OpenObserve vs SigNoz: Which Open Source Observability Platform Should You Choose? (2026)

TL;DR

OpenObserve vs SigNoz comes down to one architectural choice: where your telemetry lives. Both are open-source, OpenTelemetry-native platforms covering logs, metrics, traces, dashboards, and alerts. SigNoz stores everything in ClickHouse on disk; OpenObserve stores everything as compressed Parquet on object storage (S3, GCS, Azure Blob, MinIO) with stateless compute, and that one decision drives most of the differences below.

• Self-hosting: OpenObserve is a single binary, no database to run. SigNoz means operating a ClickHouse cluster (plus ClickHouse Keeper for coordination and PostgreSQL for metadata), including known ClickHouse pain points at observability's ingest volume: merge pressure from streaming inserts, slow async mutations for deletes, and compute and storage scaling together instead of independently.
• Retention cost: object storage runs at commodity rates, so months of history doesn't mean provisioning more disk the way it can on ClickHouse.
• Query language: standard SQL and PromQL on OpenObserve; a query builder plus ClickHouse's own SQL dialect on SigNoz.
• RUM: OpenObserve ships session replay built in; SigNoz covers Web Vitals only.
• Fair to SigNoz: it's genuinely open source and OTel-native, and a team already fluent in ClickHouse SQL will be productive with it.

The rest of this guide backs each point with numbers: architecture, pricing, self-hosting overhead, RUM, query language, licensing, and migration.

OpenObserve vs SigNoz at a Glance

If you're evaluating a broader field of tools rather than a head-to-head, open-source APM tools and top open-source observability tools cover more of the OpenTelemetry-native landscape SigNoz and OpenObserve both sit in.

What Is SigNoz?

SigNoz is an open-source, OpenTelemetry-native observability platform that unifies logs, metrics, and distributed traces (APM) in a single interface, built as a self-hostable alternative to Datadog and New Relic. It's built on two core technologies: OpenTelemetry for collection and instrumentation, and ClickHouse as the storage and query engine underneath everything, logs, traces, and metrics alike.

SigNoz's strengths are real: a polished APM experience with service maps and flame graphs, an approachable query builder for common questions, and the option to drop into raw ClickHouse SQL when the builder isn't enough. SigNoz Community Edition is MIT-licensed and self-hostable with no data caps; SigNoz Cloud is the managed offering.

What Is OpenObserve?

OpenObserve is an open-source observability platform built in Rust that ingests logs, metrics, traces, real user monitoring (RUM), and LLM telemetry into a single backend, all OpenTelemetry-native over OTLP. The architectural bet OpenObserve makes is different from SigNoz's: instead of a database cluster, telemetry is written as compressed Parquet files directly to object storage, S3, GCS, Azure Blob, or self-hosted MinIO, with compute nodes that are stateless and scale independently of storage.

That single design decision is what separates almost every other comparison point in this guide: operational overhead, long-term retention cost, and scaling model all trace back to "object storage versus a database cluster."

OpenObserve vs SigNoz: Architecture (Object Storage vs ClickHouse)

This is the one difference that actually matters most for how each platform behaves in production.

SigNoz runs on ClickHouse. ClickHouse is a genuinely fast columnar database, and it's a defensible choice for high-cardinality trace and log queries; that speed is real and not marketing. But it's still a database with a specific set of operational characteristics that observability workloads tend to hit hardest:

• Merge pressure from streaming inserts. ClickHouse's MergeTree engine writes data in parts and merges them in the background. Observability pipelines insert continuously, in small batches, from OTel collectors, which is close to the worst-case insert pattern for MergeTree. Under-batched or high-frequency inserts can pile up parts faster than the background merges clear them, and ClickHouse will start rejecting inserts with a "too many parts" error until the backlog clears. Avoiding this means deliberately tuning collector batching and merge settings, not something that happens by default.
• Async mutations for updates and deletes. ALTER TABLE ... DELETE and UPDATE in ClickHouse are mutations: they run as background jobs that rewrite affected parts, not an immediate in-place write. A routine operation elsewhere, a GDPR deletion request, redacting a field you shipped by mistake, backfilling a corrected value, is comparatively slow and resource-intensive on ClickHouse, and can compete with your regular query and merge load while it runs.
• Compute and storage scale together. In a standard self-hosted ClickHouse deployment, a node holds both the data and the query engine that reads it. Need more retention but the same query throughput, or more query concurrency but no extra data? Either way you're adding nodes to the same cluster, because the two dimensions aren't separated. Advanced configurations can tier cold data to S3, but that's additional setup layered on top of the default, not the default itself.

