DeepEval is a solid Python eval framework, but if you need multi-language support, security testing, or a SaaS dashboard, here are the best alternatives.
246 attack plugins, 145 scorers, 88 providers. TypeScript + Python. Full SaaS dashboard, compliance, LLM firewall. Open source MIT.
Best for: Teams that need eval + security + compliance with multi-language support
Open-source LLM eval with 133 red team plugins and 30+ assertions. Acquired by OpenAI (March 2026). 10.5K stars, 300K+ devs.
Best for: Teams comfortable with OpenAI ownership and CLI-first workflows
Limitations: Now OpenAI-owned, no firewall/gateway/tracing, no cost analytics
Best-in-class LLM tracing and observability (YC W23). 100+ providers via LiteLLM. No red teaming or built-in eval.
Best for: Teams needing only LLM observability and tracing
Limitations: Zero attack plugins, no built-in eval scorers, no compliance
EU-focused AI red teaming with 40-50 probes and dynamic multi-turn agents. SOC 2 Type II certified.
Best for: EU enterprises needing adaptive red teaming with SOC 2 certification
Limitations: 40-50 probes, 10-15 scorers, no firewall/tracing/gateway
Closed-source eval platform with polished UX and CI/CD integration.
Best for: Teams wanting simple eval-only workflows
Limitations: Closed source, no security testing, no self-host
Databricks' open-source ML lifecycle platform with basic LLM eval (~12 scorers). No security testing.
Best for: Teams already invested in Databricks ecosystem
Limitations: ~12 scorers, no security testing, SaaS requires Databricks
Leading experiment tracking with Weave for basic LLM evaluation (~10 scorers).
Best for: Teams needing experiment tracking + basic eval
Limitations: ~10 scorers, no security testing, no compliance
Free evaluation for OpenAI models. Completely vendor-locked.
Best for: Teams using only OpenAI models
Limitations: OpenAI only, no red teaming, vendor locked
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