Klu.ai
UnclaimedDesign, deploy, and optimize LLM applications with collaborative tooling and robust observability.
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TL;DR - Klu.ai
- Collaborative platform for designing, versioning, and evaluating LLM prompts.
- Provides comprehensive observability for tracking LLM performance, cost, and drift in production.
- Offers enterprise-grade security, private infrastructure, and governance for regulated teams.
Pricing: Free plan available
Best for: Growing teams
Pros & Cons
Pros
- Significantly reduces LLM iteration and evaluation cycles.
- Provides a single source of truth for prompt engineering and model performance.
- Offers robust enterprise features for security, compliance, and custom deployments.
- Supports integration with multiple leading LLM providers.
- Enables alignment across product, engineering, and research teams.
Cons
- Team plan is priced per seat, which can become costly for larger teams.
- Advanced governance and private deployment features are exclusive to the custom Enterprise plan.
Key Features
Collaborative prompt design workspace (Studio)Prompt versioningBuilt-in evaluation workflowsObservability dashboards for performance, cost, and driftIntegration with 50+ model and tool providersShared evaluation sets for team alignmentPrivate infrastructure deployment (VPC)Governance and audit controls (permissioned workspaces, audit trails, evaluation policies)
Pricing Plans
Free TrialStarter
Free
- Prompt workspace with versioning
- Shared evaluation sets
- Community support
Team
$99 per seat
- Collaboration and approvals
- Observability dashboards
- Usage based evaluations
Enterprise
Custom
- Private cloud deployment
- Advanced governance and SSO
- Dedicated success team
About Klu.ai
By Toolradar Team·
Klu.ai is a comprehensive platform designed for teams to collaboratively build, deploy, and optimize Large Language Model (LLM) applications. It provides a shared workspace for prompt engineering, enabling teams to draft, iterate, and version prompts with built-in evaluation workflows. The platform ensures that all experiments, evaluations, and observability data remain synchronized across the team, facilitating faster iteration cycles and consistent quality.
Klu.ai is ideal for product, engineering, and research teams developing production-grade LLM applications. It addresses the challenges of managing LLM lifecycles by offering tools for tracking performance, cost, and model drift. The platform integrates with over 50 model and tool providers, allowing users to connect various LLMs like OpenAI, Anthropic, and Google within a single environment. For enterprise clients, Klu.ai offers enhanced security features including private infrastructure deployment within a VPC, advanced governance controls, and dedicated support to meet stringent compliance and scalability requirements.
By centralizing prompt design, evaluation, and observability, Klu.ai helps teams align on measurable quality, accelerate shipping times, and maintain high performance for customer-facing AI workflows. It provides real-time dashboards and shared evaluation sets to ensure stakeholders have visibility into model quality and changes over time, ultimately reducing evaluation cycles and improving overall reliability of LLM applications.
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Klu.ai FAQ
How does Klu.ai facilitate collaboration between different roles like product, engineering, and research teams?
Klu.ai provides a shared workspace for prompt design, versioning, and evaluation sets. This allows all team members to collaborate on prompts, align on measurable quality through shared experiments and dashboards, and track performance directly tied to production, ensuring everyone is working from the same source of truth.
What specific types of model providers can be integrated with Klu.ai?
Klu.ai supports integration with a wide range of major model providers, including OpenAI, Anthropic, and Google. It offers over 50 model and tool integrations, allowing users to connect various LLMs within a single workspace for comprehensive management and evaluation.
Can Klu.ai be deployed within a private cloud environment or a Virtual Private Cloud (VPC)?
Yes, for Enterprise plans, Klu.ai offers private cloud deployment options, including running the platform within your own VPC. This provides isolated data planes and custom deployment controls, which is crucial for regulated teams requiring enhanced security and compliance.
How does Klu.ai help in understanding the reasons behind changes in LLM quality over time?
Klu.ai's observability features track performance, cost, and drift across every model and application. By connecting experiments to production data and providing real-time dashboards, it makes it easier to compare models, monitor usage, and identify why quality might be changing, enabling proactive optimization.
What is the primary difference between the 'Studio' and 'Observe' features in Klu.ai?
The 'Studio' feature is designed for the initial stages of LLM development, focusing on collaborative prompt design, iteration, and versioning with built-in evaluation workflows. 'Observe' is for post-deployment, providing comprehensive observability across live models and applications to track performance, cost, and drift, ensuring ongoing quality and reliability in production.
Source: klu.ai