LangWatch vs Moda (2026)

Pros

• Unique agent simulation testing via Scenario framework — enables multi-turn, stateful agent testing unmatched by LangSmith or Langfuse
• Fully open-source with MIT license and no feature gating on self-hosted deployments
• All-in-one platform combining tracing, evaluation, prompt management, and PII redaction
• Framework-agnostic with OpenTelemetry-native integration across all major agent frameworks
• Enterprise-ready with ISO 27001, SOC2, GDPR compliance, and on-prem deployment options

Cons

• Very small team (~5 employees) with only EUR 1M pre-seed — long-term viability less proven than well-funded competitors
• Smaller community and fewer tutorials compared to LangSmith or Langfuse ecosystems
• Self-hosting requires Kubernetes/Helm for production with known issues on reverse proxy deployments
• Only supports LLM-as-a-judge evaluations — no heuristic or rule-based evaluation options

Pros

• Clear Datadog-for-agents positioning that is easy to understand
• Both founders have direct AI agent production experience at Shopify and Notion
• Plain-language signal definition is a strong differentiator vs code-heavy eval tools
• Built-in security monitoring for prompt injection and jailbreaks at no extra config
• Agent replay and batch testing enable rapid debugging of production failures

Cons