None of this makes ClickHouse a bad database, it's a fast one, purpose-built for analytical queries. It does mean self-hosted SigNoz means someone on your team is, in practice, a ClickHouse operator: watching part counts, tuning merges, sizing disks, and planning shard growth, with ClickHouse Keeper for cluster coordination on top. If you're weighing ClickHouse itself as an observability backend rather than through SigNoz specifically, OpenObserve vs ClickHouse covers that trade-off directly.

OpenObserve runs on object storage. There's no database to operate. Ingested data is compressed into Parquet files and written straight to your object storage bucket; a lightweight metadata layer indexes what's there. Compute nodes that handle ingestion and queries are stateless: you add or remove them without rebalancing anything, because they don't hold the data, the bucket does. Retention is a matter of how much you're willing to keep in object storage, which is priced at commodity rates (roughly $0.023/GB on S3-class storage) rather than provisioned database disk.

The practical result: a SigNoz cluster that's been running for a year has an operational history, disks that have been resized, maybe a shard rebalance or two. An OpenObserve deployment a year in looks the same as it did on day one, because the storage layer was never something you managed.

OpenObserve vs SigNoz: Feature Comparison

Both platforms cover the same core observability surface. The differences show up in how deep each goes and what ships built in.

Two rows are worth expanding on.

Pipelines. SigNoz's ingest-time processing goes through OpenTelemetry Collector processors, which is standard and works, but it's a separate configuration surface from the platform itself. OpenObserve builds VRL-powered pipelines directly into the product for logs, metrics, and traces, so parsing, enrichment, redaction, and routing happen in one place without maintaining a second processing layer.

RUM and session replay. SigNoz's frontend monitoring covers Core Web Vitals via OpenTelemetry, which tells you a page was slow but not what the user was doing when it happened. OpenObserve's RUM module includes full session replay and error tracking, so a frontend investigation can go from "this page's LCP regressed" to watching the actual session, the same signal covered in RUM frustration signals.

OpenObserve vs SigNoz: Pricing Compared

Pricing is where the architecture difference turns into a dollar figure.

SigNoz Cloud (Teams plan) starts at $49/month, which buys a usage credit, after which you pay:

• $0.30/GB for logs and traces ingested
• $0.10 per million metric samples
• 15-day default retention on logs and traces (extending retention costs more)

SigNoz Community Edition (self-hosted) has no license fee and no data caps; you pay for your own infrastructure, meaning the ClickHouse cluster's compute, disk, and network egress.

OpenObserve Cloud bills usage directly with no separate credit tier:

• $0.50/GB ingested
• $0.01/GB queried
• 14-day free trial, no credit card required

OpenObserve self-hosted is free under AGPL for the core, and the self-hosted enterprise edition is free up to 50 GB/day of ingestion, which includes SSO, RBAC, and sensitive data redaction, features that are usually paywalled entirely on other open-core platforms.

The structural point: SigNoz's per-GB rate looks competitive at a glance, but it's billed against a database that needs disk provisioned ahead of ingestion, and the 15-day default retention on cloud logs and traces means "keep six months of data" is a different, more expensive conversation. OpenObserve's object-storage design means extending retention doesn't require re-provisioning anything, self-hosted or cloud.

Is SigNoz Really Open Source?

Yes, with the open-core caveat that applies to most companies in this category. SigNoz Community Edition is MIT-licensed, genuinely open source, and self-hostable with no license fee and no data caps. Some advanced features live in SigNoz Cloud or an enterprise tier rather than the open-source core, which is a normal open-core pattern, not a criticism unique to SigNoz.

OpenObserve's core is open source under AGPL-3.0. The distinction worth knowing: OpenObserve's self-hosted enterprise features (SSO, RBAC, sensitive data redaction) are free up to 50 GB/day of ingestion before a paid tier kicks in, which is a wider free enterprise allowance than most comparable open-core platforms offer, SigNoz included.

OpenObserve vs SigNoz: Which Is Easier to Self-Host?

If you're planning to self-host, this is usually the deciding factor.

