The whole point of OpenTelemetry is that this decision is reversible. Instrument once with the open standard, and the backend (the thing that stores your traces, logs, and metrics and answers questions about them) becomes a config value: an OTLP endpoint and a token, or a collector pipeline for the multi-store stacks. That is true at the wire. Dashboards, alerts, saved queries, and muscle memory migrate more slowly, which is why the choice still deserves an afternoon of thought, and why backends compete hard on what they actually do with your telemetry once it arrives.
This post compares the OpenTelemetry backends that matter in 2026: how each one models your data, what querying it feels like, how each one meters usage, and (increasingly the deciding question) how it handles applications with LLMs and agents in the request path. The scope is OTLP-native backends for application telemetry; AI-specific eval platforms are a different comparison.
What actually differs between OTLP backends
Every backend on this list accepts standard OTLP. The differences that matter day to day:
Cross-signal querying. Storage architecture aside, the practical question is whether one query can span traces, logs, and metrics. Some backends expose one queryable surface; others run a separate store and query language per signal. This decides whether "what did the requests behind these slow spans log?" is one query or a tab-switching exercise.The query ceiling. Filter bars and fixed aggregates answer the questions someone predicted. SQL-class querying answers the question nobody predicted. You find out which you have during your worst incident.High-cardinality tolerance. User IDs, tenant IDs, and prompt hashes are exactly the attributes you want to group by, and exactly the ones that punish metrics-first designs.The meter. Per-GB, per-host, per-span, per-seat, or one per-record meter: the shape of the meter determines whether your bill is predictable and what you'll be tempted to stop collecting.GenAI awareness. OpenTelemetry'ssemantic conventions for generative AIcover model names, token counts, and tool calls, but they are young: they recently moved to a dedicated repository, attribute names are still shifting, and instrumentations typically make prompt and response content capture opt-in because it contains user data. Backends therefore differ on how much normalizing they do and whether an LLM span renders as a first-class conversation or as a generic blob of attributes.Self-hosting and residency. If telemetry must stay on your infrastructure, the field narrows fast.Logfireoffers self-hosting onEnterprise; below that tier the realistic options are the open-source backends.Breadth beyond application telemetry. RUM, synthetic monitors, and continuous profiling are enterprise-APM territory; the focused backends on this list skip most of that surface by design. If you need all of it in one contract, that pulls toward the incumbents.
The best OpenTelemetry backends in 2026
For most teams the decision resolves quickly; the interesting question is which constraint would change the answer. Pydantic Logfire: best OpenTelemetry backend overall
Pydantic Logfire (that's us) is an OpenTelemetry backend from the team behind Pydantic. Traces, logs, and metrics land in one store, on one meter: the free plan includes 10 million records a month, and the Team and Growth plans bill overage at a single per-record rate. Querying is PostgreSQL-compatible SQL with joins, subqueries, and window functions, so a slow trace joins to the log lines emitted inside it in one statement. Supported LLM and agent instrumentation renders conversations and tool calls, with token and cost details where the instrumentation emits them; Pydantic AI, OpenAI, and Anthropic SDK instrumentation is maintained in-house. Self-hosting is available on Enterprise; the Personal, Team, and Growth plans are managed-only.
Grafana (Tempo, Loki, Mimir): when your dashboards already live in Grafana
The Grafana stack pairs Tempo for traces, Loki for logs, and Mimir/Prometheus for metrics. All of it is open source and OTLP-capable; Grafana Cloud is the managed path, with a free tier, and adds GenAI observability dashboards. If your dashboards already live in Grafana, extending to OTel data is the path of least resistance. The structural cost is three stores and three query languages (TraceQL, LogQL, PromQL): correlating across signals happens in the dashboard layer rather than in one query, and operating the trio yourself is real platform work.
SigNoz: if you must self-host and will operate ClickHouse
SigNoz is an open-source backend for traces, logs, and metrics in one self-hosted app: ClickHouse-based, OTLP-native, with APM views, dashboards, and alerting included, and it documents LLM observability over the GenAI semantic conventions. The core is open source with commercial enterprise features on top. You operate ClickHouse and the ingestion pipeline yourself (or use their cloud), and ad-hoc analysis beyond the query builder means writing ClickHouse SQL against their schema.
Jaeger: for traces only, at zero license cost
Jaeger is the CNCF-graduated tracing backend, and Jaeger v2 is built on the OpenTelemetry Collector framework, with Service Performance Monitoring views derived from span metrics. Paired with Prometheus and Grafana it is the classic zero-license-cost stack: right for local development, strict data-residency setups, and teams that want the smallest possible moving part. It is tracing-focused, though. Logs, cross-signal correlation, LLM-aware views, and cost accounting are assembly required.
Honeycomb: when your team already debugs in wide events
Honeycomb treats traces and logs as wide events for the "group by any attribute" investigation style. It accepts OTLP for traces, metrics, and logs, and pricing meters events, metric data points, and telemetry processing separately. The query model is proprietary end to end, so the skills and saved questions you build are Honeycomb-shaped.
Dash0: if you want PromQL against managed OTel storage
Dash0's narrow case against the rest of this list is Prometheus-native querying in a managed backend: PromQL over OTel data, with published per-unit rates. It is a managed service with no self-hosted edition listed, so confirm deployment options if residency on your own hardware matters, and as the youngest product here its workflow depth is worth evaluating against your incident practice before standardizing.
