Updated: February 2026 | Reading time: 18 min

Introduction

OpenTelemetry has won. In 2026, it is the de facto standard for instrumenting applications and collecting telemetry across every major language, framework, and cloud provider. The question is no longer "should we adopt OpenTelemetry?" but rather "where should we send all this telemetry?"

Most teams answer that question by stitching together three or four backends: Jaeger for traces, Prometheus for metrics, Elasticsearch or Loki for logs, and Grafana to tie them together. That is a lot of infrastructure to deploy, scale, secure, and pay for. It does not have to be this complicated.

Parseable is a unified observability platform that accepts all OpenTelemetry signal types — logs, metrics, traces, and events — through a native OTLP endpoint, stores everything as Apache Parquet on S3-compatible object storage, and lets you query it all with SQL. One binary. One backend. One query language. Storage costs starting at $0.023/GB/month. Available as open-source self-hosted or fully managed cloud.

This guide walks you through the complete architecture, real configuration files, and production deployment patterns for running OpenTelemetry with Parseable as your single observability backend.

Who this is for: Platform engineers, SREs, and DevOps teams who are adopting or already running OpenTelemetry and want a cost-effective, operationally simple backend for all their telemetry. You should be comfortable with YAML, Kubernetes, and Docker Compose.

Why OpenTelemetry Is the Future of Observability

OpenTelemetry (OTel) is a CNCF graduated project that provides a unified set of APIs, SDKs, and tools for generating, collecting, and exporting telemetry data. Here is why it matters:

Vendor Neutrality

OTel decouples instrumentation from backends. Your application emits telemetry in a standard format (OTLP), and your collector routes it to whatever backend you choose. Switch backends without touching application code.

Full Signal Coverage

OTel covers all four pillars of observability:

Logs: Structured and unstructured log records
Metrics: Counters, gauges, histograms
Traces: Distributed traces with spans and context propagation
Events: Semantic events built on top of logs (emerging specification)

Ecosystem and Adoption

As of 2026, OpenTelemetry has:

Auto-instrumentation for 15+ languages
200+ collector receivers, processors, and exporters
Native support in AWS, GCP, Azure, and every major observability vendor
The second-most active CNCF project after Kubernetes

The problem is that OTel gives you a fire hose of telemetry. Without the right backend, you drown in data and infrastructure complexity.

Architecture: OTel SDK to S3 in Four Hops

The Parseable + OpenTelemetry architecture is intentionally simple:

┌────────────────────────────────────────────────────────────────────┐
│                        Your Applications                           │
│                                                                    │
│  ┌──────────┐   ┌──────────┐   ┌──────────┐   ┌──────────┐       │
│  │ Service A │   │ Service B │   │ Service C │   │ Service D │      │
│  │ (OTel SDK)│   │ (OTel SDK)│   │ (OTel SDK)│   │ (OTel SDK)│     │
│  └─────┬─────┘  └─────┬─────┘  └─────┬─────┘  └─────┬─────┘     │
│        │              │              │              │              │
│        └──────────────┼──────────────┼──────────────┘              │
│                       │              │                             │
│                       ▼              ▼                             │
│              ┌────────────────────────────┐                        │
│              │   OpenTelemetry Collector   │                       │
│              │   (DaemonSet / Sidecar)     │                       │
│              │                             │                       │
│              │  Receivers:                 │                       │
│              │   - OTLP (gRPC + HTTP)     │                       │
│              │   - filelog, hostmetrics    │                       │
│              │  Processors:                │                       │
│              │   - batch, resource, filter │                       │
│              │  Exporters:                 │                       │
│              │   - otlphttp → Parseable   │                       │
│              └──────────────┬─────────────┘                        │
│                             │                                      │
└─────────────────────────────┼──────────────────────────────────────┘
                              │ OTLP/HTTP (logs, metrics, traces)
                              ▼
                    ┌─────────────────────┐
                    │     Parseable       │
                    │  (Single Binary)    │
                    │                     │
                    │  - Native OTLP      │
                    │  - SQL via DataFusion│
                    │  - Built-in UI      │
                    │  - Alerting         │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │   S3 / Object Store │
                    │   (Apache Parquet)  │
                    │                     │
                    │  $0.023/GB/month    │
                    └─────────────────────┘

How Each Layer Works

1. OTel SDKs (Application Layer): Your services are instrumented with OpenTelemetry SDKs. These emit logs, metrics, and traces in OTLP format. Auto-instrumentation is available for Java, Python, Node.js, Go, .NET, Ruby, PHP, and more.

