Coralogix vs Honeycomb Pricing (2026)

How do these two stack up on price? Here's what each one costs, what you get, and where the value sits.

Coralogix Honeycomb
Starts at $0/mo $150/mo
Number of plans 4 3
Free plan
Free trial
Pricing model usage-based usage-based

Logs

$0/mo
  • Infinite retention
  • Stored in your bucket

Traces

$0/mo
  • Infinite retention
  • Stored in your bucket

Metrics

$0/mo
  • Infinite retention
  • Stored in your bucket

AI

$1/mo
  • Infinite retention
  • Evaluate AI in real-time

Free

$0/mo
  • 2 Triggers
  • Distributed Tracing
  • BubbleUp
  • OpenTelemetry Support
  • Team Query History
  • Query Result Permalinks
  • Honeycomb Metrics
  • Canvas AI Copilot
  • Honeycomb MCP

Pro

$150/mo
  • All features from the Free Plan
  • 100 Triggers
  • 2 Service Level Objectives (SLOs)
  • Single-Sign On (SSO)
  • Honeycomb Support
  • Monthly or Annual Subscription

Enterprise

Popular
Custom
  • All features from the Pro Plan
  • Starts with 300 Triggers
  • Starts with 100 SLOs
  • Service Map
  • Enterprise Support for Refinery
  • Enterprise-Grade Support + Onboarding
  • Query Data API
  • AWS PrivateLink
  • SLO Reporting API
  • Support for Honeycomb Private Cloud

Coralogix vs Honeycomb FAQ

Which one is cheaper?
Coralogix starts lower at $0/mo, compared to $150/mo for Honeycomb.
Can I use either one for free?
Honeycomb has a free plan. Coralogix doesn't — though they do offer a free trial.
How do they charge?
Both use a usage-based model, so the comparison is straightforward — it comes down to features and limits at each price point.
Which one is a better deal?
Depends on what you need. Coralogix: Coralogix is positioning as a mid-market alternative to Datadog and Splunk — usage-based like Datadog but without the per-host seat tax, and cheaper at scale for teams that are disciplined about what they ingest. They're going after engineering teams that got burned by unpredictable observability bills. Honeycomb: Honeycomb sits in the premium tier of the observability space, competing with Datadog and New Relic but positioning itself as the serious tool for teams that actually care about distributed tracing and high-cardinality data. They're not trying to win on price — they're targeting engineering-led orgs willing to pay for depth over breadth.

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