Bring Your Own Bucket: Data Ownership Is the Future of Observability

There is a question that almost nobody asks during an observability tool evaluation, and it might be the most consequential one: who owns the data after it leaves your infrastructure?

When you ship logs, metrics, and traces to a SaaS observability vendor, that telemetry leaves your cloud account, gets ingested into the vendor's proprietary storage, and from that moment forward, accessing it is governed entirely by their terms, their retention policies, and their pricing model. You generated the data. You paid to collect it. But you no longer control it.

This is not an abstract concern. It shapes how much you pay for observability, how long you retain data, whether you can pass a compliance audit, and how painful it will be if you ever need to switch platforms. Data ownership is quietly becoming the most important factor in choosing an observability platform, and the Bring Your Own Bucket (BYOB) model is the architectural pattern that solves it.

The SaaS Observability Bargain

Let's acknowledge the obvious: SaaS observability platforms exist for good reason. Datadog, New Relic, Splunk Cloud, and similar vendors built products that are genuinely easy to adopt. Sign up, install an agent, and you have dashboards and alerts within minutes. No infrastructure to manage. No databases to tune. No upgrades to coordinate.

For small teams and early-stage startups, this trade-off is often the right one. The operational simplicity of a fully managed service is real, and the cost at low volumes is reasonable. Nobody should feel bad about choosing managed observability when it fits their situation.

The problem is what happens at scale, when that initial convenience calcifies into structural dependency.

What You Give Up When a Vendor Owns Your Data

Once telemetry flows into a vendor's infrastructure, several things become true simultaneously, and most teams do not fully appreciate the implications until it is too late.

Vendor Lock-In Through Proprietary Storage

SaaS observability platforms store your data in proprietary formats on infrastructure you cannot access directly. There is no "download my data" button that gives you back your raw logs in a portable format. If you want to migrate to a different platform, you are starting from zero — there is no way to bring your historical data along.

This means that every month you spend ingesting data into a SaaS vendor is a month of history you will lose if you leave. For organizations with regulatory requirements around data retention — where you might need to keep audit logs for three, five, or seven years — this creates a paradox. You need long-term retention, but you also cannot guarantee you will be with the same vendor for that entire period. The true cost of observability goes well beyond the monthly invoice.

Cost Unpredictability

When a vendor controls your storage, they control the economics. A traffic spike, a verbose deployment, or an overly chatty microservice can blow through ingestion budgets overnight. Worse, many vendors charge separately for ingestion, storage, and queries — so even accessing data you have already paid to ingest costs additional money.

This unpredictability forces engineering teams into defensive postures: aggressive sampling, short retention windows, dropping entire log streams. You end up paying a premium for the privilege of seeing less of your own data.

Compliance and Data Sovereignty Risk

In regulated industries — finance, healthcare, government, defense — data residency and sovereignty are not optional. When telemetry leaves your cloud environment and enters a vendor's multi-tenant infrastructure, you lose control over where it is stored, who can access it, and how it is encrypted at rest.

Even if a vendor offers region selection (US, EU), you are still trusting their infrastructure to enforce the boundaries your compliance team requires. During an audit, "we trust our vendor's compliance certifications" is a weaker position than "the data never leaves our AWS account."

You Cannot Query Your Own Data With Your Own Tools

This is the limitation that often surprises teams the most. When telemetry lives in a vendor's proprietary storage, the only way to query it is through the vendor's query interface. You cannot point Spark, DuckDB, Athena, or any other analytical tool at your observability data to run custom analyses, build ML models, or correlate telemetry with business metrics from your data warehouse.

Your observability data becomes an island, accessible only through one vendor's lens. For organizations building data-driven incident response or applying machine learning to operational data, this is a serious constraint.

What "Bring Your Own Bucket" Actually Means

Bring Your Own Bucket (BYOB) is an architecture where the observability platform writes all telemetry data directly to an object storage bucket that you own — your S3 account, your GCS bucket, your Azure Blob container. The platform handles ingestion, indexing, and querying, but the underlying data stays in your cloud account at all times.

