Observability isn’t just about insight anymore, it’s about cost. As systems scale, telemetry (logs, traces, metrics) becomes one of the fastest-growing expenses for engineering teams. Platforms like Datadog and New Relic make it easy to get started, but hard to sustain. You pay for ingestion, retention, and dashboards & the bill grows as your traffic does.

Whereas CtrlB, by building on cloud object storage (like S3, GCS), makes telemetry cheaper, scalable, and queryable on demand without relying on expensive SaaS infrastructure or redundant hot storage.

Let’s explore how this approach works and why it’s reshaping how teams think about observability costs.

How does CtrlB cut costs compared to SaaS observability tools like Datadog?

SaaS platforms charge based on data volume ingested, not how much you actually use.
You pay every time a log line passes through their system, whether you look at it or not. That model might seem convenient early on, but at scale, it becomes painful.

With CtrlB, the economics flip.

• Your logs stay in your cloud object storage (S3, GCS, Azure Blob).
• CtrlB only applies compute when you query.
• You don’t pay for continuous ingestion, indexing, or replication.

This separation of storage and compute makes a big difference.
Teams using CtrlB typically reduce their observability spend by 60–80% compared to SaaS tools without deleting data or reducing visibility.

Because storage itself is cheap (as low as $0.02/GB/month on S3) and highly durable (11 nines), you can retain all your logs indefinitely while paying only for what you actually query.

How does CtrlB handle data spikes without “bill shocks”?

If you’ve ever experienced a production incident, you know what happens next: debug logs flood your pipelines, ingestion costs spike, and next month’s observability bill doubles.

This happens because most observability platforms charge at ingestion time. Every new log means more indexing, more storage, and more compute, even if it’s only relevant for a short investigation window.

CtrlB’s architecture prevents that.

• Incoming data is written directly to object storage, not an always-on cluster.
• The system builds micro-indexes dynamically, targeting only the relevant files instead of your entire dataset.

This means even during massive data surges, CtrlB’s costs remain predictable. You’re not paying for “hot” capacity you don’t use; you pay only when you query.

For example, an e-commerce platform running a festive sale can log terabytes of traffic data without worrying about scaling infrastructure or facing a surprise invoice.

What is intelligent data tiering, & How does CtrlB do it differently?

Traditional systems rely on “hot” and “cold” tiers:

• Hot storage: Expensive, but searchable instantly.
• Cold storage (like S3 or Glacier): Cheap, but not queryable without rehydration.
  

CtrlB eliminates the hard divide between the two.

All data lives in object storage, but CtrlB automatically tiers it. For example:

• Recently written logs stay “warm” with lightweight micro-indexes for fast search.
• Older logs stay fully in object storage, but with index metadata stored separately for quick retrieval.
  

When a query spans multiple tiers, CtrlB’s control plane automatically routes it, fetching only what’s relevant.

You don’t need to maintain pipelines or rehydrate data manually. Whether logs are from last night or last quarter, they remain searchable within seconds.

What strategies help manage log volume in high-scale environments like e-commerce?

E-commerce systems generate huge, bursty telemetry loads, checkout logs, payment gateway traces, search analytics, promotions, and fraud monitoring. The challenge isn’t just storing all of this; it’s storing it efficiently.

Here’s how teams can optimize storage using CtrlB and cloud object storage:

1. Adopt schema-on-read instead of schema-on-write.
   Let your logs flow directly to storage in their native structure. CtrlB gives you results dynamically at query time, no rigid schemas or upfront transformations needed.
2. Use Parquet-based indexing for compression and query speed.
   CtrlB stores and processes data in columnar formats like Parquet, which compresses well and enables fast, selective scans. This keeps storage efficient and queries fast.
3. Retain everything, but prioritize access.
   Define policies: recent logs (<30 days) get lightweight indexing; historical data stays untouched until queried. This ensures cost balance without losing long-term visibility.
4. Avoid re-ingestion loops.
   Don’t build separate ETL jobs to rehydrate logs for analysis. CtrlB reads directly from object storage, which means your pipelines stay simple and your costs are predictable.
   

These practices make it possible for even data-heavy platforms to maintain deep observability without storage bloat or operational overhead.

How does this change the way teams think about observability architecture?

Traditional observability tools are built around control through ingestion; they want your data to live inside their platform.
CtrlB reverses that. It treats your cloud as the observability backbone, not an external dependency.

Instead of being locked into a SaaS storage tier, you control:

• Where your data lives
• How long has it been retained
• When and how it’s queried

That’s a major shift. Observability becomes a system design choice, not a line item on your monthly bill.

Why cloud object storage is the future of observability

Cloud storage has already replaced disks for backup, and analytics observability is next.
By combining:

• Durability (99.999999999% reliability)
• Elastic scaling
• Pay-per-query compute
• Native log correlation

CtrlB makes cloud object storage behave like a high-performance observability lake.

You no longer have to decide between visibility and affordability.
You get both infinite retention, real-time search, and predictable cost.

In Summary

Most observability platforms make you choose between retention and cost. CtrlB lets you have both by treating cloud object storage as a first-class citizen, not an archive.

You don’t need to delete old data, rebuild pipelines, or fear data spikes.
With CtrlB, you can store everything, query instantly, and scale observability the same way cloud storage scales: cheap, elastic, and infinite.

FAQs

1. How is CtrlB different from Datadog or other SaaS tools?
SaaS tools charge per ingested GB and store data in their infrastructure. CtrlB stores data in object storage, charges only for query compute, and gives you full control over retention.

2. Can I use CtrlB for both recent and old data?
Yes. CtrlB’s micro-indexing allows you to query both recent and historical logs directly from object storage with sub-second latency.

3. How does CtrlB handle large-scale or spiky traffic?
It scales compute elastically during traffic bursts, spins up compute to process queries, then scales down automatically. You never pay for idle capacity.

4. Is data transformation required before storing logs in S3?
No. CtrlB supports schema-on-read. You can store raw JSON or structured logs. CtrlB interprets them dynamically at query time.