Comparison
Logfire vs Datadog
Logfire is full-stack observability for the agent era: infrastructure monitoring, service maps, APM, logs, metrics, traces, session replay, feature flags, and AI observability in one OpenTelemetry-native product. The agent trace investigator follows every model call, tool call, retry, database query, API request, log, and host signal in one nested trace. For model-call telemetry, Logfire’s 10M-record free tier covers a workload that lists at about $5,125/month in Datadog Agent Observability with 30-day retention—assuming one Logfire record per LLM span. Then Logfire costs $2 per million records, without Datadog’s product-sprawl bill.
Feature Comparison
Quick comparison
| Feature | Logfire | Datadog |
|---|---|---|
| Platform coverage | Infra, APM, logs, metrics, traces, replay, flags, and AI | Comparable coverage across separately activated products |
| Architecture | One OpenTelemetry-native product | Datadog agents or OTLP intake with platform extensions |
| Pricing Model | 10M free; then $2/M records | $15/infra host + $31/APM host + $350/M LLM spans + $150/M for 30-day retention |
| Host Fees | None | Infrastructure and APM billed per host |
| AI/LLM Support | Agents, applications, and infrastructure in one project | Separately billed Agent Observability product |
| Query workflow | PostgreSQL-compatible SQL across all telemetry | Product-specific syntax; DDSQL for supported data |
| MCP server | SQL queries plus dashboards, alerts, and issues | DDSQL MCP toolset (preview) |
| Budget control | Hard price cap on Team+ plans | Usage across separately priced products |
Cost advantage
Equivalent model-call telemetry, different bill
| Scenario | Datadog | Logfire | Savings |
|---|---|---|---|
| 100M LLM spans/month | $50,125/mo | ~$229/mo (Team + 90M records) | ~219x lower |
| 500M LLM spans/month | $250,125/mo | ~$1,029/mo (Team + 490M records) | ~243x lower |
| Infrastructure and APM | $15 + $31/host/month | No host fees | No host tax |
| Monthly budget | Multiple product meters | Hard price cap on Team+ plans | A ceiling you set |
Illustrative US annual-list-price model, checked July 2026. It assumes one Logfire record per LLM inference span and excludes additional Logfire browser, tool, retrieval, API, database, log, metric, and application telemetry. Datadog Agent Observability: $160/month including 100,000 LLM spans, then $3.50 per 10,000; 30-day retention adds $1.50 per 10,000. Logfire Team: $49/month including 10 million records, then $2 per additional million. Datadog infrastructure and APM charges are additional.
Key Differences
Why teams choose Logfire
Full-stack coverage, one product
Run infrastructure monitoring, service maps, APM, logs, metrics, traces, session replay, feature flags, and AI observability in the same OpenTelemetry-native product. Every signal is available to the same team, in the same project, when an incident crosses from the browser to an API, agent, database, or host.
The agent trace investigator built for production systems
Start with one agent run, then see every model call, tool call, retry, database query, API request, log, and host signal in its nested trace. Model and agent views surface the pattern; the trace shows exactly why a production run failed.
10M included telemetry records: $5,125 of equivalent list price
Logfire's free plan includes 10 million telemetry records each month. If each of those records is an LLM inference span, Datadog Agent Observability with 30-day retention lists at $5,125/month before you add APM, host monitoring, logs, or other Datadog products. On Team and Growth, Logfire remains $2 per additional million records and lets you set a hard price cap.
One trace, one project, one query model
Datadog's Agent Observability is separately packaged and billed. Logfire puts AI, application, and infrastructure telemetry in the same project and uses PostgreSQL-compatible SQL across it. No moving from product to product—or from query syntax to query syntax—to explain a production failure.
Self-host without changing products
Logfire Cloud, Dedicated, and Self-Hosted run the same product. For sensitive workloads, deploy the official Helm chart in your own Kubernetes environment with your own PostgreSQL and object storage, and keep the same instrumentation, views, SQL, and trace-investigation workflow.
Decision Guide
Which should you choose?
Choose Logfire if...
- ✓You want agents, applications, and infrastructure in one observability project
- ✓You want LLM observability without separate per-span usage and retention meters
- ✓You want PostgreSQL-compatible SQL across every production signal
- ✓You want your coding agent to investigate telemetry, dashboards, alerts, and issues via MCP
- ✓You want a hard ceiling on telemetry spend instead of several product meters
- ✓You need the same Logfire product self-hosted in your own Kubernetes environment
Choose Datadog if...
- •You're already deeply integrated with Datadog's ecosystem
- •You need a specific Datadog integration or compliance capability
- •You need a Datadog-only product or workflow that Logfire does not yet support
FAQ
Common questions
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