2. OTel Collector (Pipeline Layer): The Collector receives telemetry from SDKs, processes it (batching, enrichment, filtering), and exports it to Parseable. Deploy it as a DaemonSet for node-level collection or as a sidecar for pod-level isolation.

3. Parseable (Backend Layer): Parseable accepts OTLP data on its native endpoint, normalizes it into a columnar format, and writes it to object storage. All telemetry is queryable via SQL through the built-in UI or API. No translation layer, no adapter, no additional components.

4. S3 / Object Storage (Storage Layer): All telemetry lands as Apache Parquet files on S3, MinIO, GCS, Azure Blob, or any S3-compatible store. Parquet's columnar format enables efficient compression and fast analytical queries. Storage costs are determined by your cloud provider — typically $0.023/GB/month on AWS S3 Standard.

Full OTel Collector Configuration: Logs, Metrics, and Traces to Parseable

Below is a complete, production-ready OTel Collector configuration that sends all three signal types to Parseable. This is the single most important config file in your observability stack.

# otel-collector-config.yaml
# Complete config for logs, metrics, and traces → Parseable
 
receivers:
  # Receive OTLP from application SDKs (gRPC and HTTP)
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318
 
  # Collect host-level metrics (CPU, memory, disk, network)
  hostmetrics:
    collection_interval: 30s
    scrapers:
      cpu:
        metrics:
          system.cpu.utilization:
            enabled: true
      memory:
        metrics:
          system.memory.utilization:
            enabled: true
      disk: {}
      network: {}
      load: {}
 
  # Collect container logs from filesystem (for Kubernetes)
  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.uid
        to: resource["k8s.pod.uid"]
      - type: move
        from: attributes.log
        to: body
 
processors:
  # Batch telemetry for efficient export
  batch:
    send_batch_size: 512
    send_batch_max_size: 1024
    timeout: 5s
 
  # Add resource attributes for cluster identification
  resource:
    attributes:
      - key: cluster.name
        value: "production-cluster"
        action: insert
      - key: deployment.environment
        value: "production"
        action: insert
 
  # Memory limiter to prevent OOM
  memory_limiter:
    check_interval: 5s
    limit_mib: 400
    spike_limit_mib: 100
 
  # Filter out health check noise from traces
  filter/traces:
    error_mode: ignore
    traces:
      span:
        - 'attributes["http.route"] == "/healthz"'
        - 'attributes["http.route"] == "/readyz"'
        - 'attributes["http.route"] == "/livez"'
 
exporters:
  # Export logs to Parseable
  otlphttp/parseable-logs:
    logs_endpoint: "http://parseable:8000/v1/logs"
    encoding: json
    compression: gzip
    tls:
      insecure: true
    headers:
      Authorization: "Basic <BASE64_ENCODED_CREDENTIALS>"
      X-P-Stream: "otel-logs"
      X-P-Log-Source: "otel-logs"
 
  # Export metrics to Parseable
  otlphttp/parseable-metrics:
    metrics_endpoint: "http://parseable:8000/v1/metrics"
    encoding: json
    compression: gzip
    tls:
      insecure: true
    headers:
      Authorization: "Basic <BASE64_ENCODED_CREDENTIALS>"
      X-P-Stream: "otel-metrics"
      X-P-Log-Source: "otel-metrics"
 
  # Export traces to Parseable
  otlphttp/parseable-traces:
    traces_endpoint: "http://parseable:8000/v1/traces"
    encoding: json
    compression: gzip
    tls:
      insecure: true
    headers:
      Authorization: "Basic <BASE64_ENCODED_CREDENTIALS>"
      X-P-Stream: "otel-traces"
      X-P-Log-Source: "otel-traces"
 
  # Debug exporter for troubleshooting (remove in production)
  debug:
    verbosity: basic
 
service:
  telemetry:
    logs:
      level: info
    metrics:
      address: 0.0.0.0:8888
 
  pipelines:
    # Logs pipeline: OTLP + filelog → batch → Parseable
    logs:
      receivers: [otlp, filelog/containers]
      processors: [memory_limiter, resource, batch]
      exporters: [otlphttp/parseable-logs]
 
    # Metrics pipeline: OTLP + host metrics → batch → Parseable
    metrics:
      receivers: [otlp, hostmetrics]
      processors: [memory_limiter, resource, batch]
      exporters: [otlphttp/parseable-metrics]
 
    # Traces pipeline: OTLP → filter health checks → batch → Parseable
    traces:
      receivers: [otlp]
      processors: [memory_limiter, filter/traces, resource, batch]
      exporters: [otlphttp/parseable-traces]

Configuration Breakdown

Receivers: We configure three receivers. The otlp receiver accepts telemetry from application SDKs over gRPC (port 4317) and HTTP (port 4318). The hostmetrics receiver scrapes system-level metrics every 30 seconds. The filelog/containers receiver tails Kubernetes pod log files and extracts metadata from the file path structure.

