Version comparison
Compare prompts, models, RAG setups, routing mechanisms, or other pipeline settings to see how changes affect LLM application performance.
Platform for LLM evaluation, testing, and monitoring across prompts, models, RAG pipelines, and agents
Deepchecks is a platform for LLM evaluation, testing, and monitoring. The pricing page describes it as a way to run continuous ML validation for AI applications, while the docs and AWS pages focus on validating prompts, models, retrieval setups, and multi-step agent workflows.
It is built for teams that need to compare versions, manage evaluation sets, automate or guide annotations, and monitor production behavior across individual AI applications. The platform is offered as a hosted SaaS product and also supports private, self-hosted, and AWS-managed deployment options.
Compare prompts, models, RAG setups, routing mechanisms, or other pipeline settings to see how changes affect LLM application performance.
Use AI-assisted annotations to streamline manual review, with customizable scoring algorithms and learning techniques that adapt to your data.
Generate, version, expand, and explore evaluation sets for a specific workflow, including ground truth creation and test-set style benchmarking.
Choose from SLM-powered quality, risk, and policy metrics, plus LLM-powered custom metrics, technical metrics, and user feedback loops.
Track monthly DPU consumption in a usage dashboard and understand which workloads are driving platform usage.
Evaluate applications in multiple languages and use built-in prompt-based evaluation without bringing your own LLM API keys.
Validate a chatbot, assistant, or other LLM application before wider release by comparing prompt or model changes and reviewing evaluation outputs against ground truth.
Operate several production AI applications with recurring evaluation and governance needs, using plan limits, retention, and usage tracking to keep teams aligned.
Assess multi-step agents that move through complex tasks, where teams need to inspect decision points, transitions, and failures across the workflow.
Benchmark retrieval-augmented generation setups by building evaluation sets and measuring how well the system uses external sources and answers with context.
Review summaries, generated text, classification behavior, or natural-language-to-code outputs with custom metrics and guided annotation workflows.
Deepchecks is positioned for teams validating and monitoring LLM applications and agent workflows. The pricing page says Basic fits early-stage validation and single-application setups, Scale fits multiple production applications and multi-step agents, and Enterprise fits business-critical deployments with advanced security and control.
For SaaS deployments, upgrades are available during the contract term. Paid plans also allow additional users or AI applications, and downgrades typically take effect at the next renewal.
Yes. Seats can be reassigned to different users within the organization through workspace administration.
An AI Application is a distinct LLM-powered system or workflow you evaluate and monitor in Deepchecks. Examples given on the pricing page include customer-facing chatbots, internal AI assistants, RAG pipelines, and multi-agent workflows.
Deepchecks supports VPC deployments on AWS, GCP, or Azure, bare-metal deployments, and an AWS-managed option. The pricing page also notes a hosted SaaS model and a privately hosted option.
Traffic data is for reference only.
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