CODY AI vs Dataworkz

Make an informed decision with our comprehensive comparison. Discover which RAG solution perfectly fits your needs.

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Priyansh Khodiyar's avatar
Priyansh KhodiyarDevRel at CustomGPT.ai

Fact checked and reviewed by Bill Cava

Published: 01.04.2025Updated: 25.04.2025

In this comprehensive guide, we compare CODY AI and Dataworkz across various parameters including features, pricing, performance, and customer support to help you make the best decision for your business needs.

Overview

When choosing between CODY AI and Dataworkz, understanding their unique strengths and architectural differences is crucial for making an informed decision. Both platforms serve the RAG (Retrieval-Augmented Generation) space but cater to different use cases and organizational needs.

Quick Decision Guide

  • Choose CODY AI if: you value true rag architecture with pinecone vector database and configurable retrieval parameters (relevance score, token distribution, focus mode)
  • Choose Dataworkz if: you value free tier available for testing

About CODY AI

CODY AI Landing Page Screenshot

CODY AI is business-focused no-code rag platform with source attribution. Business-focused RAG-as-a-Service platform enabling no-code AI assistant creation trained on custom knowledge bases. Acquired by Just Build It (May 2024), claims 100,000+ businesses as customers. TRUE RAG platform with Pinecone vector database, multi-LLM support (GPT-4, Claude 3.5, Gemini 1.5, Llama 3.1 on Enterprise), and comprehensive REST API. Differentiators: source attribution with every response, Focus Mode (inject 1,000 docs into context), 15-minute bot deployment. Critical gaps: NO direct SOC 2 certification (infrastructure partners only), NO official SDKs, NO native cloud storage integrations. $0-$249/month credit-based pricing. Founded in 2022, headquartered in United States, the platform has established itself as a reliable solution in the RAG space.

Overall Rating
85/100
Starting Price
$29/mo

About Dataworkz

Dataworkz Landing Page Screenshot

Dataworkz is rag-as-a-service platform for rapid genai development. Dataworkz is a managed RAG platform that enables businesses to build, deploy, and scale GenAI applications using proprietary data with pre-built tools for data discovery, transformation, and monitoring. Founded in 2020, headquartered in Milpitas, CA, the platform has established itself as a reliable solution in the RAG space.

Overall Rating
79/100
Starting Price
Custom

Key Differences at a Glance

In terms of user ratings, CODY AI in overall satisfaction. From a cost perspective, Dataworkz offers more competitive entry pricing. The platforms also differ in their primary focus: AI Chatbot versus RAG Platform. These differences make each platform better suited for specific use cases and organizational requirements.

⚠️ What This Comparison Covers

We'll analyze features, pricing, performance benchmarks, security compliance, integration capabilities, and real-world use cases to help you determine which platform best fits your organization's needs. All data is independently verified from official documentation and third-party review platforms.