Processors: The memory_limiter prevents the collector from consuming unbounded memory. The batch processor groups telemetry records for efficient network transmission. The resource processor injects cluster and environment labels. The filter/traces processor drops health check spans that generate noise.

Exporters: We define three separate otlphttp exporters — one for each signal type. Each targets a different Parseable OTLP endpoint (/v1/logs, /v1/metrics, /v1/traces) and routes data to a separate stream using the X-P-Stream header. This gives you clean separation of concerns while still landing everything in a single Parseable instance.

Pipelines: Each signal type gets its own pipeline, letting you apply different processing logic per signal. Traces get health check filtering; logs get both OTLP and filelog receivers; metrics get both OTLP and host metrics.

Note: Replace <BASE64_ENCODED_CREDENTIALS> with the base64-encoded string of your Parseable username:password. Generate it with: echo -n "admin:admin" | base64

Docker Compose: Local Development Stack

Tip: Prefer not to run Parseable and MinIO locally? Use Parseable Cloud and only deploy the OTel Collector and your application. Replace the Parseable endpoint in the collector config with your Cloud instance URL.

For local development and testing, here is a Docker Compose file that runs the full stack — a sample application instrumented with OTel, the OTel Collector, Parseable, and MinIO for object storage.

# docker-compose.yaml
version: "3.9"
 
services:
  # MinIO - S3-compatible object storage for Parseable
  minio:
    image: minio/minio:latest
    command: server /data --console-address ":9001"
    environment:
      MINIO_ROOT_USER: minioadmin
      MINIO_ROOT_PASSWORD: minioadmin
    ports:
      - "9000:9000"
      - "9001:9001"
    volumes:
      - minio-data:/data
    healthcheck:
      test: ["CMD", "mc", "ready", "local"]
      interval: 5s
      timeout: 5s
      retries: 5
 
  # Create the bucket Parseable needs
  minio-init:
    image: minio/mc:latest
    depends_on:
      minio:
        condition: service_healthy
    entrypoint: >
      /bin/sh -c "
      mc alias set local http://minio:9000 minioadmin minioadmin &&
      mc mb local/parseable-data --ignore-existing
      "
 
  # Parseable - Unified observability backend
  parseable:
    image: parseable/parseable:latest
    command:
      - parseable
      - s3-store
    environment:
      - P_S3_URL=http://minio:9000
      - P_S3_ACCESS_KEY=minioadmin
      - P_S3_SECRET_KEY=minioadmin
      - P_S3_REGION=us-east-1
      - P_S3_BUCKET=parseable-data
      - P_STAGING_DIR=/tmp/parseable/staging
      - P_USERNAME=admin
      - P_PASSWORD=admin
    ports:
      - "8000:8000"
    depends_on:
      minio-init:
        condition: service_completed_successfully
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/api/v1/liveness"]
      interval: 10s
      timeout: 5s
      retries: 5
 
  # OpenTelemetry Collector
  otel-collector:
    image: otel/opentelemetry-collector-contrib:0.115.0
    command: ["--config=/etc/otel/config.yaml"]
    volumes:
      - ./otel-collector-config.yaml:/etc/otel/config.yaml:ro
    ports:
      - "4317:4317"   # OTLP gRPC
      - "4318:4318"   # OTLP HTTP
      - "8888:8888"   # Collector metrics
    depends_on:
      parseable:
        condition: service_healthy
 
  # Sample instrumented application (Python Flask)
  sample-app:
    image: ghcr.io/open-telemetry/opentelemetry-demo:latest
    environment:
      - OTEL_EXPORTER_OTLP_ENDPOINT=http://otel-collector:4317
      - OTEL_SERVICE_NAME=sample-app
      - OTEL_RESOURCE_ATTRIBUTES=service.namespace=demo,deployment.environment=local
    depends_on:
      - otel-collector
 
volumes:
  minio-data:

Start the stack:

docker compose up -d

Within seconds, telemetry flows from the sample application through the OTel Collector into Parseable. Open http://localhost:8000 to access the Parseable UI and start querying your data with SQL.

