Parea AI
UnclaimedTest, evaluate, and confidently ship LLM applications to production with comprehensive tooling.
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TL;DR - Parea AI
- Comprehensive platform for LLM testing, evaluation, and observability.
- Enables confident deployment of LLM applications to production.
- Offers experiment tracking, human review, and prompt management.
Pricing: Free plan available
Best for: Growing teams
Pros & Cons
Pros
- Streamlines the entire LLM development and deployment lifecycle.
- Provides clear insights into model performance and regressions.
- Facilitates collaboration through human review and feedback mechanisms.
- Offers flexible SDKs for Python and JavaScript/TypeScript.
- Integrates with major LLM providers and frameworks.
Cons
- Free tier has limited team members and log retention.
- Enterprise features like SSO and custom roles require custom pricing.
- Log retention on the Team plan is limited to 3 months by default.
Key Features
Automated domain-specific evaluation creationExperiment tracking and performance monitoringHuman review and annotation for feedback and fine-tuningPrompt playground and deployment managementProduction and staging data observability (cost, latency, quality)Dataset generation from logs for model fine-tuning
Pricing Plans
Free
$0/month
- All platform features
- Max. 2 team members
- 3k logs / month (1 mon retention)
- 10 deployed prompts
- Discord community
Team
$150/month
- 3 members ($50 / month per add'l. member up to 20)
- 100k logs / month incl. ($0.001 / extra log)
- 3 month data retention, (6/12 mon upgrade)
- Unlimited projects
- 100 deployed prompts
- Private Slack channel
Enterprise
Custom
- On-prem/self-hosting
- Support SLAs
- Unlimited logs
- Unlimited deployed prompts
- SSO enforcement and custom roles
- Additional security and compliance features
AI Consulting
Custom
- Rapid Prototyping & Research
- Building domain-specific evals
- Optimizing RAG pipelines
- Upskilling your team on LLMs
About Parea AI
By Toolradar Team·
Parea AI provides a comprehensive platform for developing and deploying Large Language Model (LLM) applications. It offers tools for experiment tracking, observability, and human annotation, enabling teams to ensure the quality and performance of their AI systems before and after deployment. The platform focuses on streamlining the LLM development lifecycle from prompt engineering to production monitoring.
This product is designed for AI developers, MLOps engineers, and product teams working with LLMs who need to rigorously test, evaluate, and debug their applications. It helps answer critical questions about model performance, regression detection, and the impact of model upgrades, ultimately accelerating the confident shipment of LLM apps to users. Parea AI supports both Python and JavaScript/TypeScript environments with native SDKs and integrations with popular LLM providers and frameworks.
How we evaluate tools →Source: parea.ai
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Parea AI FAQ
How does Parea AI help in debugging LLM failures in production?
Parea AI's observability features log production and staging data, allowing users to debug issues, run online evaluations, and capture user feedback. It tracks cost, latency, and quality in one centralized place, making it easier to identify and resolve performance regressions or unexpected behaviors.
Can Parea AI be used to optimize Retrieval Augmented Generation (RAG) pipelines?
Yes, Parea AI offers AI Consulting services specifically for optimizing RAG pipelines. This indicates that the platform's evaluation and testing capabilities are well-suited for improving the performance and relevance of RAG-based LLM applications.
What is the process for incorporating production logs into test datasets for fine-tuning models?
Parea AI allows users to incorporate logs directly from staging and production environments into test datasets. These datasets can then be utilized to fine-tune models, ensuring that the models are trained on real-world interactions and data patterns.
Which LLM frameworks and providers does Parea AI natively integrate with?
Parea AI offers native integrations with a variety of major LLM providers and frameworks including OpenAI, Anthropic, LangChain, Instructor, DSPy, LiteLLM, Maven, SGLang, and Trigger.dev, alongside its Python and JS/TS SDKs.
What kind of human feedback can be collected and how is it used within Parea AI?
Parea AI enables the collection of human feedback from end users, subject matter experts, and product teams. This feedback can involve commenting on, annotating, and labeling logs, which is crucial for quality assurance (Q&A) and for generating high-quality data for model fine-tuning.
Is it possible to self-host Parea AI or deploy it on-premise?
Yes, the Enterprise plan for Parea AI includes options for on-premise deployment and self-hosting, catering to organizations with specific security, compliance, or infrastructure requirements.
Source: parea.ai