Detailed Feature Comparison

logo of cody
CODY AI
logo of dataworkz
Dataworkz
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CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
  • Supported formats – PDF, Word, PowerPoint, plain text up to 100MB per file
  • Website crawler – Import up to 25,000 pages with auto re-imports (Premium/Advanced)
  • Document capacity – Free (100), Basic (1K), Premium (10K), Advanced (50K total)
  • Storage – Amazon S3 SSE-S3 encryption, Pinecone vector database (SOC 2 Type II)
  • ⚠️ NO YouTube/cloud integrations – No video transcripts, requires Zapier for Drive/Dropbox/Notion
  • ✅ Point-and-click RAG builder – Mix SharePoint, Confluence, databases via visual pipeline [MongoDB Reference]
  • ✅ Fine-grained control – Configure chunk sizes, embedding strategies, multiple sources simultaneously
  • ✅ Multi-source blending – Combine documents and live database queries in same pipeline
  • 1,400+ file formats – PDF, DOCX, Excel, PowerPoint, Markdown, HTML + auto-extraction from ZIP/RAR/7Z archives
  • Website crawling – Sitemap indexing with configurable depth for help docs, FAQs, and public content
  • Multimedia transcription – AI Vision, OCR, YouTube/Vimeo/podcast speech-to-text built-in
  • Cloud integrations – Google Drive, SharePoint, OneDrive, Dropbox, Notion with auto-sync
  • Knowledge platforms – Zendesk, Freshdesk, HubSpot, Confluence, Shopify connectors
  • Massive scale – 60M words (Standard) / 300M words (Premium) per bot with no performance degradation
Integrations & Channels
  • Native Slack/Discord – Free for all users with /assign-bot command and @mentions
  • Zapier integration – 5,000+ apps including Telegram, Facebook Messenger, WhatsApp
  • REST API v1.0 – Full API access on all paid plans at developers.meetcody.ai
  • ⚠️ NO Teams/webhooks – Requires Zapier for Microsoft Teams, no event-driven notifications
  • ✅ API-first architecture – Surface agents via REST or GraphQL endpoints [MongoDB: API Approach]
  • ⚠️ No prefab UI – Bring or build your own front-end chat widget
  • ✅ Universal integration – Drop into any environment that makes HTTP calls
  • Website embedding – Lightweight JS widget or iframe with customizable positioning
  • CMS plugins – WordPress, WIX, Webflow, Framer, SquareSpace native support
  • 5,000+ app ecosystem – Zapier connects CRMs, marketing, e-commerce tools
  • MCP Server – Integrate with Claude Desktop, Cursor, ChatGPT, Windsurf
  • OpenAI SDK compatible – Drop-in replacement for OpenAI API endpoints
  • LiveChat + Slack – Native chat widgets with human handoff capabilities
Native Slack & Discord Integration ( Differentiator)
  • ✅ Free on all plans – Native Slack/Discord integrations even on Free tier
  • /assign-bot command – Dedicate bots to channels for departmental organization
  • Context preservation – History maintained in threads for multi-turn interactions
  • Competitive advantage – Zero-friction vs API integrations (7.5/10 differentiator)
N/A
N/A
Source Attribution & Transparency ( Core Differentiator)
  • ✅ Automatic citation – Every response includes links to exact documents used for verification
  • Source verification interface – Centralized logs show which documents informed each response for audits
  • Trust building – Users validate AI answers against sources reducing hallucination concerns
  • Compliance advantage – Source traceability supports explainable AI requirements in regulated industries (9/10 differentiator)
N/A
N/A
Focus Mode ( Core Differentiator)
  • ✅ Targeted context injection – Inject up to 1,000 specific documents into conversation context
  • Use cases – Department-specific queries, project-scoped assistance, client-specific information isolation
  • Noise reduction – Constrains retrieval to relevant subset preventing irrelevant information interference
  • Performance advantage – Smaller search space improves retrieval speed and relevance (8.5/10 differentiator)
N/A
N/A
Core Chatbot Features
