Zero Stack Observability: One Binary, Complete Visibility

How many components does your observability stack have? Not the number you put on the architecture slide for leadership, but the real count — every process, every dependency, every piece of infrastructure that has to be running for you to see what is happening in production.

If you are running the ELK stack, the answer is at least six: Elasticsearch nodes, Logstash, Kibana, and likely Kafka, Redis, and a curator process for index lifecycle management. If you built on ClickHouse, you have ClickHouse itself, Kafka, Zookeeper (or Keeper), a schema registry, an ingestion service, and whatever you bolted on for query and alerting. The Grafana ecosystem asks you to run Loki, Prometheus, Tempo, Grafana, and Alertmanager — five distinct systems, each with its own configuration language, upgrade cycle, and failure mode.

Every one of those components is a surface area for failure, a line item on your infrastructure bill, and a chunk of operational knowledge your team has to maintain. The more components in the stack, the more time you spend operating your observability system instead of using it. Zero stack observability is the architectural principle that asks: what if you could replace all of that with a single binary and an object store?

The Component Explosion Problem

Observability platforms did not start complex. The original ELK stack was genuinely three components. Prometheus was a single Go binary you ran next to your workloads. But as data volumes grew and reliability requirements tightened, the operational footprint of these systems expanded dramatically.

Here is what a production-grade deployment looks like today for each of the major stacks:

ELK Stack (Production)

Elasticsearch alone requires careful JVM tuning, shard management, and cluster health monitoring. It is not uncommon for organizations to have a dedicated team just for Elasticsearch operations.

ClickHouse-Based Stack

ClickHouse is fast, but it is not self-contained. Production deployments require a message queue for buffering, a coordination service for distributed state, and custom services for ingestion and query handling. Each layer introduces its own operational burden, as explored in our ClickHouse ClickStack vs. Parseable comparison.

Grafana Ecosystem

The Grafana stack has the additional complexity of being five separate projects with five separate release cycles, configuration formats, and upgrade procedures. Correlating data across Loki, Prometheus, and Tempo requires careful label consistency and templating work.

The Operational Tax

Every component in the stack carries an operational tax that goes far beyond provisioning a server and running a process.

Upgrades. When ClickHouse releases a new version, you cannot just upgrade ClickHouse. You have to verify compatibility with your Kafka consumer, your Zookeeper version, your ingestion service, and your query frontend. A single version mismatch can cause silent data loss or query failures. Teams often defer upgrades for months because the coordination cost is too high.

Failure modes. Each component introduces its own failure modes. Kafka partition leaders can get stuck. Zookeeper can lose quorum. Elasticsearch can hit split-brain scenarios. Redis can run out of memory. When your observability stack goes down during an incident, you are flying blind at the worst possible moment.

Resource consumption. Kafka brokers need fast disks and significant memory. Zookeeper requires low-latency networking. Elasticsearch demands heap tuning and careful garbage collection configuration. The aggregate resource footprint of a multi-component stack often exceeds the resources dedicated to the workloads being monitored.

Hiring. Operating a 15-component observability stack requires expertise in distributed databases, message queues, coordination services, JVM tuning, and infrastructure-as-code. That is a tall hiring requirement for teams that just want to search their logs and get alerts when something breaks.

The true cost of observability is not just the licensing or hosting bill — it is the engineering time consumed by operational overhead that delivers zero business value.

What "Zero Stack" Means

Zero stack observability is an architectural principle: minimize external dependencies to the absolute minimum required for a complete observability platform. For Parseable, that minimum is two things:

1. A single binary — the Parseable server
2. An S3-compatible object store — Amazon S3, MinIO, Google Cloud Storage, or Azure Blob Storage

That is it. No Kafka. No Zookeeper. No Elasticsearch. No separate metadata services. No Redis. No Curator. No schema registry. The single binary handles ingestion, parsing, indexing, querying, alerting, and serves the web UI (called Prism). The object store handles durable storage.

This is not a toy setup or a demo configuration. It is the production architecture. The same binary that runs on a developer's laptop runs in production handling terabytes of data per day.

How Parseable Achieves This

Eliminating external dependencies without sacrificing capability requires deliberate architectural choices. Here is what makes zero stack possible:

Rust for Performance Without a Runtime

Parseable is written in Rust. There is no JVM to tune, no garbage collection pauses to work around, no interpreted runtime to manage. The compiled binary runs with predictable memory usage and consistent latency. Rust's ownership model provides memory safety without the overhead of garbage collection — a critical property for a system that needs to ingest, process, and query data simultaneously.

Apache Arrow DataFusion for Query Processing

Instead of embedding a traditional database or building a custom query engine from scratch, Parseable uses ParseableDB — built on Apache Arrow DataFusion. DataFusion is a query engine designed for columnar data, which means it operates natively on the same format used for storage. There is no translation layer between the query engine and the data format, eliminating an entire category of complexity and overhead.