This is not the same as "self-hosted." In a BYOB model, the observability platform can still be fully managed — you are not running infrastructure yourself. The critical difference is where the data lands. With SaaS, data goes to the vendor. With BYOB, data stays with you.

There is a related model called BYOC (Bring Your Own Cloud), where the compute plane also runs in your cloud account. BYOC takes the ownership model further by keeping both data and processing within your perimeter. For organizations with strict security requirements, BYOC provides the most control.

The Open Format Advantage

BYOB alone is valuable, but the format the data is stored in matters just as much. If a BYOB platform writes data in a proprietary format, you still cannot use it independently — you are locked into the vendor's query engine.

The real unlock happens when BYOB is paired with an open, industry-standard format like Apache Parquet. Parquet is a columnar storage format readable by virtually every analytical tool in the modern data ecosystem. When your observability data lives in Parquet files in your own bucket, you can:

• Query it with any compatible tool — Spark, DuckDB, Athena, Presto, Trino, pandas
• Join it with business data in your data warehouse or lakehouse
• Build custom ML pipelines for anomaly detection or capacity planning
• Archive indefinitely at object storage costs ($0.02-0.03/GB/month)
• Migrate freely — your data works with any Parquet-compatible platform

This is the difference between a data lake and a data warehouse approach to observability. A data lake keeps your options open.

Real-World Scenarios Where Data Ownership Matters

Scenario 1: The Compliance Audit

Your organization operates in a regulated industry. An auditor asks: "Where is your infrastructure telemetry stored? Who has access to it? Can you demonstrate chain of custody for the last 24 months?"

With SaaS observability: You point to your vendor's SOC 2 report and hope the auditor accepts third-party assurance. If they want to inspect the data directly, you file a support ticket and wait.

With BYOB: The data is in your S3 bucket, encrypted with your KMS keys, governed by your IAM policies. You can demonstrate access controls, produce audit trails, and provide the data directly. The conversation is much shorter.

Scenario 2: The Vendor Migration

After two years on a SaaS platform, the annual renewal comes in 40% higher than expected. Your team evaluates alternatives, and quickly realizes that migrating means losing two years of historical telemetry. You cannot export it in a portable format. All your carefully tuned dashboards and alert queries are in a proprietary syntax that does not transfer.

With BYOB on open formats: Your historical data is Parquet files in your own bucket. You can point a new query engine at the same data. Your retention history survives the migration. Switching platforms is a compute decision, not a data decision.

Scenario 3: The Cost Negotiation

Your observability vendor knows you have two years of data locked in their platform. Both sides understand the switching cost is enormous. This is not a negotiation where you have leverage.

With BYOB: Your data is yours. If terms change unfavorably, you switch the compute layer while keeping your data. The vendor knows this, which tends to produce more reasonable pricing from the start.

Scenario 4: Advanced Analytics

Your ML team wants to predict service degradation by correlating traces with infrastructure metrics. With SaaS observability, they need the vendor's limited API, deal with rate limits, and transform proprietary formats.

With BYOB on Parquet: They point their Spark cluster at the S3 bucket and start building. The data is already in the format their tools expect. This is the foundation of an observability lakehouse — operational telemetry that doubles as an analytical asset.

How Parseable Approaches Data Ownership

Parseable was designed around the principle that telemetry data belongs to the people who generate it. The architecture stores all data in Apache Parquet on object storage, and the deployment model gives teams a clear spectrum of control depending on their needs.

Parseable Pro: Managed Simplicity

Parseable Pro is the managed cloud offering, at $0.39/GB ingested. It provides 365-day retention, AI-native analysis, anomaly detection, unlimited users, dashboards, alerts, and full API access. It includes a 14-day free trial so teams can evaluate the platform with real workloads.

Pro runs on Parseable Cloud — a shared, multi-tenant infrastructure managed by the Parseable team. This means the data lives on Parseable's infrastructure, similar to other SaaS platforms. For teams that want managed simplicity without operational overhead and do not have strict data sovereignty requirements, Pro is an excellent option. The economics are compelling: $0.39/GB is a fraction of what Datadog or Splunk charge, and 365-day retention is included rather than being a costly add-on.