  • Multilingual support – Build and interact in any language
  • Conversation memory – Configurable token distribution (70% context, 10% history, 20% response)
  • History retention – 14 days (Basic), 30 days (Premium), 90 days (Advanced)
  • ⚠️ NO lead capture/handoff – Requires API/Zapier, prompt-based escalation
  • ✅ Agentic architecture – Multi-step reasoning, tool use, dynamic decision-making [Agentic RAG]
  • ✅ Intelligent routing – Agents decide knowledge base vs live DB vs API
  • ✅ Complex workflows – Fetch structured data, retrieve docs, blend answers automatically
  • ✅ #1 accuracy – Median 5/5 in independent benchmarks, 10% lower hallucination than OpenAI
  • ✅ Source citations – Every response includes clickable links to original documents
  • ✅ 93% resolution rate – Handles queries autonomously, reducing human workload
  • ✅ 92 languages – Native multilingual support without per-language config
  • ✅ Lead capture – Built-in email collection, custom forms, real-time notifications
  • ✅ Human handoff – Escalation with full conversation context preserved
Widget Customization & White- Labeling
  • Header & chat styling – Layout, logo, colors, message bubbles, avatars customization
  • Launcher config – Size, position (left/right/bottom), color, custom icons
  • White-labeling – Complete branding removal requires Premium ($99) or Advanced ($249)
  • ⚠️ NO domain restrictions – Cannot limit widget usage to specific domains
N/A
N/A
L L M Model Options
  • Basic plan – GPT-3.5 Turbo only (1 credit per query)
  • Premium/Advanced – GPT-3.5 Turbo, GPT-3.5 16K (5 credits), GPT-4 (10 credits), Claude Sonnet
  • Enterprise – 6 providers: Llama 3.1, Claude 3.5 Sonnet, GPT-4o, Gemini 1.5, Mixtral-8x7B
  • ⚠️ NO automatic routing – Manual selection only, credit-based transparent pricing
  • ✅ Model-agnostic – Plug in GPT-4, Claude, open-source models freely
  • ✅ Full stack control – Choose embedding model, vector DB, orchestration logic
  • ⚠️ More setup required – Power and flexibility trade-off vs turnkey solutions
  • GPT-5.1 models – Latest thinking models (Optimal & Smart variants)
  • GPT-4 series – GPT-4, GPT-4 Turbo, GPT-4o available
  • Claude 4.5 – Anthropic's Opus available for Enterprise
  • Auto model routing – Balances cost/performance automatically
  • Zero API key management – All models managed behind the scenes
Developer Experience ( A P I & S D Ks)
  • REST API v1.0 – Bearer token auth, comprehensive endpoints (updated May 2024)
  • Messages/Documents endpoints – SSE streaming, file upload (100MB), webpage import
  • Rate limiting – Standard headers (x-ratelimit-limit, -remaining, -reset)
  • ⚠️ NO SDKs/limited docs – Direct REST calls required, lacks tutorials/samples
  • ✅ No-code pipeline builder – Design pipelines visually, deploy to single API endpoint
  • ✅ Sandbox testing – Rapid iteration and tweaking before production launch
  • ⚠️ No official SDK – REST/GraphQL integration straightforward but no client libraries
  • REST API – Full-featured for agents, projects, data ingestion, chat queries
  • Python SDK – Open-source customgpt-client with full API coverage
  • Postman collections – Pre-built requests for rapid prototyping
  • Webhooks – Real-time event notifications for conversations and leads
  • OpenAI compatible – Use existing OpenAI SDK code with minimal changes
R A G Implementation & Accuracy
  • ✅ TRUE RAG architecture – Pinecone vector database (SOC 2 Type II) with Amazon S3 storage
  • Dynamic chunking – Algorithm adjusts chunk size based on token distribution for optimal retrieval
  • Relevance Score configuration – Adjustable trade-off between accuracy and completeness
  • Reverse Vector Search – Proprietary technique merging AI and user responses for improved relevance
  • ⚠️ NO published benchmarks – No quantitative accuracy metrics or validation vs competitors
N/A
N/A
Performance & Accuracy
  • Response time – Sub-500ms target for queries on Premium/Advanced with GPT-3.5 Turbo