Kubernetes Deployment: Production-Grade OTel Collector DaemonSet

For production Kubernetes clusters, deploy the OTel Collector as a DaemonSet so every node has a collector instance. This pattern is optimal for collecting node-level logs and host metrics alongside application-emitted OTLP telemetry.

Step 1: RBAC and ServiceAccount

# otel-rbac.yaml
apiVersion: v1
kind: Namespace
metadata:
  name: observability
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: otel-collector
  namespace: observability
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: otel-collector
rules:
  - apiGroups: [""]
    resources: [nodes, nodes/metrics, nodes/proxy, services, endpoints, pods]
    verbs: [get, list, watch]
  - apiGroups: [""]
    resources: [configmaps]
    verbs: [get]
  - apiGroups: ["apps"]
    resources: [daemonsets, deployments, replicasets, statefulsets]
    verbs: [get, list, watch]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: otel-collector
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: otel-collector
subjects:
  - kind: ServiceAccount
    name: otel-collector
    namespace: observability

Step 2: Collector ConfigMap

# otel-configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: otel-collector-config
  namespace: observability
data:
  config.yaml: |
    receivers:
      otlp:
        protocols:
          grpc:
            endpoint: 0.0.0.0:4317
          http:
            endpoint: 0.0.0.0:4318
 
      hostmetrics:
        collection_interval: 30s
        scrapers:
          cpu:
            metrics:
              system.cpu.utilization:
                enabled: true
          memory:
            metrics:
              system.memory.utilization:
                enabled: true
          disk: {}
          network: {}
          load: {}
 
      filelog/pods:
        include:
          - /var/log/pods/*/*/*.log
        exclude:
          - /var/log/pods/*/otel-collector/*.log
          - /var/log/pods/kube-system_*/*/*.log
        start_at: end
        include_file_path: true
        operators:
          - type: regex_parser
            id: extract_metadata
            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.uid
            to: resource["k8s.pod.uid"]
          - type: move
            from: attributes.log
            to: body
 
    processors:
      memory_limiter:
        check_interval: 5s
        limit_mib: 400
        spike_limit_mib: 100
      batch:
        send_batch_size: 512
        send_batch_max_size: 1024
        timeout: 5s
      resource:
        attributes:
          - key: cluster.name
            value: "production"
            action: insert
          - key: deployment.environment
            value: "production"
            action: insert
 
    exporters:
      otlphttp/parseable-logs:
        logs_endpoint: "http://parseable.observability.svc.cluster.local:8000/v1/logs"
        encoding: json
        compression: gzip
        tls:
          insecure: true
        headers:
          Authorization: "Basic <BASE64_ENCODED_CREDENTIALS>"
          X-P-Stream: "k8s-logs"
          X-P-Log-Source: "otel-logs"
 
      otlphttp/parseable-metrics:
        metrics_endpoint: "http://parseable.observability.svc.cluster.local:8000/v1/metrics"
        encoding: json
        compression: gzip
        tls:
          insecure: true
        headers:
          Authorization: "Basic <BASE64_ENCODED_CREDENTIALS>"
          X-P-Stream: "k8s-metrics"
          X-P-Log-Source: "otel-metrics"
 
      otlphttp/parseable-traces:
        traces_endpoint: "http://parseable.observability.svc.cluster.local:8000/v1/traces"
        encoding: json
        compression: gzip
        tls:
          insecure: true
        headers:
          Authorization: "Basic <BASE64_ENCODED_CREDENTIALS>"
          X-P-Stream: "k8s-traces"
          X-P-Log-Source: "otel-traces"
 
    service:
      telemetry:
        logs:
          level: info
      pipelines:
        logs:
          receivers: [otlp, filelog/pods]
          processors: [memory_limiter, resource, batch]
          exporters: [otlphttp/parseable-logs]
        metrics:
          receivers: [otlp, hostmetrics]
          processors: [memory_limiter, resource, batch]
          exporters: [otlphttp/parseable-metrics]
        traces:
          receivers: [otlp]
          processors: [memory_limiter, resource, batch]
          exporters: [otlphttp/parseable-traces]

