Decision comparison
New Relic wins on breadth, ecosystem maturity, and compliance. Observe wins on modern open data architecture, AI SRE automation, and cost efficiency for large engineering teams.
| Decision factor | New Relic | Observe |
|---|---|---|
| Compliance Certifications | FedRAMP Moderate, HIPAA | SOC 2 (standard enterprise) |
| Best For | Organizations needing the broadest observability coverage from a single platform with compliance certifications | Teams prioritizing cost efficiency, open data standards, and AI-driven investigation at scale |
| Pricing | Free tier available, paid plans start at $19/mo per host, additional costs based on usage and features | Logs at $0.49, other tiers at $0.00, $0.01, $0.59 |
| Storage format | Proprietary NRDB | Open Iceberg tables with 10x compression |
Exact public-source results for the mapped package version or repository. Not assessed means there is no observed result for that source.
0 known vulnerabilities
npm · newrelic@14.3.7
Assessed August 17, 2026
0 known vulnerabilities
PyPI · newrelic@13.4.0
Assessed August 17, 2026
Not assessed
Not assessed
Not assessed
Package and repository evidence only. It does not rate the full product and does not affect our rating or ranking.
| Feature | New Relic | Observe |
|---|---|---|
| Data Architecture | ||
| Storage format | Proprietary NRDB | Open Iceberg tables with 10x compression |
| Query language | NRQL | OPAL with chat-based root cause analysis |
| Storage/compute separation | Coupled | Separated with elastic scaling |
| Monitoring Capabilities | ||
| Capability breadth | 50+ capabilities (APM, browser, mobile, synthetic, security) | Core pillars (logs, APM, infra, LLM) |
| Digital experience monitoring | Session replay + AI friction detection | Not native |
| Mobile app monitoring | Native iOS/Android monitoring | Not native |
| AI & Automation | ||
| AIOps capability | Automated detection, correlation, resolution | AI SRE builds investigation plans |
| LLM/agent monitoring | Dedicated AI and agentic monitoring | LLM observability built-in |
| Natural language investigation | AI-assisted queries | Chat-based RCA with stored investigation summaries |
| Pricing & Integration | ||
| Free tier | 100 GB/mo + unlimited basic users | 30-day trial |
| Per-user pricing | Core $49/user/mo; Full $349/user/mo | No per-user fees (unlimited users) |
| Integration count | 780+ quickstart integrations | 400+ integrations, OpenTelemetry-native |
| Compliance certifications | FedRAMP Moderate, HIPAA | SOC 2 (standard enterprise) |
Storage format
Query language
Storage/compute separation
Capability breadth
Digital experience monitoring
Mobile app monitoring
AIOps capability
LLM/agent monitoring
Natural language investigation
Free tier
Per-user pricing
Integration count
Compliance certifications
New Relic wins on breadth, ecosystem maturity, and compliance. Observe wins on modern open data architecture, AI SRE automation, and cost efficiency for large engineering teams.
Choose New Relic if:
Choose New Relic if you need full-stack observability (APM, mobile, synthetic, session replay) and FedRAMP/HIPAA compliance.
Choose Observe if:
Choose Observe if you have a large engineering team where per-user fees add up and you want open Iceberg-based data with AI-driven investigation.
Choose New Relic if:
If you're unsure, New Relic's free 100 GB tier lets you trial a mature platform with no upfront commitment.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
New Relic charges based on data ingest volume with prices starting at $0.40/GB for standard data and $0.60/GB for Data Plus, plus per-user fees of $49/user for core users and $349/user for full platform users. New Relic offers 100 GB of free monthly data ingest. Observe charges $0.49/GB for logs with compute included and unlimited users, meaning there are no per-seat fees. Observe also offers other signal types at $0.59/GB. For log-heavy workloads, Observe's unlimited-user model with included compute can deliver significant savings compared to New Relic's combined per-GB and per-user pricing structure.
New Relic provides AIOps with automated alerting, detection, correlation, and resolution capabilities. It also offers AI and agentic monitoring specifically designed to monitor LLM applications and AI agents, tracking behavior and token usage automatically. Observe takes a different approach with its AI SRE, which acts as an automated investigation assistant. When an incident occurs, the AI SRE formulates an investigation plan, delegates tasks to specialized agents, and presents results to the on-call engineer. It also provides chat-based root cause analysis that summarizes investigations as they progress, creating a stored record for future reference.
Both platforms support OpenTelemetry, but their approaches differ. New Relic ingests OpenTelemetry metrics, traces, and logs with open-source instrumentation and positions itself as a prominent open-source provider with sizable ecosystem support. Observe builds OpenTelemetry data collection directly into its real-time ingest pipeline and stores telemetry in open Iceberg table formats, emphasizing vendor lock-in avoidance. Observe's open data lake architecture means telemetry data remains in open formats for potential reuse outside the platform. For teams committed to open standards and data portability, Observe's architecture provides stronger guarantees against vendor lock-in.
New Relic uses its proprietary NRDB (New Relic Database) with NRQL, a SQL-like query language that enables custom queries and flexible dashboarding across all telemetry types. This approach offers powerful querying but ties data to the New Relic ecosystem. Observe uses an open data lake architecture with Iceberg tables, providing 10x compression on low-cost cloud storage. The O11y Context Graph structures telemetry data using semantic relationships, incremental views, and token indexes for fast search and correlation. Observe's architecture separates storage from compute with elastic scaling, which allows the platform to handle growing data volumes without proportional cost increases.