Kolena
General-purpose AI agents
Agentic AI software
AI agents
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What is Kolena
Kolena is an AI quality and evaluation platform used to test, monitor, and improve machine learning and generative AI systems. It supports teams that need structured evaluation workflows for models and agentic applications, including dataset curation, test case management, and performance analysis over time. The product focuses on measurable quality signals (metrics, slices, and regressions) rather than end-user CRM or contact-center automation.
Purpose-built AI evaluation workflows
Kolena centers on evaluation management for ML and generative AI, including organizing test cases, running evaluations, and tracking results over time. This is useful for teams building agentic systems that need repeatable quality gates before deployment. Compared with business-process agent tools, it is oriented toward engineering and model quality assurance rather than sales or service workflows.
Supports regression and monitoring
The platform is designed to help teams detect performance drift and regressions by comparing evaluation runs across versions. This fits continuous delivery practices where models and prompts change frequently. It provides a structured way to document what changed and how it affected measured outcomes.
Analysis via slices and metrics
Kolena emphasizes breaking down performance by data slices and metrics to identify where models fail (for example, specific input types or edge cases). This helps teams move from aggregate scores to actionable debugging. The approach aligns with common evaluation needs for LLM and agent behaviors where failures can be concentrated in narrow scenarios.
Not a business workflow suite
Kolena is not positioned as a full customer engagement, CRM, or contact-center platform. Organizations looking for out-of-the-box agents for sales outreach, inbound qualification, or omnichannel routing will likely need additional systems. Integration work may be required to connect evaluation outputs to business operations tools.
Requires evaluation maturity
To get strong value, teams typically need defined success criteria, representative datasets, and a process for labeling or adjudicating outputs. Organizations early in their AI lifecycle may find setup and governance work non-trivial. Without disciplined test maintenance, evaluation results can become stale or misleading.
Limited public detail on packaging
Publicly available information may not fully specify all enterprise requirements such as deployment options, compliance certifications, or detailed SLA/support tiers. Buyers may need vendor confirmation for security, data residency, and procurement needs. This can lengthen due diligence compared with more standardized enterprise software categories.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Starter | Not publicly listed (contact Kolena / sales) | API + Python SDK access, integrations, templated outputs, unlimited agents; Monthly credit quota: 800 credits; User seats: 3. Source: Kolena official docs (Organization Tiers). |
| Professional | Not publicly listed (contact Kolena / sales) | API + Python SDK access, integrations, templated outputs, unlimited agents, dashboards, data analysis, web search; Monthly credit quota: 1800 credits; User seats: 10. Source: Kolena official docs (Organization Tiers). |
| Enterprise | Custom / Not publicly listed (contact Kolena / sales) | All Professional features plus access audit logs, agent-level permissions, custom roles, data retention policies, service users, SSO/SCIM, teams, workspaces; Monthly credit quota: 4000 credits (listed as Enterprise*); User seats: 20 (Enterprise*). Contact sales for custom limits/pricing. Source: Kolena official docs (Organization Tiers). |
