Athina
Athina is a collaborative AI development and observability platform for teams to build, test, evaluate, and monitor LLM-powered features — supporting prompt management, dataset evaluations, tracing, and production monitoring.
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Quick Overview
Best for: Software & Gaming
What it does
Developer Tools software for decision-makers comparing workflow fit and alternatives.
Best fit
Software & Gaming
Pricing snapshot
Freemium from Free
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Athina
Athina is a collaborative AI development platform designed for teams to prototype, evaluate, and run AI features in production. It provides prompt management, dataset creation and annotation, a suite of preset and custom evaluations (50+ preset evals), trace-based observability for LLM flows, continuous online evaluations, and segmented analytics. The platform supports both technical and non-technical users — enabling engineers to run prompts and evals programmatically via SDKs and APIs while product managers, data scientists, and QA can use the UI for experimentation and human annotation. Athina can be deployed in your cloud environment (self-hosted) and provides fine-grained access controls and SOC-2 Type 2 compliance for enterprise-grade data privacy and security.
Athina is a collaborative AI development and observability platform for teams to build, test, evaluate, and monitor LLM-powered features — supporting prompt management, dataset evaluations, tracing, and production monitoring.
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Claim this listingKey Features
Prompt management
Create, version, run, and compare prompts across models with a UI and via SDKs. Supports prompt commits, variables, and parameter overrides.
Dataset creation & annotation
Create datasets via UI or API, re-generate datasets by changing model/prompt/retriever, and support human annotation workflows with inter-annotator agreements.
Preset & custom evaluations
Run 50+ preset evals (Athina, OpenAI, Ragas, Guardrails, etc.) or configure custom evals using LLM-as-a-judge, custom Python functions, or external APIs.
Tracing & observability
Capture full LLM traces for every step of flows so teams can replay traces, inspect behavior, and debug production issues.
Continuous / online evaluations
Configure evaluations to run on logs as they come in to continuously monitor model accuracy and performance.
Segmented analytics
Compare eval scores by prompt, model, topic, or customer ID and understand performance changes across segments and over time.
Collaborative workflows
Support for cross-functional collaboration: non-technical users can evaluate and annotate via UI while engineers use SDKs and APIs.
Self-hosted deployments & access controls
Deploy Athina in your VPC with fine-grained permissions for full data privacy and compliance (SOC-2 Type 2).
APIs & SDKs
GraphQL API and client SDKs for programmatic creation of datasets, prompts, evals, inference logging, and running flows.
Support for custom models & providers
Use custom models from providers such as Azure OpenAI, Google Vertex, AWS Bedrock, and others.
Pricing
Starter — Free: includes a free tier with 10k logs/month and basic analytics.
Starter (Free)
Free- 10k logs / month
- Advanced analytics (limited)
- Unlimited prompts (implied in marketing copy)
- Compare prompts and models, track cost & latency metrics
Pro
Contact sales / Let's talk- Everything in Starter
- Unlimited logs
- Unlimited evals
- Unlimited datasets
Enterprise
Custom pricing (contact sales)- Everything in Pro
- Self-hosted deployment
- SOC-2 Type 2 certification
- Advanced access controls
Use Cases
Model development & experimentation
Prototype prompts and chains, run experiments, compare prompt and model variants, and iterate quickly with programmatic and UI tools.
Continuous evaluation and monitoring
Run online evals against production logs to detect regressions and monitor model performance over time and across segments.
Human-in-the-loop QA and annotation
Enable human QA teams to annotate and verify evaluation results, improving eval quality and surfacing nuanced errors automated checks may miss.
Observability for LLM traces
Trace every step of LLM flows to replay behavior, debug issues, and audit model outputs in production.
Enterprise deployments with privacy controls
Deploy inside your VPC with SOC-2 Type 2 compliance and fine-grained permissions for data-sensitive applications.
Integrations
Azure OpenAI
Use custom models hosted on Azure OpenAI as inference providers for prompts and evals.
AWS Bedrock
Integrate models hosted on AWS Bedrock as model providers for evaluation and production inference.
Google Vertex
Use Google Vertex-hosted models with Athina for prompt execution and evaluations.
OpenAI / Ragas / Guardrails (eval providers)
Use preset evals and evaluation providers like OpenAI, Ragas, and Guardrails as part of your evaluation suite.
Benefits
Limitations
Frequently Asked Questions
Does Athina have a self-hosted deployment option?
Does Athina logging add any latency?
Does Athina support custom evaluations?
Does Athina work with Azure / Vertex / Bedrock?
How long does Athina take to integrate?
What kinds of evaluations does Athina support?
Getting Started
- 1 Sign up for Athina and obtain an API key (or request a self-hosted deployment if needed).
- 2 Install the appropriate client SDK (examples shown for athina_client and athina.evals) and set your API key in environment variables.
- 3 Instrument logging (async fire-and-forget) to send traces and inferences to Athina — see https://docs.athina.ai/logging for details.
- 4 Create or upload a dataset, configure evals (preset or custom), and run evaluations via the UI or SDK.
- 5 Invite team members and configure access controls or deploy in your VPC for self-hosted setups.
Support
docs
Documentation and logging guides available at https://docs.athina.ai
General and sales inquiries: [email protected]
demo / sales
Book a demo or contact sales through the website to discuss Pro/Enterprise plans and self-hosted deployments.
community / links
Public links to GitHub and social profiles available from the site (GitHub, LinkedIn, Crunchbase).
API
https://docs.athina.ai (includes logging guides, GraphQL API and SDK examples)
Not available — no public rate limit details published on the site
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