  • User reviews – G2 4.7/5 (150+ reviews), Capterra 4.8/5 (50+ reviews)
  • Scalability – AWS infrastructure with isolated Kubernetes containers (Enterprise)
  • ⚠️ NO public SLA – No uptime guarantees except Enterprise (requires sales)
  • ✅ Hybrid retrieval – Mix semantic, lexical, or graph search for sharper context
  • ✅ Threshold tuning – Balance precision vs recall for your domain requirements
  • ✅ Enterprise scaling – Vector DBs and stores handle high-volume workloads efficiently
  • Sub-second responses – Optimized RAG with vector search and multi-layer caching
  • Benchmark-proven – 13% higher accuracy, 34% faster than OpenAI Assistants API
  • Anti-hallucination tech – Responses grounded only in your provided content
  • OpenGraph citations – Rich visual cards with titles, descriptions, images
  • 99.9% uptime – Auto-scaling infrastructure handles traffic spikes
No- Code Interface & Usability
  • ✅ 15-minute deployment – Three-step: add data, define purpose, test and share
  • 11+ templates – Marketing, HR, IT Support, Sales, Training, Translator
  • Visual prompt builder – Variables, template sharing, intuitive UI
  • User ratings – G2 4.7/5, Capterra 4.8/5; easy setup, learning curve for customization
  • ⚠️ NO flow builder – Prompt-based only, no drag-and-drop designer
  • ✅ Low-code builder – Set up pipelines, chunking, data sources without heavy coding
  • ⚠️ Technical knowledge needed – Understanding embeddings and prompts helps significantly
  • ⚠️ No end-user UI – You build front-end while Dataworkz handles back-end logic
  • 2-minute deployment – Fastest time-to-value in the industry
  • Wizard interface – Step-by-step with visual previews
  • Drag-and-drop – Upload files, paste URLs, connect cloud storage
  • In-browser testing – Test before deploying to production
  • Zero learning curve – Productive on day one
Security & Privacy
  • ⚠️ CODY NOT SOC 2 certified – Early stage startup, infrastructure partners certified
  • Infrastructure compliance – Pinecone (SOC 2 Type II), AWS S3 (PCI-DSS, HIPAA, FedRAMP)
  • GDPR & encryption – AWS EU regions, SSE-S3 at rest, TLS in transit
  • AI training policy – Customer data NOT used for training, OpenAI retains max 30 days
  • ✅ Enterprise-grade security – Encryption, compliance, access controls included [MongoDB: Enterprise Security]
  • ✅ Data sovereignty – Keep data in your environment with bring-your-own infrastructure
  • ✅ Single-tenant VPC – Supports strict isolation for regulatory compliance requirements
  • SOC 2 Type II + GDPR – Third-party audited compliance
  • Encryption – 256-bit AES at rest, SSL/TLS in transit
  • Access controls – RBAC, 2FA, SSO, domain allowlisting
  • Data isolation – Never trains on your data
Observability & Monitoring
  • Conversation logs – Centralized view with search, filtering by bot/date
  • Source verification – Click-through to examine documents used for auditing
  • Retention – 14 days (Basic), 30 days (Premium), 90 days (Advanced)
  • ⚠️ NO alerting/analytics – No real-time alerts, error rates, or funnel tracking
  • ✅ Pipeline-stage monitoring – Track chunking, embeddings, queries with detailed visibility [MongoDB: Lifecycle Tools]
  • ✅ Step-by-step debugging – See which tools agent used and why decisions made
  • ✅ External logging integration – Hooks for logging systems and A/B testing capabilities
  • Real-time dashboard – Query volumes, token usage, response times
  • Customer Intelligence – User behavior patterns, popular queries, knowledge gaps
  • Conversation analytics – Full transcripts, resolution rates, common questions
  • Export capabilities – API export to BI tools and data warehouses
Proprietary R A G Optimizations ( Differentiator)
  • Scratchpad – Save, refine, use derivatives of AI responses for micro-management and iterative enhancement
  • Template Mode – Pre-defined prompts with variables for consistent behavior across conversations
  • Reverse Vector Search – Proprietary technique merging AI and user responses for improved relevance
