Data-accessible telemetry platforms

If your priority is easy access to raw telemetry without vendor lock-in, I'd start by evaluating platforms built around OpenTelemetry, especially those that let you route data to your own storage or export pipeline.

The key distinction is that accepting telemetry through an open standard is not the same as letting you export all the raw data you have already stored. You want both: portable data collection and a clear way to retrieve your data in usable formats.

OpenTelemetry is a strong foundation because it's a vendor-neutral framework for collecting and exporting logs, metrics, and traces, supported by a broad ecosystem of observability vendors.opentelemetry.io

Best telemetry platforms to shortlist

1. Grafana LGTM Stack

(Best for data ownership)

Best for: Teams that want self-hosting, control over storage, and flexible exports.

Grafana's stack combines Prometheus or Mimir for metrics, Loki for logs, and Tempo for traces. You can self-host components and choose your storage infrastructure. Loki supports object storage such as Amazon S3 and Google Cloud Storage, while Tempo stores trace data in object storage.grafana.com+2

• Data access: Strong when you control the storage and retention.
• Trade-off: More infrastructure and operational work.
• Explore:Grafana open-source observability

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2. SigNoz

(Best unified self-hosted option)

Best for: Teams that want a unified logs, metrics, and traces platform without building a stack from separate tools.

SigNoz is OpenTelemetry-native and its self-hosted edition lets you operate the collection pipeline and ingestion endpoints yourself. Its architecture uses ClickHouse for telemetry storage.signoz.io+1

• Data access: Self-hosting gives you direct control of the underlying infrastructure.
• Trade-off: Verify query and export workflows against your exact data types and schema.
• Explore:SigNoz self-hosting documentation

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3. Elastic Observability

Best for: Search-heavy log analytics, structured events, and teams that want programmatic access to stored data.

Elasticsearch provides APIs for working with indexed data, making it attractive when your team needs to retrieve events and integrate them with external data pipelines.

• Data access: API-based retrieval and self-managed deployment options.
• Trade-off: Check licensing, storage formats, and which features require a particular subscription.
• Explore:Elastic Observability

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4. Honeycomb

Best for: Teams that want managed, high-cardinality event and trace analysis with portable instrumentation.

Honeycomb accepts OpenTelemetry data through OTLP, so you can instrument applications independently of the backend.docs.honeycomb.io+4

• Data access: Open ingestion standards make it easier to route data elsewhere before it reaches Honeycomb.
• Trade-off: Don't assume OTLP ingestion guarantees bulk export of all retained events. Confirm historical data retrieval, retention, and export limits.
• Explore:Honeycomb data ingestion documentation

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5. Grafana Cloud

Best for: Teams that want managed operations but still value open ingestion and flexible routing.

Grafana Cloud supports OTLP ingestion and a range of telemetry sources. Grafana Alloy can route telemetry to multiple compatible backends, helping you maintain options outside the managed service.grafana.com+1

• Data access: Good portability at the collection and routing layer.
• Trade-off: Validate how to retrieve historical stored data and whether bulk exports incur extra costs.
• Explore:Grafana Cloud data ingestion

Quick comparison

These are qualitative assessments of architecture and portability, not a guarantee that every product offers unrestricted export of every signal.opentelemetry.io+4

What to verify before choosing

I would make these requirements part of your procurement checklist:

• Complete raw-data export: Can you retrieve individual events, spans, log records, and metric samples—not just dashboards or aggregates?
• Machine-readable formats: JSON, NDJSON, Parquet, or another documented format you can process independently.
• Documented APIs: Bulk export, pagination, time-range queries, and programmatic access.
• No punitive egress costs: Understand API limits, export fees, and bandwidth charges.
• Independent retention: Can you retain a copy in your own S3-compatible object storage or data lake?
• Portable instrumentation: Applications use OpenTelemetry SDKs and collectors rather than vendor-specific agents wherever practical.
• A real exit path: Test migrating a sample of historical data to another backend before committing.

One particularly important distinction: metrics are often stored as aggregates or time series, and tracing platforms may sample or discard spans. Even a platform with excellent export APIs cannot export data it never retained.

My recommendation

For maximum control, I would shortlist Grafana LGTM and SigNoz first. If you prefer a managed service, evaluate Grafana Cloud and Honeycomb, but make historical bulk-export guarantees a deciding factor.

A resilient architecture looks like this:

Applications and infrastructure

Logs · Metrics · Traces

OpenTelemetry Collector

Normalize · Filter · Route · Duplicate

Observability platform

Dashboards, alerts, investigation

Your storage

Raw records, long-term retention, analytics

Independent destinations help preserve your exit options. Confirm that your collector pipeline supports each signal and destination.

OpenTelemetry's Collector is designed to receive, process, and export telemetry, and Grafana Alloy provides a supported collector distribution with multi-backend routing capabilities.grafana.com+5

One question to narrow this down: What kind of telemetry are you collecting?

1. Primary telemetry

Application logs, metrics, and distributed traces

Infrastructure, servers, Kubernetes, and network metrics

Product usage events and behavioral analytics

IoT devices, sensors, and high-volume time series

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2. Deployment preference

Self-hosted only

Managed SaaS only

Either, if data stays portable

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3. Most important requirement

Full raw-data export and no lock-in

Low cost at high telemetry volume

Easy setup and low operational overhead

Long-term retention in our own data lake

[Find the best-fit platforms]