Apache Parquet on Object Storage

All data is stored as Apache Parquet files directly on object storage. Parquet provides 70-90% compression compared to raw JSON logs, columnar access for fast analytical queries, and universal compatibility with the broader data ecosystem. Because Parseable writes Parquet directly to S3 (or any S3-compatible store), there is no intermediate storage layer, no write-ahead log on local disk, and no replication protocol to manage.

This design is what makes Parseable function as an observability data lake — your telemetry data lives in an open format on storage you control, queryable by Parseable but also accessible to Spark, DuckDB, Athena, or any other tool that reads Parquet.

Native OpenTelemetry Ingestion

Parseable accepts data natively via OTLP endpoints — /v1/logs, /v1/traces, and /v1/metrics — without requiring a separate collector, gateway, or transformation service. Any OpenTelemetry SDK or collector can ship data directly to Parseable. No Kafka in between. No Logstash. No custom ingestion service.

Built-in UI, Alerting, and API

The Prism web interface, alerting engine, and REST API all ship inside the same binary. You do not install a separate Kibana or Grafana. You do not configure a separate Alertmanager. Everything a practitioner needs for day-to-day observability workflows is included.

Operational Comparison

The practical differences between zero stack and traditional multi-component architectures show up in everyday operations:

The backup story deserves emphasis. With traditional stacks, your data lives in Elasticsearch indices, ClickHouse tables, or Kafka topics — all formats that require component-specific backup tooling. With Parseable, your data is Parquet files on S3. It is already in the most durable storage tier available. There is no separate backup process because there is nothing to back up beyond S3 itself.

When Zero Stack Matters Most

Zero stack observability is valuable at any scale, but certain situations make it especially compelling:

Small teams and startups. If you have five engineers and no dedicated infrastructure team, you cannot afford to spend 20% of engineering time operating Elasticsearch. A single binary that just works lets you focus on your product. Parseable Pro at $0.39/GB ingested includes a 14-day free trial, making it accessible from day one.

Edge and resource-constrained environments. Running Kafka, Zookeeper, and ClickHouse on edge hardware is impractical. A single binary with a local MinIO instance gives you full observability capability in environments where a 15-component stack simply cannot fit.

Regulated industries. Fewer components means a smaller attack surface, fewer CVEs to track, and simpler compliance audits. When a security team asks "what processes handle our telemetry data?", the answer is one binary instead of a spreadsheet. Combined with Bring Your Own Bucket on the Enterprise plan, you get data sovereignty with minimal operational complexity.

Teams migrating from expensive SaaS. If you are moving off Datadog or Splunk, the last thing you want is to replace one form of complexity (vendor lock-in) with another (operational burden). Zero stack means you get self-hosted or BYOC deployment without the operational tax of traditional self-hosted stacks.

Try It in 60 Seconds

The simplest way to experience zero stack observability is to run Parseable locally with Docker. This single command starts the Parseable server with a local object store — no other dependencies required:

docker run -p 8000:8000 \
  parseable/parseable:latest \
  parseable local-store

That is one process. Open http://localhost:8000 to access Prism, the built-in web UI. Send logs via the REST API or configure an OpenTelemetry collector to ship to http://localhost:8000/v1/logs. Search, alert, and build dashboards — all from the same binary you just started.

For production deployments on your own infrastructure, point Parseable at an S3 bucket and you have a system that handles terabytes of daily ingestion with the same operational simplicity. For teams that prefer managed infrastructure, Parseable Cloud runs the same architecture without you managing anything at all.

The Bigger Picture: Observability as a Data Problem

Zero stack is not just about operational simplicity. It reflects a deeper shift in how the industry thinks about observability. When your telemetry data lives in Parquet on object storage, it stops being trapped in an observability silo and becomes part of your broader data lake architecture. Data engineers can query it with SQL. ML teams can train anomaly detection models on it. Security teams can run forensic analysis with tools they already know.

The SaaS observability model locks your data into a vendor's proprietary format and charges you to access it. Multi-component self-hosted stacks give you control but demand significant operational investment. Zero stack gives you both: control over your data with minimal operational overhead.

An observability lakehouse built on OpenTelemetry is the natural evolution — and a single binary architecture makes it practical for teams that do not have a dedicated platform engineering organization.

Get Started

If you are tired of operating your observability stack instead of using it, Parseable offers a path to simpler infrastructure without giving up capability.

• Self-hosted: Pull the Docker image or grab the binary from GitHub (AGPL-3.0 license). One binary, one S3 bucket, complete observability.
• Parseable Cloud Pro: $0.39/GB ingested, 365-day retention, AI-native analysis, and a 14-day free trial.
• Enterprise: Custom pricing starting at $15,000/year with BYOB (Bring Your Own Bucket), Apache Iceberg support, BYOC deployment, and premium support.

Stop managing your monitoring stack. Start using it. Try Parseable today.