Parseable Enterprise: Full Data Ownership With BYOB

For organizations where data ownership is non-negotiable, the Parseable Enterprise plan offers BYOB — Bring Your Own Bucket. Starting at $15,000/year with custom pricing, Enterprise keeps all telemetry data in the customer's own S3, GCS, or Azure Blob account. Enterprise per-GB rates are also lower: $0.25/GB for BYOC and $0.20/GB for self-hosted deployments.

Enterprise BYOB means:

• Data never leaves your cloud account. Parseable writes directly to your bucket.
• Data is stored in Apache Parquet — open, columnar, and readable by any compatible tool.
• Apache Iceberg support for table-format management, enabling catalog-level interoperability with your broader data ecosystem.
• External query access — point Spark, DuckDB, Athena, or any Parquet-compatible engine at your bucket and run queries independently of Parseable.
• Unlimited retention — governed by your object storage policies, not a vendor's pricing tier.
• Flexible deployment — run on Parseable Cloud (managed), BYOC (in your cloud account), or fully self-hosted.

The query engine powering all of this is ParseableDB, built on Apache Arrow DataFusion. It provides fast, interactive queries over Parquet data without requiring the complex cluster management that traditional OLAP databases demand. The web UI, Prism, gives engineers a familiar interface for search, correlation, dashboards, and alerting, while the underlying data remains open and portable.

Enterprise also includes native OTLP ingestion for OpenTelemetry compatibility and premium support. For self-hosted deployments, Parseable ships as a single binary, which means a production-grade observability platform can be running in minutes rather than days.

The Spectrum of Control

This is worth emphasizing: Parseable does not force you into one model. Teams that want fast onboarding and zero infrastructure management start with Pro. Organizations that need data sovereignty, regulatory compliance, or long-term analytical flexibility move to Enterprise with BYOB. The SaaS observability model is not broken for everyone — but for organizations where data ownership matters, having a clear path to full control is essential.

Evaluating BYOB: What to Look For

If data ownership is a priority, here is a quick framework for evaluating BYOB observability platforms:

1. Where does the data land? Confirm data is written directly to your storage account, not replicated from the vendor's infrastructure. True BYOB means your bucket is the primary store.
2. What format is the data in? Open formats like Apache Parquet are essential. Proprietary formats mean BYOB in name only.
3. Can external tools query it? Test this directly. Run a DuckDB or Athena query against the bucket. If external tools cannot read the data, the "open format" claim does not hold.
4. What happens if you stop paying? With true BYOB, your data remains accessible after cancellation. You lose the query engine, but the data is yours. This is the ultimate lock-in test.
5. Is compute separated from storage? A well-architected BYOB platform lets you scale query capacity independently and swap out the compute layer without touching your data.

The Industry Is Moving This Way

The BYOB model is not a niche requirement. It reflects a broader shift toward observability data lakes — architectures where telemetry is treated as a first-class data asset rather than a disposable operational byproduct. Cloud cost maturity, regulatory pressure from GDPR/HIPAA/SOX/FedRAMP, data mesh adoption, and the rise of OpenTelemetry are all driving organizations toward the same conclusion: if your telemetry pipeline is vendor-neutral, your storage should be too.

Getting Started

If you want to experience Parseable's approach to observability today, there are two paths:

Start with Parseable Pro for immediate, managed observability. Sign up at app.parseable.com for a 14-day free trial. Pro gives you $0.39/GB ingestion, 365-day retention, AI-native analysis, and full-featured dashboards and alerting. It is the fastest way to see Parseable in action with real workloads.

Talk to us about Enterprise if data ownership, BYOB, Apache Iceberg, or flexible deployment models are requirements for your organization. Visit parseable.com to start a conversation about Enterprise pricing and deployment options.

Your observability data is one of the most valuable assets your engineering organization produces. It tells you what went wrong, when, and why. It feeds capacity planning, incident post-mortems, and increasingly, machine learning models. Handing permanent custody of that asset to a third party made sense when there were no alternatives. There are alternatives now.