DeepEval vs Giskard
A side-by-side comparison of DeepEval and Giskard, two Eval tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | DeepEval | Giskard |
|---|---|---|
| Category | Eval | Eval |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment | Hybrid | Hybrid |
| Platforms (differs) | CLI, API | Web, API |
| Model support (differs) | BYO key / model | Model-agnostic |
| Vendor (differs) | Confident AI | Giskard |
| Capabilities (differs) |
|
|
The honest brief
DeepEval
Write LLM evals as Pytest-style assertions and run them in CI, backed by 50+ metrics across RAG, agents, and safety.
- Assertions run in your CI pipeline
- Metrics for RAG, agents, and safety
- Bring any judge model (BYO key)
- Integrates LangChain/CrewAI/OpenAI
- LLM-as-judge adds cost
- Dashboards need paid Confident AI
- Judge metrics can be noisy
Giskard
Its Scan auto-generates adversarial suites mapped to the OWASP LLM Top-10, framing eval as security red-teaming, not just accuracy.
- Automatic vulnerability scan
- Multi-turn red-teaming agents
- Covers LLMs, RAG apps, and ML models
- Publishes the open Phare safety benchmark
- Python-library learning curve
- Collaboration features are paid (Hub)
- Less focused on production tracing
When to pick which
Both cover LLM evaluation.
Pick DeepEval if you need LLM observability.
- LLM observability (secondary capability)
Pick Giskard if you need Red-teaming and AI security scanning.
- Red-teaming (primary capability)
- AI security scanning (primary capability)
DeepEval leans on LLM evaluation as a headline capability; Giskard treats it as secondary.
- LLM evaluation (primary capability)
They also differ on:
- Platforms
- CLI, API · Web, API