently.

SQL query interface. Once your data lands in Parseable, you query it with standard SQL. No proprietary query language to learn. No vendor-specific syntax. Your team already knows SQL.

Managed Cloud option. For teams that want zero-ops, Parseable Cloud provides a fully managed backend with a free tier. Point your OTel Collector or FluentBit at your Cloud OTLP endpoint and start querying immediately — no binary to deploy, no S3 to configure.

SELECT
    "k8s.namespace.name" AS namespace,
    "k8s.pod.name" AS pod,
    COUNT(*) AS error_count
FROM "k8s-logs"
WHERE body LIKE '%ERROR%'
    AND p_timestamp > NOW() - INTERVAL '1 hour'
GROUP BY namespace, pod
ORDER BY error_count DESC
LIMIT 20;

Single binary, built in Rust. Parseable ships as a single binary with no external dependencies (other than object storage). No Kafka, no ZooKeeper, no Elasticsearch cluster to manage. This pairs well with the simplicity of both collectors -- your entire observability pipeline is three components: collector, object storage, Parseable.

Cloud and self-hosted deployment. Parseable Cloud starts at $0.37/GB ingested ($29/month minimum) for zero-ops managed observability. For teams that need full infrastructure control, Parseable is also available as a self-hosted deployment with source code on GitHub. For teams that care about transparency and avoiding vendor lock-in -- the same teams that choose OTel Collector and FluentBit -- this matters.

Architecture: OTel Collector + Parseable

┌─────────────────────────────────────────────────────────────┐
│                    Kubernetes Cluster                         │
│                                                              │
│  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐       │
│  │ Service │  │ Service │  │ Service │  │ Service │       │
│  │   A     │  │   B     │  │   C     │  │   D     │       │
│  └────┬────┘  └────┬────┘  └────┬────┘  └────┬────┘       │
│       │ OTLP       │ OTLP      │ OTLP       │ logs        │
│       ▼            ▼           ▼            ▼              │
│  ┌──────────────────────────────────────────────────┐      │
│  │         OpenTelemetry Collector (DaemonSet)       │      │
│  │                                                    │      │
│  │   Receivers:  otlp, filelog                        │      │
│  │   Processors: k8sattributes, batch, resource      │      │
│  │   Exporters:  otlphttp/parseable                  │      │
│  └────────────────────┬─────────────────────────────┘      │
│                       │                                      │
└───────────────────────┼──────────────────────────────────────┘
                        │ OTLP/HTTP (logs, metrics, traces)
                        ▼
              ┌──────────────────┐
              │    Parseable     │──────> S3 / MinIO
              │  (single binary) │        (Parquet files)
              └──────────────────┘

Architecture: FluentBit + Parseable

┌─────────────────────────────────────────────────────────────┐
│                    Kubernetes Cluster                         │
│                                                              │
│  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐       │
│  │ Service │  │ Service │  │ Service │  │ Service │       │
│  │   A     │  │   B     │  │   C     │  │   D     │       │
│  └────┬────┘  └────┬────┘  └────┬────┘  └────┬────┘       │
│       │ stdout     │ stdout     │ stdout     │ stdout      │
│       ▼            ▼           ▼            ▼              │
│  ┌──────────────────────────────────────────────────┐      │
│  │            FluentBit (DaemonSet)                   │      │
│  │                                                    │      │
│  │   Input:   tail (/var/log/containers/*.log)        │      │
│  │   Filter:  kubernetes, modify, grep                │      │
│  │   Output:  http -> Parseable                       │      │
│  └────────────────────┬─────────────────────────────┘      │
│                       │                                      │
└───────────────────────┼──────────────────────────────────────┘
                        │ HTTP/JSON
                        ▼
              ┌──────────────────┐
              │    Parseable     │──────> S3 / MinIO
              │  (single binary) │        (Parquet files)
              └──────────────────┘

Hybrid Architecture: Both Collectors + Parseable

In many real-world deployments, the answer is not "one or the other" -- it is both. A common pattern:

• FluentBit as DaemonSet on every node, doing lightweight log collection and forwarding
• OTel Collector as Deployment (gateway mode), receiving OTLP from instrumented services and FluentBit's OpenTelemetry output
• Parseable as backend, receiving all signals