Self-hosted SigNoz is a coordinated set of services, even at the single-node level: ClickHouse for storage, ClickHouse Keeper for coordination, PostgreSQL for metadata, the bundled SigNoz application (frontend, API server, ruler, and alertmanager in one binary), and an OpenTelemetry Collector for ingestion. Each has its own health checks, upgrade path, and failure mode. Scaling to HA means turning on ClickHouse's replicated configuration and adding Keeper nodes for quorum, so sharding, replica management, disk sizing, and rebalancing become your responsibility as data grows.

Self-hosted OpenObserve ships as a single binary you can run with one command, or a single container. There is no external database: OpenObserve embeds its own storage engine and writes to local disk or your own object storage bucket directly. Scaling to an HA cluster via Helm on Kubernetes separates compute from storage, so you add or remove stateless nodes without touching a schema or rebalancing a shard.

For a small team, that's the difference between "install and run" and "stand up and operate a distributed database."

OpenObserve vs SigNoz: Query Language

SigNoz gives you a query builder for the common cases (filter by service, span name, status code) and drops you into raw ClickHouse SQL for anything the builder doesn't cover. ClickHouse SQL is powerful but is its own dialect, with its own functions and table layout to learn, on top of standard SQL.

OpenObserve uses standard SQL for logs and traces and PromQL for metrics, no platform-specific query language. If your team already writes SQL and knows PromQL from Prometheus or Grafana, there's nothing new to learn to start querying OpenObserve. If your team is already fluent in ClickHouse SQL, that specific edge disappears, but for most teams evaluating both platforms, standard SQL is the shorter path to a working query.

Migrating From SigNoz to OpenObserve

Because both platforms are OpenTelemetry-native, migrating is mostly a configuration change, not a re-instrumentation project.

1. Repoint your OpenTelemetry Collector. Your applications are already instrumented with OTel. Add an OTLP exporter for OpenObserve to your existing Collector config and dual-ship to both platforms while you validate that traces, logs, and metrics match.
2. Rebuild dashboards and alerts. Recreate your key SigNoz dashboards using standard SQL and PromQL in OpenObserve, and port alert rules over. Any ingest-time processing built on OTel Collector processors becomes an OpenObserve VRL pipeline.
3. Cut over and retire the ClickHouse stack. Once OpenObserve's numbers match what SigNoz reported, shift traffic fully, then decommission ClickHouse, ClickHouse Keeper, PostgreSQL, and the rest of the SigNoz services. Long-term history moves to object storage instead of disks you have to keep provisioning.

There's no proprietary agent to rip out on either side, which is the advantage of both platforms committing to OpenTelemetry rather than a custom collection format.

OpenObserve vs SigNoz: Which Should You Choose?

Choose SigNoz if

Your team already runs ClickHouse (or wants to) and is comfortable operating it, you value a purpose-built query builder plus the option to write raw ClickHouse SQL for APM-style analysis, and a multi-service deployment isn't a concern because you already have the platform expertise on staff.

Choose OpenObserve if

You want a single binary with no external database to operate, object-storage economics so extending retention doesn't mean provisioning more disk, standard SQL and PromQL instead of a platform-specific query dialect, and built-in RUM with session replay rather than Web Vitals alone. For teams that want observability without taking on a database operations project, OpenObserve is the more direct path.

Both, if you're still deciding

Both are open source and OpenTelemetry-native, so there's little switching cost to try either against a real workload: point the same OTel Collector at both, ingest a few days of production traffic, and compare query latency, storage growth, and how much operational attention each one needs before you commit. That side-by-side is the fastest way to answer "OpenObserve vs SigNoz" for your specific stack instead of a generic one.

Try OpenObserve Cloud

If object storage economics and a single binary sound better than operating a ClickHouse cluster, the fastest way to see the difference is to send your own telemetry to both and compare. Start a free OpenObserve Cloud trial with no credit card, or self-host the single binary under AGPL. Point your existing OpenTelemetry Collector at it and your logs, metrics, and traces are queryable in minutes.

• Try OpenObserve Cloud: free tier, no credit card required
• See the full SigNoz alternative comparison: feature matrix, migration steps, and pricing calculator
• Read open-source APM tools: where SigNoz and OpenObserve sit in the wider OTel-native landscape
• Also evaluating other tools? See how OpenObserve compares to Grafana, ClickHouse, and Elasticsearch