Step 3: DaemonSet Deployment

# otel-daemonset.yaml
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: otel-collector
  namespace: observability
  labels:
    app: otel-collector
spec:
  selector:
    matchLabels:
      app: otel-collector
  template:
    metadata:
      labels:
        app: otel-collector
    spec:
      serviceAccountName: otel-collector
      tolerations:
        - operator: Exists
      containers:
        - name: otel-collector
          image: otel/opentelemetry-collector-contrib:0.115.0
          args: ["--config=/etc/otel/config.yaml"]
          ports:
            - containerPort: 4317
              hostPort: 4317
              protocol: TCP
              name: otlp-grpc
            - containerPort: 4318
              hostPort: 4318
              protocol: TCP
              name: otlp-http
            - containerPort: 8888
              protocol: TCP
              name: metrics
          env:
            - name: NODE_NAME
              valueFrom:
                fieldRef:
                  fieldPath: spec.nodeName
            - name: HOST_IP
              valueFrom:
                fieldRef:
                  fieldPath: status.hostIP
          resources:
            requests:
              cpu: 200m
              memory: 256Mi
            limits:
              cpu: "1"
              memory: 512Mi
          volumeMounts:
            - name: config
              mountPath: /etc/otel
            - name: varlogpods
              mountPath: /var/log/pods
              readOnly: true
            - name: varlibdockercontainers
              mountPath: /var/lib/docker/containers
              readOnly: true
          livenessProbe:
            httpGet:
              path: /
              port: 13133
            initialDelaySeconds: 15
            periodSeconds: 10
          readinessProbe:
            httpGet:
              path: /
              port: 13133
            initialDelaySeconds: 5
            periodSeconds: 10
      volumes:
        - name: config
          configMap:
            name: otel-collector-config
        - name: varlogpods
          hostPath:
            path: /var/log/pods
        - name: varlibdockercontainers
          hostPath:
            path: /var/lib/docker/containers
---
# Service for applications to send OTLP data to the collector
apiVersion: v1
kind: Service
metadata:
  name: otel-collector
  namespace: observability
spec:
  selector:
    app: otel-collector
  ports:
    - name: otlp-grpc
      port: 4317
      targetPort: 4317
      protocol: TCP
    - name: otlp-http
      port: 4318
      targetPort: 4318
      protocol: TCP
  type: ClusterIP

Step 4: Deploy Everything

kubectl apply -f otel-rbac.yaml
kubectl apply -f otel-configmap.yaml
kubectl apply -f otel-daemonset.yaml
 
# Verify collector pods are running (one per node)
kubectl get pods -n observability -l app=otel-collector
 
# Check collector logs
kubectl logs -n observability -l app=otel-collector --tail=20

Applications in your cluster can now send OTLP telemetry to otel-collector.observability.svc.cluster.local:4317 (gRPC) or port 4318 (HTTP). The collector handles batching, enrichment, and export to Parseable.

Querying OpenTelemetry Data in Parseable with SQL

Once telemetry is flowing, Parseable gives you a SQL interface (powered by Apache Arrow DataFusion) to query across all signal types.

Query Logs

-- Find error logs from a specific service in the last hour
SELECT
  p_timestamp,
  "service.name",
  "k8s.namespace.name",
  severity_text,
  body
FROM "k8s-logs"
WHERE severity_text IN ('ERROR', 'FATAL')
  AND "service.name" = 'checkout-service'
ORDER BY p_timestamp DESC
LIMIT 100;

Query Traces

-- Find slow spans (> 500ms) for a specific service
SELECT
  trace_id,
  span_id,
  "service.name",
  span_name,
  duration_nano / 1000000 AS duration_ms,
  status_code
FROM "k8s-traces"
WHERE "service.name" = 'payment-service'
  AND duration_nano > 500000000
ORDER BY duration_nano DESC
LIMIT 50;

Query Metrics

-- Get CPU utilization across nodes
SELECT
  p_timestamp,
  "host.name",
  metric_name,
  gauge_value
FROM "k8s-metrics"
WHERE metric_name = 'system.cpu.utilization'
ORDER BY p_timestamp DESC
LIMIT 200;

Cross-Signal Correlation

The power of unified observability is correlating across signal types. When you find a slow trace, you can immediately look up the logs for that same trace:

-- Step 1: Find the slow trace
SELECT trace_id, span_name, duration_nano / 1000000 AS duration_ms
FROM "k8s-traces"
WHERE "service.name" = 'order-service'
  AND duration_nano > 2000000000
ORDER BY duration_nano DESC
LIMIT 1;
 
-- Step 2: Get all logs for that trace ID
SELECT p_timestamp, severity_text, body
FROM "k8s-logs"
WHERE trace_id = '<trace_id_from_step_1>'
ORDER BY p_timestamp ASC;

This is what "unified observability" actually means in practice — one backend, one query language, full correlation across signals.