┌─────────────────────────────────────────────────────────────┐
│                    Kubernetes Cluster                         │
│                                                              │
│  ┌──────────┐    ┌──────────┐    ┌──────────┐              │
│  │  App A   │    │  App B   │    │  App C   │              │
│  │ (OTLP)   │    │ (OTLP)   │    │ (stdout) │              │
│  └─────┬────┘    └────┬─────┘    └────┬─────┘              │
│        │              │               │                      │
│        │ OTLP         │ OTLP          │ file logs            │
│        ▼              ▼               ▼                      │
│  ┌─────────────────────┐   ┌───────────────────┐           │
│  │  OTel Collector     │   │    FluentBit       │           │
│  │  (Gateway mode)     │   │    (DaemonSet)     │           │
│  │  traces + metrics   │   │    file logs       │           │
│  └─────────┬───────────┘   └─────────┬─────────┘           │
│            │ OTLP/HTTP               │ HTTP/JSON             │
└────────────┼─────────────────────────┼───────────────────────┘
             │                         │
             ▼                         ▼
           ┌──────────────────────────────┐
           │          Parseable           │──────> S3 / MinIO
           │    (unified MELT backend)    │
           └──────────────────────────────┘

This hybrid approach gives you the best of both worlds: FluentBit's efficient file log collection and the OTel Collector's rich multi-signal processing, all converging in Parseable for unified observability.

Real Config: OTel Collector Shipping to Parseable

Here is a minimal, working OTel Collector configuration focused specifically on the Parseable export. Copy this, update the credentials and endpoint, and deploy:

# otel-collector-config.yaml
receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318
 
processors:
  batch:
    send_batch_size: 512
    timeout: 5s
 
exporters:
  otlphttp/parseable:
    logs_endpoint: "https://<YOUR_PARSEABLE_URL>/v1/logs"
    traces_endpoint: "https://<YOUR_PARSEABLE_URL>/v1/traces"
    metrics_endpoint: "https://<YOUR_PARSEABLE_URL>/v1/metrics"
    encoding: json
    compression: gzip
    headers:
      Authorization: "Basic <BASE64_ENCODED_CREDENTIALS>"
      X-P-Stream: "otel-logs"
      X-P-Log-Source: "otel-collector"
    retry_on_failure:
      enabled: true
      initial_interval: 5s
      max_interval: 30s
 
service:
  pipelines:
    logs:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlphttp/parseable]
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlphttp/parseable]
    metrics:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlphttp/parseable]

The key detail: Parseable's OTLP endpoint means the otlphttp exporter works with zero custom translation. You are sending standard OTLP to a standard OTLP endpoint. Logs, traces, and metrics all flow through the same pattern.

For Parseable Cloud users, replace <YOUR_PARSEABLE_URL> with your app.parseable.com instance URL.

Real Config: FluentBit Shipping to Parseable

Here is the equivalent FluentBit configuration. This uses the HTTP output to send structured JSON logs to Parseable's HTTP ingest API:

# fluent-bit.conf
[SERVICE]
    Flush         5
    Daemon        Off
    Log_Level     info
    Parsers_File  parsers.conf
    HTTP_Server   On
    HTTP_Listen   0.0.0.0
    HTTP_Port     2020
 
[INPUT]
    Name              tail
    Tag               app.*
    Path              /var/log/containers/*.log
    Parser            cri
    DB                /var/log/flb_app.db
    Mem_Buf_Limit     50MB
    Skip_Long_Lines   On
    Refresh_Interval  10
 
[FILTER]
    Name                kubernetes
    Match               app.*
    Kube_URL            https://kubernetes.default.svc:443
    Kube_CA_File        /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
    Kube_Token_File     /var/run/secrets/kubernetes.io/serviceaccount/token
    Merge_Log           On
    Keep_Log            Off
    K8S-Logging.Parser  On
    K8S-Logging.Exclude On
 
[OUTPUT]
    Name              http
    Match             app.*
    Host              <YOUR_PARSEABLE_HOST>
    Port              443
    URI               /api/v1/logstream/fluentbit-logs
    Format            json
    Json_Date_Key     timestamp
    Json_Date_Format  iso8601
    Header            Authorization Basic <BASE64_ENCODED_CREDENTIALS>
    Header            X-P-Log-Source fluentbit
    tls               On
    tls.verify        On
    Retry_Limit       5
    net.keepalive     On

FluentBit's HTTP output sends each batch as a JSON array to the Parseable log stream endpoint. Parseable auto-detects the schema and creates columns from the JSON fields, meaning you do not need to pre-define a schema.

