Observability has come a long way, from proprietary agents and fragmented tooling to a new era where open standards dictate the future. At the forefront of this transformation is OpenTelemetry (OTel), the CNCF-incubated open-source standard that is redefining how telemetry data is collected, processed, and exported. OpenTelemetry isn’t just a new technology; it’s a forcing function, compelling observability vendors to compete on true innovation rather than vendor lock-in.

Breaking Free from Vendor Lock-In

For years, observability vendors thrived on lock-in. If you wanted deep insights into your application, you had to use a vendor-specific agent, store data in their proprietary format, and rely on their querying language. Switching providers meant ripping out your entire observability stack, an expensive proposition.

OpenTelemetry changes this paradigm by introducing a vendor-neutral telemetry standard. Now, organizations can instrument their applications once and send logs, metrics, and traces to any backend of their choice (as long as it is OTel compatible). This forces vendors to compete not on how well they lock you in, but on:

• Return on investment: Who offers the best value for money without sacrificing quality?

• Performance: Who can process and query data faster?

• User experience: Who makes telemetry easier to manage and explore?

Open Standards as a Catalyst for Innovation

History has shown that open standards drive industry-wide progress. Consider OCI (Open Container Initiative), which standardized container runtimes and gave rise to an explosion of innovation in Kubernetes, serverless computing, and edge deployments. Before OCI, each cloud provider and container platform had its own way of running containers. Once OCI provided a common runtime standard, companies competed on orchestration, security, and developer experience rather than reinventing the wheel.

OpenTelemetry is having a similar effect on observability. As organizations standardize their telemetry collection using OTel, they can experiment freely with different observability backends. This means vendors must continuously innovate on querying efficiency, scalability, and cost reduction rather than relying on data silos to retain customers.

Here’s a Hot Take from the Orange Website We All Love

A particularly spicy thread on Hacker News summed it up well:

“Two problems with OpenTelemetry: (1) It doesn’t know what the hell it is. (2) No one from OpenTelemetry seems to have actually tried using OpenTelemetry.”

Ouch. But fair.

One user even abandoned their OpenTelemetry integration after realizing that something as simple as a heartbeat signal was shockingly hard to implement. Another lamented that defining a histogram's buckets near the actual metric definition was clearly too convenient; instead, you have to pass overrides to a global exporter. Because… reasons.

My hot take on “what is Otel?” Thing that’s simultaneously a protocol, a library, a standard, a philosophy, and possibly an elaborate prank on anyone trying to instrument their code.

If you’ve ever thought, “Wait, do I need to send my logs, traces, and metrics separately? Or together? Or should I just cry?”—congrats, you’re not alone.

But let’s be real, every groundbreaking tech standard has had growing pains. Remember when Kubernetes first dropped, and half the industry was asking, “Wait, I need how many YAML files to deploy a web app?” And now, we can’t live without it. OpenTelemetry is on the same path: messy at first, but destined to be everywhere. Resistance is futile.

And that’s exactly why Parseable + OpenTelemetry is a powerful combination, because if you’re going to endure the pain of instrumenting everything with OTel, you should at least get the gain of a scalable, cost-effective, and open storage backend. Parseable runs on your infrastructure, giving you complete control over your telemetry data without vendor lock-in. Built with a high-performance, less memory intensive Rust-based database, Parseable is optimized for low compute overhead while delivering fast ingestion and querying on S3. This means massively reduced costs compared to traditional observability platforms that burn through resources. So, is OpenTelemetry worth the effort? With the right backend, absolutely.

How Parseable’s Fast Observability on S3 Complements OpenTelemetry

At Parseable, we built Fast Observability on S3 with the same philosophy that underpins OpenTelemetry: portability, cost efficiency, and performance.

• Cost-Effective Storage: Unlike traditional observability platforms that charge per GB or retention period, Parseable uses S3 as the primary data store, making observability significantly more cost-effective.

• Fast Queries: We leverage columnar storage and indexing on demand, meaning you only index what you need, keeping resource usage minimal.

• Search: Parseable offers serverless full-text and vector search, allowing you to instantly find relevant logs, metrics, and traces without unnecessary overhead.

• Portable and Open: Since Parseable speaks OpenTelemetry natively, you can send logs, metrics, and traces from your applications without vendor-specific agents.

Getting Started: Using OpenTelemetry with Parseable

For complete setup instructions, visit our official documentation: Parseable OpenTelemetry Integration Guide.

For best practices and real-world examples, check out our blogposts

1. Step-by-Step Guide to Setting Up PostgreSQL Observability with FluentBit and OpenTelemetry

2. Ingest OTEL Logs with Grafana Alloy & Parseable

Moving Beyond Reactive Observability

The days of relying on a separate ops team for observability are fading, modern engineering teams own their code in production. OpenTelemetry aligns with this shift by enabling developers to instrument and monitor their applications directly, making observability a first-class part of the development workflow. But beyond ownership, observability must evolve beyond static dashboards and reactive alerting. Traditional tools focus on predefined graphs and thresholds, often missing unexpected issues. Instead, engineering teams need interactive, query-driven observability that helps them anticipate and prevent failures rather than just reacting to them.

This is where MCP (Model Context Protocol) servers and LLM-driven automation are changing the game. These systems analyze telemetry data in real-time, detecting anomalies before they escalate, whether it's an impending database overload, inefficient resource usage, or application slowdowns. OpenTelemetry plays a crucial role here by standardizing data collection across logs, metrics, and traces, ensuring these insights are portable, vendor-neutral, and actionable.

The Future of Observability is Open

At Parseable, we are building modern observability powered by open standards. OpenTelemetry is reshaping the industry, forcing vendors to compete on real value instead of lock-in. Pairing it with Parseable’s Fast Observability on S3 ensures organizations get cost-effective, scalable, and portable telemetry ingestion without being trapped by proprietary solutions.

Start Using Parseable

ParseableDB is open-source under the AGPL-3.0 license, so don’t just take our word for it—give it a try! To get started with enterprise version, reach out to us at hello@parseable.com for a demo, and we’ll help you get started and running.

Want to learn more? Check out our documentation