Comparison
Logfire vs Datadog
Both provide application observability but differ in how they treat AI. Datadog added LLM observability as a separate module where AI traces live apart from application traces, while Logfire was built for AI applications from the start with full-stack visibility: LLM calls, database queries, and API requests in one trace, at a fraction of the cost. Datadog monitors everything but treats AI as an afterthought. Logfire is built for AI applications from the ground up—with OpenTelemetry, SQL queries, and pricing that doesn't penalize modern architectures.
Feature Comparison
Quick comparison
| Feature | Logfire | Datadog |
|---|---|---|
| Architecture | OpenTelemetry-native | Proprietary agents |
| Pricing Model | Per-span ($2/million) | Per-host + ingestion + custom metrics |
| Host Fees | None | $15-40/host/month |
| AI/LLM Support | First-class, one function call | Add-on, separate product |
| Query Language | SQL (PostgreSQL) | Proprietary |
| Setup | 3 lines of code | Agent deployment per host |
| Autoscaling Impact | Linear cost increase | High-water-mark billing spikes |
Cost Savings
Pricing comparison
| Scenario | Datadog | Logfire | Savings |
|---|---|---|---|
| Hobby (2 hosts, 5M spans) | $101/mo | $0/mo (free tier) | 100% |
| Startup (10 hosts, 100M spans) | $560/mo | $180/mo | 68% |
| Scale-up (50-150 hosts, 500M spans) | $9,860/mo | $980/mo | 90% |
| High-volume (500 hosts, 2B spans) | $33,550/mo | $3,980/mo | 88% |
*Logfire Cloud Pro pricing ($2/million spans). Enterprise pricing available on request.
Key Differences
Why teams choose Logfire
AI-First, Not AI-Afterthought
Logfire was built for AI applications. One function call gives you token tracking, cost monitoring per model, LLM-specific conversation history panels, tool call inspection, and streaming support. All of this appears in the same trace as your database queries and API calls, so when an agent fails you see the complete picture. Datadog added LLM observability as a separate product module — it works, but LLM traces live apart from application traces, so you're context-switching between views to debug a single failure.
OpenTelemetry Native
Logfire is built on OTel from day one—your instrumentation is portable, no vendor lock-in. Datadog uses proprietary agents; while they support OTel export, it's not the native path, and OTel metrics are treated as expensive "custom metrics."
SQL Queries, Not Proprietary DSL
Query your data with standard PostgreSQL SQL—AI assistants write excellent queries for you. Your team already knows SQL; use that knowledge. Datadog uses a proprietary query language with a learning curve.
Pricing That Doesn't Penalize Modern Practices
Datadog's per-host model penalizes microservices, autoscaling, and serverless. Running 10 microservices across 10 small instances costs 10x more than one big server. Logfire charges for data, not infrastructure decisions.
Decision Guide
Which should you choose?
Choose Logfire if...
- ✓You're building AI-native applications and want first-class AI observability
- ✓You want simple per-span pricing without host fees or metric surcharges
- ✓You want portable instrumentation with OpenTelemetry, not vendor lock-in
- ✓Your infrastructure scales dynamically and you don't want billing surprises
- ✓You prefer familiar SQL over learning a proprietary query language
Choose Datadog if...
- •You're already deeply integrated with Datadog's ecosystem
- •You need comprehensive infrastructure metrics alongside APM
- •You need specific compliance certifications or integrations Datadog offers
FAQ
Common questions
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