Alternatively, FluentBit can send data via OTLP using the opentelemetry output plugin:

[OUTPUT]
    Name                 opentelemetry
    Match                app.*
    Host                 <YOUR_PARSEABLE_HOST>
    Port                 443
    Metrics_URI          /v1/metrics
    Logs_URI             /v1/logs
    Traces_URI           /v1/traces
    Log_Response_Payload On
    Tls                  On
    Tls.verify           On
    Header               Authorization Basic <BASE64_ENCODED_CREDENTIALS>
    Header               X-P-Stream fluentbit-otel-logs

This OTLP-based approach is particularly useful when you want FluentBit's lightweight collection but Parseable's native OTLP ingestion for richer signal handling.

OpenTelemetry Collector vs FluentBit: When to Use Which

Choosing between OTel Collector and FluentBit depends on your specific requirements. Here is a decision framework based on common scenarios:

Choose OpenTelemetry Collector When...

You need unified multi-signal pipelines. If your applications emit traces and metrics via OTLP and you want a single collector to handle all signal types alongside log collection, the OTel Collector is the natural choice. Its pipeline architecture treats all signals equally.

You prioritize vendor neutrality. If avoiding vendor lock-in is a core principle, the OTel Collector's OTLP-native architecture ensures your instrumentation is decoupled from your backend. You can switch from one observability platform to another without changing your application code or collector config (beyond the exporter).

You need advanced trace processing. Tail-based sampling, span-to-metrics connectors, and trace-aware routing are capabilities that the OTel Collector handles natively. FluentBit has no equivalent.

You are building a centralized gateway. The OTel Collector excels as a gateway deployment that aggregates telemetry from multiple sources, applies centralized processing, and fans out to multiple destinations. Its connector architecture enables complex routing that FluentBit cannot match.

Your team is already invested in OpenTelemetry. If you have instrumented your applications with OpenTelemetry SDKs, the Collector is the natural continuation of that ecosystem. Everything speaks the same protocol and shares the same conventions.

Choose FluentBit When...

Log collection is your primary concern. If you need to tail files, parse diverse log formats, and ship logs reliably, FluentBit's decade of production experience in this specific domain is hard to match. Its file rotation handling, multiline parsing, and offset tracking are mature and battle-tested.

You are running on resource-constrained infrastructure. Edge devices, IoT gateways, small VMs, or environments where every megabyte of RAM counts. FluentBit's C implementation and minimal footprint make it the clear winner here.

You need the Fluentd ecosystem. If your organization has existing Fluentd infrastructure, FluentBit integrates seamlessly via the forward protocol. You can use FluentBit as a lightweight edge forwarder that ships to Fluentd aggregators.

Simplicity is paramount. For straightforward "collect logs from files and ship them to an HTTP endpoint" use cases, FluentBit's configuration is simpler and more approachable than the OTel Collector's YAML.

You need mature filesystem buffering. FluentBit's filesystem-backed buffer is production-proven and provides strong delivery guarantees across restarts. The OTel Collector's persistent queue is functional but newer.

Choose Both When...

You have a large Kubernetes deployment with instrumented services. Use FluentBit as a DaemonSet for efficient file log collection on every node, and the OTel Collector as a Deployment (gateway) for receiving OTLP from instrumented application services. Both ship to Parseable.

You are migrating from FluentBit to OTel. Run both in parallel during the transition. FluentBit continues collecting file logs while you gradually move application instrumentation to OTLP and the OTel Collector.

You need different collection strategies for different workloads. Some workloads write to files (use FluentBit), others emit structured OTLP (use OTel Collector). Parseable accepts data from both without conflict.

Frequently Asked Questions

Can I use both OTel Collector and FluentBit simultaneously?