Comparing OTel Backends: Parseable vs the Alternatives

When choosing an OpenTelemetry backend, you are evaluating five dimensions: signal coverage, storage cost, operational complexity, query capabilities, and scalability. Here is how the major options compare.

Capability	Parseable	Jaeger	Elasticsearch	SigNoz	Grafana Stack (Loki + Tempo + Mimir)
Logs	Native OTLP	No	Via Filebeat/OTel	Native OTLP	Loki (LogQL)
Traces	Native OTLP	Native OTLP	Via APM agent	Native OTLP	Tempo (TraceQL)
Metrics	Native OTLP	No	Via Metricbeat	Native OTLP	Mimir (PromQL)
Unified Backend	Single binary	Traces only	Logs primarily	Yes (ClickHouse)	3 separate systems
Storage	S3 (Parquet)	Cassandra/ES/Badger	Lucene indices	ClickHouse	Object storage (each)
Storage Cost (1TB/mo)	~$23 (S3)	$200+ (Cassandra)	$300+ (EBS/SSD)	$100+ (ClickHouse NVMe)	~$50-100 (mixed)
Query Language	SQL	Jaeger UI only	KQL/Lucene	ClickHouse SQL	LogQL + TraceQL + PromQL
Deployment Complexity	Single binary	Medium	High (cluster)	Medium (ClickHouse)	High (3 systems + Grafana)
Deployment	Cloud (app.parseable.com) + self-hosted	Self-hosted	Self-hosted or Elastic Cloud	Self-hosted or Cloud	Self-hosted or Grafana Cloud
Memory Footprint	<50MB idle	~500MB	4GB+ minimum	1GB+	2GB+ per component

Jaeger

Jaeger is purpose-built for distributed tracing. It does one thing well, but it only does that one thing. If you need logs or metrics, you need additional backends. Jaeger also requires a storage backend of its own (Cassandra, Elasticsearch, or Badger), adding operational overhead. For teams that need traces alongside logs and metrics, Jaeger is not sufficient on its own.

Elasticsearch / OpenSearch

Elasticsearch is a general-purpose search engine, not an observability platform. You can make it work for logs and traces (via Elastic APM), but it was designed for full-text search, not telemetry analytics. The operational burden is significant — cluster management, shard balancing, index lifecycle policies, JVM heap tuning. Storage costs are high because Lucene indices require fast disk. The query language (KQL/Lucene) is not SQL.

SigNoz

SigNoz is the closest architectural peer to Parseable — it is open source, supports all OTel signal types, and provides a unified UI. The key difference is the storage layer. SigNoz uses ClickHouse, which requires NVMe SSDs for performance and has non-trivial operational complexity at scale (replication, sharding, schema migrations). Parseable uses S3/object storage, which is serverless, infinitely scalable, and costs an order of magnitude less per gigabyte.

Grafana Stack (Loki + Tempo + Mimir)

The Grafana stack is powerful but complex. You deploy and operate three separate backends — Loki for logs, Tempo for traces, Mimir for metrics — plus Grafana for visualization. Each has its own query language (LogQL, TraceQL, PromQL), its own scaling characteristics, and its own failure modes. For large teams with dedicated platform engineering resources, this works. For everyone else, the operational surface area is prohibitive.

Why Parseable Is the Ideal OpenTelemetry Backend

Choosing an observability backend is one of the most consequential infrastructure decisions a platform team makes. The backend you pick determines your storage costs, query capabilities, operational burden, and how quickly your engineers can debug production incidents. After evaluating the landscape, here is why Parseable stands out as the optimal destination for OpenTelemetry telemetry in 2026.

S3-Native Storage Eliminates the Biggest Cost Driver

The single largest line item in most observability budgets is storage. Traditional backends — Elasticsearch, ClickHouse, Cassandra — require provisioned compute-attached storage (EBS, NVMe SSDs, local disks). You pay for the storage whether you use it or not, and you pay premium rates for the IOPS you need to serve queries.

Parseable stores all telemetry as Apache Parquet files on S3-compatible object storage. The cost model is fundamentally different:

AWS S3 Standard: $0.023/GB/month
S3 Infrequent Access: $0.0125/GB/month
S3 Glacier Instant Retrieval: $0.004/GB/month
MinIO (self-hosted): Cost of raw disk only

For a team ingesting 1TB of telemetry per day, the storage cost on S3 is approximately $690/month for 30-day retention. The same volume on Elasticsearch clusters with gp3 EBS volumes runs $3,000–5,000/month or more when you factor in replica shards and operational overhead. On a proprietary SaaS platform, you are looking at $10,000+/month easily.

