Honeycomb Alternatives (2026): 9 Observability Platforms

Engineers and SREs leaving Honeycomb usually want one of four things: tooling that's native to OpenTelemetry, an open-source option, storage in their own cloud account, or a broader suite that adds logs, on-call and incident management. The nine options below cover those routes, in no particular order, grouped by approach.

Portability starts with OpenTelemetry

If your services already send traces through OpenTelemetry, you can point that data at a new backend without re-instrumenting your code. That's the whole value of the standard: the instrumentation belongs to you, not the vendor. A tool that wants its own agent first, and treats OpenTelemetry as an add-on, can quietly lock you in again.

Ask every vendor on your shortlist the same three questions:

• Does OpenTelemetry data go in natively, or through a translation layer?
• Is the vendor's own agent required for any feature you need?
• Can you export your telemetry in an open format if you leave?

A clear yes, no and yes means you can walk away later without touching your code. Anything vaguer is worth digging into during the trial.

Where your telemetry data sits

The second check is data location. Most observability platforms are hosted by the vendor: your traces, logs and metrics leave your infrastructure and live in their cloud. A few work differently. groundcover's page says it's deployed in your own cloud, and Coralogix's page says data is stored on your own cloud. Both are vendor claims, so ask exactly what runs where during a trial.

Telemetry often carries more than you'd expect, including user IDs, request details and internal hostnames. If security or contracts say that data mustn't leave your own account, the vendor-hosted tools drop out before you've compared anything else. If nobody minds, hosted tools save you running storage yourself. Rules on personal data vary by country, so check your regulator's guidance if traces include anything about your users.

Hosted or in your own cloud

Tool	Hosting	Good for
Axiom	Vendor hosted	Event store for machine data, schema-less ingest
Better Stack	Vendor hosted	Logs, traces, errors and on-call in one product
Chronosphere	Vendor hosted	Observability built for Kubernetes
Coralogix	Your own cloud (vendor claim)	Telemetry lake queried with unified syntax
Dash0	Vendor hosted	OpenTelemetry-native observability on open standards
groundcover	Your own cloud (vendor claim)	Full-stack observability inside your own cloud
Last9	Vendor hosted	Unified metrics, logs, traces and APM
SigNoz	Open source	Open-source APM, logs, traces and metrics on OpenTelemetry
Uptrace	Self-host option	OpenTelemetry-native tracing, metrics and logs

Open-source and OpenTelemetry-first tools

SigNoz is an open-source observability platform built on OpenTelemetry, covering APM, logs, traces and metrics. Uptrace is OpenTelemetry-native for tracing, metrics and logs, with a self-hosted edition. Dash0 builds on open standards such as PromQL, Perses and OTLP, so queries and dashboards stay in formats other tools understand.

These three suit teams that care most about not being locked in again. The open-source ones also let you run everything inside your own infrastructure, as long as someone on the team is happy to own upgrades and storage.

Telemetry stores built for volume

Axiom is an event store for machine data with schema-less ingest, so new fields arrive without a schema change. Chronosphere is built for Kubernetes, and its pitch centres on not keeping data you don't use. The telemetry lake from the data location section belongs in this group too.

Tools in this group make most sense when sheer volume is the problem: lots of services, lots of high-cardinality data and a need to keep it searchable. Ask how each one decides what to keep, what to sample and what to drop, and who controls those rules.

Full-stack suites

Better Stack puts logs, metrics, traces, error tracking, incident management and on-call in one product, which suits small teams that would rather not stitch several tools together. Last9 offers unified observability for metrics, logs, traces and APM. The own-cloud platform from the data location section is a full-stack option as well.

A suite means fewer vendors and one place to look during an incident. The trade is that each part may be less deep than a specialist tool, so test the part you lean on most.

What each platform is for

Listed A to Z, one line each.

Axiom

Best for: Teams sending large volumes of machine data who want an event store with schema-less ingest, so new fields don't need planning upfront.

Better Stack

Best for: Small engineering teams wanting logs, metrics, traces, error tracking, incident management and on-call together in one product.

Chronosphere

Best for: Platform teams running Kubernetes who want observability built for it, from a vendor focused on not keeping data you don't use.

Coralogix

Best for: Organisations that want telemetry stored on their own cloud and queried with one unified syntax, as the vendor describes its telemetry lake.

Dash0

Best for: Engineers committed to open standards who want OpenTelemetry-native observability built on PromQL, Perses and OTLP.

groundcover

Best for: Companies needing full-stack observability deployed in their own cloud, which the vendor pitches as monitoring everything without giving up data or control.

Last9

Best for: Developers and SREs needing one view of metrics, logs, traces and APM, in line with the vendor's pitch of full visibility.

SigNoz

Best for: Anyone wanting an open-source observability platform built on OpenTelemetry, covering APM, logs, traces and metrics in one place.

Uptrace

Best for: Self-hosters who'd like OpenTelemetry-native tracing, metrics and logs they can run themselves, with a self-hosted edition its page describes as free.

My advice for keeping your telemetry portable

Instrument with OpenTelemetry first and choose a backend second. If your code emits standard traces, metrics and logs, the backend becomes a decision you can revisit, and a move like this one turns into a configuration change rather than months of re-instrumentation.

Then be wary of every claim about volume and savings on these home pages. They're the vendors' own claims, measured on someone else's workload, and your traffic and retention needs are what decide how a tool behaves. For a broader view, my guides to observability tools and Datadog alternatives cover more suites, including bigger names.

The same-slow-request trial

1. Pick one service that matters, with real traffic, and send its OpenTelemetry data to two shortlisted tools at once.
2. Find one real slow request from the last day, using its trace ID or the time it happened.
3. Start a timer and see how fast each tool gets you from a dashboard to that exact trace.
4. Check whether you can see what made it slow: the database call, the downstream service or the code path.
5. Ask a teammate who's never used either tool to repeat the hunt, and note where they get stuck.
6. Write down where the trial data is stored and how you'd delete it.

The tool that finds the slow request faster, for whoever's on call at 3am, is the one to keep.

Do you make observability or tracing tools? Tool makers can ask to be considered for a featured place on this list.

Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.

Frequently asked questions

What does OpenTelemetry-native mean?

It means a tool accepts OpenTelemetry traces, metrics and logs directly as its main way in, rather than needing its own agent or converting the data first.

Can I switch observability tools without changing my code?

If your services are instrumented with OpenTelemetry, usually yes. You change where the data is sent, often in the OpenTelemetry Collector, rather than rewriting instrumentation.

What does bring your own cloud mean for observability?

The vendor's software runs in, or stores data in, your own cloud account, so telemetry stays inside infrastructure you control. Ask exactly which parts run where.

Is open-source observability harder to run?

It can be. You'll own hosting, upgrades, storage and scaling. In return you keep full control of your data and aren't tied to one vendor.

Do I need tracing as well as logs and metrics?

For anything with more than a couple of services, tracing is what shows you where a slow or failed request spent its time. Logs and metrics alone often leave you guessing.

How do I compare observability tools fairly?

Send the same service's data to two tools at once, then time how long each takes to find the same real problem. Feature lists won't tell you that.