AI Tool
Deepchecks pricing, features, company info, and alternatives
A factual product page for Deepchecks' AI evaluation and monitoring platform.
Last updated April 2026 · Pricing and features verified against official documentation
Pricing
Current public pricing tiers on file for Deepchecks, last verified Apr 26, 2026.
Open-Source
$0
AGPL testing package license; free with 1 model per deployment and on-prem deployment mode.
Startup Plan
$159 / model
Limited-time offer of $89 per model on the pricing page.
Dedicated
Get a quote
Quote-based option with single-tenant, cloud, on-prem, or bare-metal deployment choices.
Partnership
Get a quote
Quote-based option that can be distributed as part of a platform.
What You Can Do With It
The main capabilities that shape how people use Deepchecks today.
Covers testing, CI, and continuous monitoring for AI and ML systems across the development lifecycle.
Supports evaluation of LLM apps, agents, prompts, and model versions with auto-scoring and comparison workflows.
Provides prompt-based metrics, custom checks, and data retention controls on the public pricing tiers.
Documents SaaS, virtual private cloud, bare-metal, and AWS-managed deployment paths.
Best For
Who Deepchecks is most clearly built for.
Teams that need evaluation and monitoring for LLM apps, agents, or other AI workflows.
Organizations that want to compare prompts, models, or agent versions before release.
Buyers that need deployment flexibility for data residency, security, or procurement reasons.
Company
Leadership and company context for Deepchecks Ltd..
Headquarters
Ramat Gan, Israel
Platforms
Where you can use Deepchecks today.
Web
SaaS
Virtual Private Cloud
Bare metal
AWS-managed
Privacy Notes
Publicly stated data-handling notes that matter when evaluating Deepchecks.
Deepchecks says it supports secure access controls, data isolation, and auditability for regulated and security-conscious organizations.
The public site says the SaaS product uses multi-tenant hosting, while virtual private cloud and bare-metal deployments are available for stricter data constraints.
The pricing page documents AWS-managed, VPC, and bare-metal deployment options alongside the hosted product.
Compliance
Public compliance or enterprise-governance signals we found for Deepchecks.
SOC 2 Type 2
GDPR
HIPAA
AWS GovCloud supported
Alternatives
Other tools worth considering alongside Deepchecks.
Open-source LLM engineering platform for tracing, prompt management, evaluations, and analytics.
Open-source LLM observability, evaluation, and optimization platform from Comet.
Open-source AI observability and evaluation platform for tracing, prompt iteration, and experiments.
AI agent testing and LLM evaluation platform for observability, prompt management, simulations, and guardrails.
Product Snapshot
Deepchecks is an AI evaluation and monitoring platform. The public site currently emphasizes LLM evaluation and agentic workflows, while the documentation still describes continuous validation for data and models from research through production.
What You Can Do With It
- Evaluate LLM apps, agents, prompts, and model versions across development and production workflows.
- Run auto-scoring, prompt-based metrics, and model comparison workflows before release.
- Use testing, CI, and continuous monitoring workflows for AI and ML systems.
- Choose SaaS, virtual private cloud, bare-metal, or AWS-managed deployment modes.
Why It Stands Out
Deepchecks combines evaluation, continuous monitoring, and deployment flexibility in one product family. The public pricing pages also separate hosted, dedicated, and open-source options, which makes the buying path explicit.
Tradeoffs To Know
- Public pricing is split across a free open-source option, a per-model startup plan, and quote-based options.
- Some security and compliance details are only described at a high level on public pages or noted as available on request.
- The current site mixes LLM evaluation, ML testing, and monitoring terminology, so buyers need to map their use case to the right plan or deployment model.