This is not a marginal savings — it is a structural cost advantage that compounds as your data volumes grow. And because S3 is effectively infinitely scalable, you never hit a capacity cliff that requires an emergency cluster resize at 2 AM.

Native OTLP Means Zero Translation Overhead

Parseable speaks OTLP natively. When the OTel Collector sends data to Parseable's /v1/logs, /v1/metrics, or /v1/traces endpoints, there is no adapter, no translation layer, no middleware converting OTLP into some internal format. The OTLP payload is ingested directly, normalized into Parseable's columnar schema, and written to object storage.

This matters for three reasons. First, fewer moving parts means fewer failure points. Second, the absence of a translation layer reduces ingestion latency. Third, all OTel semantic conventions (resource attributes, scope attributes, span events) are preserved without lossy conversion.

Compare this to backends that require you to run an intermediate adapter — for instance, using the Elasticsearch exporter in the OTel Collector, which must map OTel's data model to Elasticsearch's document model. Every mapping introduces the possibility of data loss, semantic drift, and debugging complexity.

Unified MELT in a Single Binary

Parseable handles logs, metrics, events, and traces in a single process. There is no "logs backend" and "traces backend" running as separate clusters with separate scaling knobs. One binary, one API, one query engine.

This has profound operational implications. Your on-call engineer learns one system, not three. Your Terraform modules deploy one service, not three. Your monitoring dashboards watch one endpoint, not three. Your capacity planning models account for one data sink, not three.

The unified data model also enables cross-signal correlation that is impossible or awkward when signals live in separate systems. A single SQL query can join trace IDs across your logs and traces streams, surfacing the exact log lines associated with a slow span — without context-switching between Grafana panels backed by different data sources.

SQL Queries via Apache Arrow DataFusion

Parseable uses Apache Arrow DataFusion as its query engine, giving you full ANSI SQL over your telemetry. This is a deliberate architectural choice:

SQL is universal: Every engineer knows it. No one needs to learn LogQL, TraceQL, SPL, or KQL.
SQL is composable: Use CTEs, window functions, JOINs, subqueries, and aggregations to answer complex questions.
SQL is tooling-friendly: Connect any BI tool, notebook, or CLI that speaks SQL.
Arrow columnar format: Queries run directly on Parquet data via Arrow's zero-copy read path, delivering sub-second performance on analytical workloads.

For example, to find the top 10 services by error rate in the last hour:

SELECT
  "service.name",
  COUNT(*) FILTER (WHERE severity_text = 'ERROR') AS errors,
  COUNT(*) AS total,
  ROUND(100.0 * COUNT(*) FILTER (WHERE severity_text = 'ERROR') / COUNT(*), 2) AS error_rate_pct
FROM "k8s-logs"
GROUP BY "service.name"
ORDER BY error_rate_pct DESC
LIMIT 10;

Try writing that in LogQL.

Built in Rust for Minimal Resource Footprint

Parseable is built in Rust and ships as a single static binary. In idle state, it consumes less than 50MB of RAM. Under load, memory usage scales with query concurrency, not data volume — because the data lives on S3, not in memory.

This makes Parseable viable in environments where other backends are not: edge clusters, IoT gateways, resource-constrained namespaces, developer laptops. You can run a production-grade observability backend in a 512Mi memory limit Kubernetes pod.

Cloud and Self-Hosted Deployment

Parseable Cloud is the fastest way to get started — a fully managed service starting at $0.37/GB ingested ($29/month minimum) with a free tier. No infrastructure to manage, no S3 buckets to configure.

For teams that need infrastructure control, Parseable is also available as a self-hosted deployment with source code on GitHub. There is no "open core" bait-and-switch where critical features like alerting, RBAC, or high availability are locked behind a commercial license.

Advanced Patterns: Scaling the OTel + Parseable Stack

Multi-Cluster Telemetry Aggregation

For organizations running multiple Kubernetes clusters, use a two-tier collector architecture:

Cluster A (DaemonSet Collectors) ──┐
                                    ├──▶ Gateway Collector ──▶ Parseable ──▶ S3
Cluster B (DaemonSet Collectors) ──┘

The gateway collector receives OTLP from all clusters, applies cross-cluster processing (deduplication, sampling), and exports to Parseable. Tag each cluster in the resource processor so you can filter by cluster in queries.

