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October 29, 2024
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Dynamiq

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Build secure GenAI apps fast with enterprise control
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Dynamiq is a comprehensive platform for building, deploying, and monitoring General AI (GenAI) applications on-premise, aimed at meeting business needs and boosting revenue and competitiveness.

The Dynamiq platform offers a range of features that streamline the process of creating and maintaining GenAI applications. It includes a low-code agentic builder for developing applications, an observability tool for monitoring the functioning and performance of applications, a Rule as a Graph (RAG) toolbox for enhancing data centralization, and Language Model (LLM) deployment.

Furthermore, it provides fine-tuning options for open-source LLMs on private data and a collaborative workspace with company-wide guardrails. The platform also guarantees user control over data with adherence to regulatory compliance requirements, accounting for robust data protection measures.

It allows seamless integration of company-specific data using RAG, enhancing the robustness of conversational applications. Emphasizing security and privacy, Dynamiq maintains strong standards for information processing, data retention, and access control.

Users can also assure sensitive customer data protection and fine-grained platform security with specified access controls.

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Pricing

Pricing model
Paid
Paid options from
$975/month
Billing frequency
Monthly
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Reviews

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Dynamiq was manually vetted by our editorial team and was first featured on October 29th 2024.

Pros and Cons

Pros

Low-code agentic builder
Observability tool included
RAG toolbox for data
LLM deployment facility
Open-source LLM fine-tuning
Collaborative workspace
On-premise application deployment
Strict regulatory compliance
Strong data protection
Customizable access controls
Company-wide guardrails
Optimized for conversational applications
Advanced information processing
Robust data retention tools
Secured against data breaches
Seamless data integration
Shared workspace for collaboration
Rapid prototyping and testing
Scalable for large enterprises
Extensive documentation and support
40% operational cost reduction
10-20% revenue increase
Control over data ownership
Ease of building conversational workflows
Centralize and enhance data
Seamless deployment options
Guaranteed precision and reliability
Log all interactions
Fine-tune on your private data
Shared workspace across organization
On-premise deployment option
Regulatory compliance adherence
Powerful observability suite
Real-time insights and performance metrics
Streamlined debugging processes
Rapid fine-tuning and deployment
Guardrails for LLM outputs
Company-specific data source integration
Bank-grade security and privacy
PII protection
Guaranteed structured output
Fine-grain access controls
VPC-based LLM deployment

View 38 more pros

Cons

Limited to on-premise deployment
Doesn't specify multilingual support
Unclear pricing structure
Unclear model training speed
Reliance on RAG technology
Doesn't mention model versioning
Unclear audit logging
Requires existing technical team
No mention of API-based integration
Reliance on company-specific data

View 5 more cons

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Q&A

What is Dynamiq?
What is the main function of Dynamiq?
Can Dynamiq be used for building and monitoring AI applications on-premise?
What specific tools does Dynamiq offer for building GenAI applications?
Does Dynamiq offer any observability tools for monitoring applications?
What is the Rule as a Graph (RAG) toolbox in Dynamiq for?
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