Athina review
LLM tracing and evaluation analytics for teams operating production workloads.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Athina helps teams build and operate LLM features by logging inferences and complex traces for inspection and evaluation. Developers can review prompts, retrieved context, responses, and inference metadata, while teams can track token use, cost, response time, and model or prompt version. Evaluations can run continuously on production logs or against datasets, and custom evaluators are supported. It is suited to teams that need to assess production behavior across models and prompts; teams looking only for a lightweight prompt editor may not need its broader monitoring and evaluation workflow.
Its analytics can be compared by prompt, model, topic, or customer ID, giving teams several ways to investigate performance patterns. Integrations include LiteLLM, LangChain, LlamaIndex, Ragas, Guardrails, and Hugging Face. Logging is available through APIs, SDKs, or integrations, and the platform supports web and API use. LiteLLM integration documents additional supported providers, including Azure, Ollama, VLLM, SageMaker, Hugging Face, and Replicate. This breadth is useful for teams whose LLM workflow spans multiple tools, while prospective buyers should check whether their own logging path fits the documented integrations.
The published plans show Starter as free, with advanced analytics, unlimited prompts, prompt and model comparisons, and cost and latency tracking. Pro adds unlimited logs, evaluations, datasets, and team seats, plus white-glove support and a GraphQL API; its listed price is “Let’s talk.” Enterprise has custom pricing and adds self-hosting, SOC-2 Type 2 certification, advanced access controls, and support for custom models. Choose Athina if production traces, ongoing evaluation, and segmented analytics are central requirements. Organizations that need a stated fixed price or require self-hosting below Enterprise should compare alternatives before deciding.
Athina pros and cons
- Where it wins
- Runs continuous evaluations on production logs and evaluations on datasets
- Breaks down analytics by prompt, model, topic, or customer ID
- Offers cloud and self-hosted deployment options
- Where it doesn't
- Pro and Enterprise pricing requires a sales conversation
- Self-hosted deployment is listed under Enterprise
- Support channels are limited to email and documentation
Athina fact sheet, pricing and score →
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