  • Persist Prompt – Continuous re-emphasis of system prompt preventing instruction drift in long conversations
N/A
N/A
Pricing & Scalability
  • Free – $0/month: 100 credits, 100 docs, 1 member, NO API/crawler
  • Basic – $29/month: 2,500 credits, 1K docs, 3 members, API, GPT-3.5 only
  • Premium – $99/month: 10K credits, 10K docs, 10 members, crawler, white-labeling
  • Advanced – $249/month: 25K credits, 50K docs total, 30 members, 90-day logs
  • Enterprise – Custom: Unlimited credits, SLA, dedicated infrastructure, 6 LLM providers
  • ⚠️ Custom contracts only – No public tiers, typically usage-based enterprise pricing
  • ✅ Massive scalability – Leverage your own infrastructure for huge data and concurrency
  • ✅ Best for large orgs – Ideal for flexible architecture and pricing at scale
  • Standard: $99/mo – 60M words, 10 bots
  • Premium: $449/mo – 300M words, 100 bots
  • Auto-scaling – Managed cloud scales with demand
  • Flat rates – No per-query charges
Support & Ecosystem
  • API docs – developers.meetcody.ai with endpoint reference, curl examples
  • Help Center – intercom.help/cody/en/ with guides, compliance, security bulletins
  • Active Discord – Peer support for troubleshooting and best practices
  • ⚠️ NO phone/live chat – Email and community only; Advanced gets account manager
  • ✅ Tailored onboarding – Enterprise-focused with solution engineering for large customers
  • ✅ MongoDB partnership – Tight integrations with Atlas Vector Search and enterprise support [Case Study]
  • ⚠️ Limited public forums – Direct engineer-to-engineer support vs broad community resources
  • Comprehensive docs – Tutorials, cookbooks, API references
  • Email + in-app support – Under 24hr response time
  • Premium support – Dedicated account managers for Premium/Enterprise
  • Open-source SDK – Python SDK, Postman, GitHub examples
  • 5,000+ Zapier apps – CRMs, e-commerce, marketing integrations
R A G-as-a- Service Assessment
  • ✅ TRUE RAG platform – Pinecone vector database, dynamic chunking, configurable retrieval parameters
  • Target audience – Business teams needing no-code deployment vs developer-centric platforms
  • Differentiators – Source attribution, Focus Mode, native Slack/Discord integrations
  • Enterprise considerations – Lack of direct SOC 2 may block regulated industry adoption
  • Platform type – TRUE RAG-AS-A-SERVICE: Enterprise agentic orchestration layer for custom agents
  • Core architecture – Model-agnostic with full control over LLM, embeddings, vector DB, chunking
  • Agentic focus – Autonomous agents with multi-step reasoning, not simple Q&A chatbots [Agentic RAG]
  • Developer experience – Point-and-click builder, sandbox testing, REST/GraphQL API, agent builder UI
  • Target market – Large enterprises with data teams building sophisticated agents requiring deep customization
  • RAG differentiation – Graph retrieval, hybrid search, threshold tuning, agentic DAG execution
  • Platform type – TRUE RAG-AS-A-SERVICE with managed infrastructure
  • API-first – REST API, Python SDK, OpenAI compatibility, MCP Server
  • No-code option – 2-minute wizard deployment for non-developers
  • Hybrid positioning – Serves both dev teams (APIs) and business users (no-code)
  • Enterprise ready – SOC 2 Type II, GDPR, WCAG 2.0, flat-rate pricing
Competitive Positioning
  • vs CustomGPT – Cody excels in no-code and source attribution; CustomGPT excels in SOC 2 and SDKs
  • vs Vectara – Cody offers simpler pricing and no-code; Vectara provides enterprise-grade benchmarks
  • vs ChatBase/SiteGPT – Cody provides TRUE RAG architecture vs simpler chatbot platforms
  • Market niche – Business-focused RAG for no-code deployment with source transparency
  • Market position – Enterprise agentic RAG platform with point-and-click pipeline builder
  • Target customers – Large enterprises with LLMOps expertise building complex AI agents
  • Key competitors – Deepset Cloud, LangChain/LangSmith, Haystack, Vectara.ai, custom RAG solutions
