Both products are OpenTelemetry-native. Logfire adds the rest of the production AI loop: full-stack traces, datasets, evaluators, human review, quality SLOs, and improvement workflows, with PostgreSQL-compatible SQL that developers and coding agents can use directly.
| Feature | Logfire | Honeycomb |
|---|---|---|
| OpenTelemetry | Native, no proprietary agent | Native, no proprietary agent |
| Query interface | SQL (PostgreSQL syntax) as text | Visual Query Builder with SQL-shaped clauses, plus a natural-language assistant |
| Evals & datasets | Datasets, code and LLM-judge scorers, experiments, human annotation, live evals | Not offered |
| Free tier | 10M records/month | 20M events/month |
| Usage pricing | Personal: 10M records free; Team: $49/month + $2/M over 10M | $150 Pro entry; $3/M events of monthly capacity, with a separate metric-point allowance |
| Seats | 1 on Free; 5 on Team ($49/mo), $25 each beyond; unlimited on Growth ($249/mo) | Unlimited on every plan |
| SLOs | SLIs from any SQL predicate, including AI quality | Mature SLO product; not on Free, 2 on Pro |
| Illustrative monthly workload | Logfire | Honeycomb |
|---|---|---|
| Free cloud allowance | 10M records/month | 20M events + 100M metric points/month |
| 50M trace spans/month | $129 Team (30 days) · $329 Growth (up to 90 days) | $150 Pro (60 days) + 250M metric points |
| 750M trace spans/month | $1,529 Team (30 days) · $1,729 Growth (up to 90 days) | $2,250 Pro (60 days) + 3.75B metric points |
| Billing shape | $2/M records above 10M | $3/M events of chosen monthly capacity; sustained overage can throttle |
Calculation scope
Honeycomb's 2026 monthly event-capacity pricing for trace-only workloads at the published 50-million-event entry tier and 750-million-event upper Pro boundary. Metric data points remain a separate included allowance.
Basis: USD; public monthly list price. Prices checked .
The trace-only workload fits within both vendors' free allowance; Honeycomb's Free plan has another 10 million events and 100 million metric points of headroom.
10M records are within the 10M Personal allowance $0 10M trace events are within the 20M event allowance $0 Honeycomb's entry Pro tier is compared with both Logfire plans because the included retention periods differ.
$49 $49 (50M − 10M included) ÷ 1M × $2 $80 $249 $249 (50M − 10M included) ÷ 1M × $2 $80 50M events × $3/M events $150 The upper published Pro boundary is calculated from Honeycomb's $3-per-million event rate; included metric capacity is not converted into trace value.
$49 $49 (750M − 10M included) ÷ 1M × $2 $1,480 $249 $249 (750M − 10M included) ÷ 1M × $2 $1,480 750M events × $3/M events $2,250 “Covers 10 million logs, spans, and metrics every month.”
The official calculator shows 20 million records and no extra seats totaling $69/month on Team.
“Up to 750M events + 3.75B metric data points per month”
Honeycomb publishes a 20-million-event Free allowance and Pro capacity from 50 million through 750 million events per month.
“The new per-event rate is $3.00 per million events.”
Honeycomb says the 2026 Pro structure has four capacity tiers and tops out at 750 million events per month.
“Honeycomb measures two types of usage: events and metrics data points.”
Each trace span counts as one event; event and metric limits are independent; events and logs have 60-day fixed retention and metrics have 13-month fixed retention by default.
Honeycomb markets LLM observability, an agent timeline and token tracking. What it does not have is any part of the evaluation workflow: no datasets, no scorers, no experiments, no human annotation. That is fine if you are monitoring an AI feature. It is a problem if you are trying to answer whether last week's prompt change made your answers better, because tracing shows you what happened and says nothing about whether it was any good. Logfire ships datasets built from production traces, code and LLM-judge scorers, annotation queues, experiment comparison against a baseline, and live evals over real traffic.
Honeycomb's Query Builder is a well-made visual tool: filters, breakdowns and calculations across SQL-shaped clauses, with a natural-language assistant on top. Logfire's queries are SQL text. The difference is not really ergonomics, it is what a query is: an interface you operate, or an artifact you can put in a script, review in a pull request, paste into an alert, and hand to a coding agent that already writes PostgreSQL. Point Claude Code or Cursor at our MCP server and it writes the query itself.
Logfire defines an SLI as a SQL predicate over spans, and evaluation results are spans too. That means an objective can be “the judge scored this run at or above 0.8” or “availability, but only for calls to this provider”. For a team running AI in production, quality can be operated with the same discipline as latency and availability.
Honeycomb's free plan includes 20 million events and 100 million metric points a month. Pro sells monthly capacity at a published $3 per million events, starting at $150 for 50 million events and 250 million metric points, with 60-day event retention. Logfire Personal includes 10 million records; Team is $49 plus $2 per million beyond that with 30-day retention, while Growth supports up to 90 days. The worked trace-only examples show both Logfire plans and preserve Honeycomb's separate metric allowance instead of pretending every entitlement is interchangeable.
No. Honeycomb's platform and AI observability pages market LLM observability, an agent timeline and token tracking, but no datasets, scorers, experiments or human annotation. If you are buying observability for an AI product and you need to know whether a prompt change made your answers better, that is the gap. Logfire ships datasets, code and LLM-judge scorers, human annotation queues, experiment comparison and live evals over production traffic.
Yes, genuinely. Neither requires a proprietary agent, both accept standard OTLP, and both let you export the same telemetry elsewhere. Honeycomb's OpenTelemetry contribution record is real and deep. Anyone telling you one of these two is OTel-native and the other is not is selling you something.
Honeycomb's Query Builder is a visual tool: you add filters, breakdowns and calculations across SELECT, WHERE, GROUP BY, ORDER BY, LIMIT and HAVING clauses, with a natural-language assistant that generates queries for you. It is fast and well designed. Logfire's queries are SQL text, which means they paste into a script, live in a pull request, and can be written by a coding agent that already knows PostgreSQL syntax. Which you prefer depends on whether you want queries to be an interface or an artifact.
Honeycomb publishes 20 million events plus 100 million metric points per month, while Logfire includes 10 million records across logs, spans, and metrics. Those are different units and product entitlements, not a like-for-like capacity claim. Beyond the free tier, Honeycomb's 2026 Pro rate is $3 per million events of chosen monthly capacity, starting at 50 million events, while Logfire Team is $49 plus $2 per million records over 10 million. The worked trace-only examples keep retention and Honeycomb's separate metric allowance visible.
Yes, and it is the sensible way to decide. Both speak OTLP, so you can fan the same telemetry out to both backends with an OpenTelemetry Collector, run the same debugging task in each, and keep whichever answered faster. No instrumentation rewrite is required, which is the practical benefit of both being OTel-native.
Honeycomb can be the practical choice for a team already standardized on its investigation workflow, especially when BubbleUp or unlimited seats are requirements. Logfire is the stronger fit when the same product must connect full-stack telemetry to datasets, evaluators, human review, and production improvement.
Both speak OTLP, so a collector can send the same telemetry to each and you can decide on your own traces. 10 million records a month, free.