Tail-Based Sampling for Traces

To reduce trace volume without losing important data, enable tail-based sampling in the gateway collector:

processors:
  tail_sampling:
    decision_wait: 10s
    num_traces: 100000
    policies:
      - name: error-policy
        type: status_code
        status_code:
          status_codes: [ERROR]
      - name: slow-policy
        type: latency
        latency:
          threshold_ms: 1000
      - name: probabilistic-policy
        type: probabilistic
        probabilistic:
          sampling_percentage: 10

This keeps 100% of error and slow traces while sampling 10% of everything else.

Alerting on OTel Data

Parseable has built-in alerting that runs SQL queries on a schedule and triggers notifications when conditions are met. For example, alert when error rate spikes:

-- Alert: Error rate exceeds 5% in any service
SELECT "service.name", COUNT(*) AS errors
FROM "k8s-logs"
WHERE severity_text = 'ERROR'
GROUP BY "service.name"
HAVING COUNT(*) > 100

Configure alert destinations including Slack, PagerDuty, email, and webhooks directly in the Parseable UI.

Frequently Asked Questions

Does Parseable support OTLP over gRPC?

Yes. Parseable accepts OTLP over both HTTP and gRPC. The OTel Collector configurations in this guide use OTLP/HTTP because it is simpler to debug (you can inspect payloads with curl), but gRPC works identically and is more efficient for high-throughput pipelines.

Can I use Parseable as a drop-in replacement for Jaeger?

For trace storage and querying, yes. Parseable accepts OTel traces via the same OTLP protocol that Jaeger supports. You point your OTel Collector's trace exporter at Parseable instead of Jaeger. The difference is that Parseable also stores your logs and metrics, giving you unified observability rather than traces in isolation. Parseable does not implement the Jaeger UI — you use Parseable's built-in UI or connect your own visualization tools.

How does Parseable handle high-cardinality metrics?

Parseable stores metrics as Apache Parquet on object storage. Unlike Prometheus or InfluxDB, which use time-series-specific storage engines with cardinality limits, Parseable treats metrics as structured data in a columnar format. High-cardinality labels (like container_id or request_id) are just columns in Parquet — the storage cost scales linearly with data volume, not with the number of unique label combinations.

What happens if the OTel Collector cannot reach Parseable?

The OTel Collector has built-in retry logic with exponential backoff. If Parseable is temporarily unreachable, the collector queues data in memory (bounded by the memory_limiter processor) and retries. For durability across collector restarts, configure the file_storage extension to persist the retry queue to disk. In practice, Parseable's single-binary architecture means it has minimal downtime — there is no distributed consensus, no shard rebalancing, and no cluster coordination that can fail.

Can I migrate existing data from Elasticsearch or Jaeger to Parseable?

Parseable accepts data via its HTTP API and OTLP endpoints. The recommended migration path is to run both backends in parallel during a transition period — point the OTel Collector at both your existing backend and Parseable. Once you have validated your queries, dashboards, and alerts in Parseable, decommission the old backend. For historical data, you can use Parseable's bulk ingest API to load exported data.

How do I set up retention policies for OTel data in Parseable?

Parseable supports per-stream retention policies. You can configure different retention periods for each signal type — for example, 7 days for debug logs, 30 days for traces, and 90 days for metrics. Since data is stored on S3, you can also leverage S3 lifecycle policies to automatically transition older data to cheaper storage tiers (S3 IA, Glacier) for long-term compliance retention.

Getting Started

You can have the full OpenTelemetry + Parseable stack running in under 10 minutes. Pick your path:

Parseable Cloud (Recommended): Create a free account at app.parseable.com — starts at $0.37/GB ingested ($29/month minimum), no infrastructure to manage. Point your OTel Collector at the provided OTLP endpoint and start querying immediately.
Docker Compose: Use the Docker Compose configuration from this guide for local development and testing.
Kubernetes: Apply the RBAC, ConfigMap, and DaemonSet manifests from this guide to your cluster.
Self-hosted source: Explore the source, file issues, and contribute at github.com/parseablehq/parseable.

OpenTelemetry gives you vendor-neutral instrumentation. Parseable gives you a unified, cost-effective backend that does justice to that telemetry. Together, they form the modern observability stack — simple to deploy, powerful to query, and affordable to run at any scale.

Related reading:

OpenTelemetry + Parseable: The Modern Observability Stack (2026)

Predictive Observability at Scale

Table of Contents

Try Parseable Pro free for 14 days

Subscribe to our newsletter

Home

Pricing

Resources

Company

SFO

BLR