OpenObserve: for single-binary self-hosting, where AGPL fits
OpenObserve is a single-binary Rust backend that ingests traces, logs, and metrics over OTLP with object-storage-backed persistence. It is a fit when you want self-hosted and small-footprint without operating a ClickHouse cluster. Two things to know: the license is AGPL-3.0 (relicensed from Apache 2.0 in 2023) with a commercial edition on top, and the investigation workflows are younger than the incumbents', so trial your real workflows against it first.
Datadog, New Relic, Dynatrace: where the contract already exists
The enterprise APMs all ingest OTLP alongside their proprietary agents, ship their own LLM observability products, and cover the breadth (RUM, synthetics, profiling, security) that focused backends deliberately skip. If your company already runs one, sending OTel data into the existing pane keeps AI telemetry next to infrastructure and incident tooling. Two things to check before defaulting here: OTel-ingested data does not always get feature parity with agent-collected data (Datadog's OTLP metrics intake expects delta temporality while most SDKs default to cumulative, for example), and the pricing models (per-host for some vendors, per-GB and per-user for others, with per-feature SKUs on top) are exactly the spreadsheet the single-meter backends exist to avoid.
Also worth evaluating, outside this post's scope: Elastic's OpenTelemetry distribution, ClickHouse's ClickStack (HyperDX), and Uptrace.
Comparison at a glance
| Backend | Meter | Cross-signal querying | Query surface | Self-host |
|---|---|---|---|---|
| Pydantic Logfire | One per-record meter | One SQL surface | PostgreSQL-compatible SQL | Enterprise only |
| Grafana stack | Usage-based cloud or free OSS | Dashboard-layer correlation | TraceQL + LogQL + PromQL | Yes |
| SigNoz | Usage-based cloud or OSS | One app, per-signal tables | Builder + ClickHouse SQL | Yes |
| Jaeger | Free OSS | Traces (+ span metrics) | UI filters + PromQL | Yes |
| Honeycomb | Per event + metric points | Events (+ metrics) | Proprietary query UI | Private Cloud (AWS) |
| Dash0 | Per unit, published rates | One store | PromQL + SQL | No |
| OpenObserve | OSS (AGPL) or cloud | One store | SQL-like + UI | Yes |
| Datadog / New Relic / Dynatrace | Per host, GB, or user (varies) | Cross-product views | Proprietary per product | No (except Dynatrace Managed) |
Rates and capabilities above were last verified August 2026; meters change, so check the linked pricing pages.
How to choose
Default: Logfire. One store, one meter, SQL across traces, logs, metrics, and LLM spans, and a free tier big enough to prove it on your real workload before anyone signs anything.Only if your dashboards already live in Grafana and you can staff three query languages: Tempo/Loki/Mimir.Only if self-hosting is a hard requirement: Logfire Enterprise keeps the same product on your infrastructure; going open source instead, SigNoz if you will operate ClickHouse, OpenObserve if you want the smallest footprint and AGPL fits, or Jaeger + Prometheus if traces alone are enough.Only if your team already debugs in the wide-events style and will accept a proprietary query model: Honeycomb.Only if PromQL in a managed backend is the requirement: Dash0.Only if the enterprise-APM contract already exists: send OTLP there, and verify feature parity before renewing.
When the backend choice matters less than you think
Because OpenTelemetry made switching cheap at the wire, the worst outcome isn't picking the "wrong" backend. It's delaying instrumentation while you compare them. Instrument with OTel now, point it at whichever backend fits today (Logfire's 10 million free records a month exist for exactly this experiment), and let real usage tell you whether the query ceiling, the bill, or the LLM support becomes the constraint. The instrumentation is the asset; the backend is the part you can renegotiate.
Frequently asked questions
What is an OpenTelemetry backend? The service that receives OTLP data from your instrumented applications and collectors, stores it, and provides the querying, dashboards, and alerting on top. The instrumentation layer is standardized; the backend is where vendors compete.
Can I switch backends later? Yes, and far more cheaply than in the proprietary-agent era: standard OTel instrumentation moves to a new backend by repointing the OTLP exporter endpoint and token; for the multi-store stacks the change lives in the collector pipeline instead, where each signal can need its own endpoint and credentials. What you rebuild is the backend-side configuration: dashboards, alerts, saved queries, and any sampling or temporality settings the new backend expects.
Which backend is best for LLM and agent applications? Most backends on this list now advertise GenAI support in some form: token dashboards, cost tracking, agent views. The questions that separate them are whether an agent run renders as a conversation with tool calls or as a list of raw spans, whether LLM telemetry is correlated with the surrounding application trace or lives in its own view, and what you can ask after the fact. On those tests Logfire is the strongest answer on this list: supported instrumentation renders as conversations with tool calls, and the surrounding application trace is one SQL join away. The best Sentry alternatives guide covers the adjacent error-tracking decision.
Do I need a commercial backend at all? No, but read the licenses before assuming "open source" means the same thing everywhere: Jaeger and the Grafana OSS components are permissively licensed, SigNoz is open source at the core with commercial enterprise features, and OpenObserve is AGPL-3.0 with a commercial edition. You trade a bill for operations work; the right answer depends on which is scarcer for your team. There is also a third option: Logfire's free plan is managed, includes 10 million records a month, and has no license to read.
Try Pydantic Logfire free Point any OTLP exporter at Logfire and query your traces, logs, and metrics with SQL. The free plan includes 10 million records a month. Get started in a few lines, or see the APM overview for the instrumentation story.