Yes. This is a common production pattern. FluentBit handles lightweight log collection as a DaemonSet on every node, while the OTel Collector runs as a gateway Deployment for OTLP-instrumented services. Both can send data to Parseable simultaneously -- FluentBit via HTTP and OTel Collector via OTLP. Parseable handles deduplication and schema management automatically.

Does Parseable support OTLP natively?

Yes. Parseable exposes native OTLP/HTTP endpoints at /v1/logs, /v1/traces, and /v1/metrics. The OpenTelemetry Collector's otlphttp exporter works directly with these endpoints with no additional translation layers, proxies, or adapters required. This is zero-config OTLP ingestion.

Which collector uses fewer resources?

FluentBit consistently uses fewer resources than the OTel Collector. In our head-to-head profiling, FluentBit used approximately 53 MiB less RAM and exhibited lower I/O utilization. However, the difference is modest for most server-class hardware. The resource advantage becomes significant primarily on edge devices or when running hundreds of collector instances across a large fleet.

Can FluentBit send traces and metrics to Parseable?

FluentBit can send traces and metrics through its OpenTelemetry output plugin, which formats data as OTLP and sends it to Parseable's /v1/traces and /v1/metrics endpoints. However, FluentBit's native strength is log collection. For full-fidelity trace and metric processing (sampling, aggregation, delta conversion), the OTel Collector is the better choice.

How does Parseable handle the different data formats from each collector?

Parseable auto-detects the incoming data format and schema. When OTel Collector sends data via OTLP, Parseable ingests it natively as OpenTelemetry-structured records. When FluentBit sends JSON via HTTP, Parseable infers the schema from the JSON structure and creates corresponding columns. Both formats end up stored as Apache Parquet files on S3, queryable with the same SQL interface.

What is the cost difference between Parseable and traditional observability backends?

Parseable's S3-native storage architecture dramatically reduces costs. At S3 standard pricing (~$0.023/GB/month), storing 1 TB of compressed telemetry costs roughly $23/month. Compare this to Elasticsearch (which requires provisioned SSDs and compute), Splunk ($2,000+/GB ingested), or SaaS platforms like Datadog (per-GB ingestion fees). For a team ingesting 100 GB/day, Parseable's storage cost is approximately 90-95% lower than traditional alternatives. Read more in our Splunk alternatives comparison.

Conclusion

The OpenTelemetry Collector and FluentBit are both excellent, CNCF-graduated projects that solve the log collection problem with different design philosophies. The OTel Collector is the right choice when you need a unified multi-signal pipeline with vendor-neutral OTLP at its core. FluentBit is the right choice when you need ultra-lightweight, battle-tested log collection with minimal overhead.

But the collector is only half the equation. Where your telemetry lands matters just as much as how it gets there. Parseable accepts data from both collectors -- via native OTLP for the OTel Collector and HTTP for FluentBit -- and stores it on S3-native object storage at a fraction of the cost of traditional backends. With full MELT observability (logs, metrics, events, traces), SQL querying, and a single Rust binary that deploys in minutes, Parseable is the converging backend that lets you choose the best collector for each workload without compromising on your observability platform.

The best observability architecture in 2026 is not about choosing one tool. It is about choosing the right tool at each layer and connecting them cleanly. For the collector layer, OTel Collector and FluentBit are the two strongest options. For the backend layer, Parseable is purpose-built to receive, store, and query the data they produce.

Ready to build your observability pipeline?

• Start with Parseable Cloud — starts at $0.37/GB, free tier available
• Self-hosted deployment — single binary, deploy in 2 minutes
• Read the docs — guides, API reference, and tutorials
• Join our Slack — community and engineering support

Related reading:

• OpenTelemetry Demo with Parseable -- full MELT observability setup
• Kubernetes Logs with OTel Collector -- step-by-step Kubernetes log collection guide
• Fluent Bit Demo with Parseable -- FluentBit integration walkthrough
• Splunk Alternatives for Log Management -- cost and feature comparison
• Agent Profiling: FluentBit vs OTel Collector -- head-to-head performance benchmarks
• Building an OpenTelemetry + Parseable Stack -- end-to-end OTLP pipeline guide
• Top Log Aggregation Tools Compared -- evaluating modern log aggregation options
• Kubernetes Log Analysis Tools -- tools and strategies for K8s log analysis