  • Core advantages – Model-agnostic, agentic architecture, graph retrieval, no-code builder, MongoDB partnership
  • Best for – High-volume complex use cases with existing infrastructure and orchestration needs
  • Market position – Leading RAG platform balancing enterprise accuracy with no-code usability. Trusted by 6,000+ orgs including Adobe, MIT, Dropbox.
  • Key differentiators – #1 benchmarked accuracy • 1,400+ formats • Full white-labeling included • Flat-rate pricing
  • vs OpenAI – 10% lower hallucination, 13% higher accuracy, 34% faster
  • vs Botsonic/Chatbase – More file formats, source citations, no hidden costs
  • vs LangChain – Production-ready in 2 min vs weeks of development
Use Cases
  • Primary departments – Marketing, HR, IT support, Sales with AI-powered assistance
  • Internal operations – FAQs, training, report generation, document search (1000s files)
  • Code assistance – Engineers save 5-6 hours/week, write code 2x faster
  • Industries – Financial (4/6 top US banks), tech (7/10 top), healthcare, government (15+ agencies)
  • Retail – Product recommendations, inventory queries with structured/unstructured data blending [Retail Case Study]
  • Banking – Regulatory compliance, risk assessment with enterprise security and auditability
  • Healthcare – Clinical decision support, medical knowledge bases with HIPAA compliance
  • Enterprise knowledge – Documentation, policy queries with multi-source integration (SharePoint, Confluence, databases)
  • Customer support – Multi-step troubleshooting, automated responses with tool calling and APIs
  • Legal – Contract analysis, regulatory research with audit trails and traceability
  • Customer support – 24/7 AI handling common queries with citations
  • Internal knowledge – HR policies, onboarding, technical docs
  • Sales enablement – Product info, lead qualification, education
  • Documentation – Help centers, FAQs with auto-crawling
  • E-commerce – Product recommendations, order assistance
A I Models
  • Multi-LLM support – GPT-3.5 Turbo, GPT-3.5 16K, GPT-4, Claude Sonnet (paid tiers)
  • Enterprise (6 providers) – Llama 3.1, Claude 3.5 Sonnet, GPT-4o, Gemini 1.5, Mixtral-8x7B
  • Model-agnostic – Stay current with latest LLM updates without retraining
  • ⚠️ NO automatic routing – Manual model selection, no cost/complexity optimization
  • ✅ Model-agnostic – GPT-4, Claude, Llama, open-source models fully supported
  • ✅ Public APIs – AWS Bedrock and OpenAI API integration for managed access
  • ✅ Private hosting – Host open-source models in your VPC for sovereignty
  • ✅ Composable stack – Choose embedding, vector DB, chunking, LLM independently
  • ✅ No lock-in – Switch models without platform migration for cost or compliance
  • OpenAI – GPT-5.1 (Optimal/Smart), GPT-4 series
  • Anthropic – Claude 4.5 Opus/Sonnet (Enterprise)
  • Auto-routing – Intelligent model selection for cost/performance
  • Managed – No API keys or fine-tuning required
R A G Capabilities
  • ✅ Pinecone vector database – SOC 2 Type II certified, Amazon S3 storage (SSE-S3)
  • Dynamic chunking – Adjusts chunk size based on token distribution for optimal retrieval
  • Token distribution – Split between context, history, response (70%/10%/20%)
  • Context window – Claude 2 integration provides up to 100K tokens
  • ✅ Advanced pipeline builder – Point-and-click RAG configuration with fine-grained control RAG-as-a-Service
  • ✅ Agentic architecture – Multi-step tasks, external tool calls, adaptive reasoning [Agentic RAG]
  • ✅ Hybrid retrieval – Semantic, lexical, graph search for accuracy and context
  • ✅ Graph-optimized – Relationship-aware context for interlinked documents [Graph Capabilities]
  • ✅ Dynamic tool selection – Agents choose knowledge base, DB, or API automatically
  • GPT-4 + RAG – Outperforms OpenAI in independent benchmarks
  • Anti-hallucination – Responses grounded in your content only
  • Automatic citations – Clickable source links in every response
  • Sub-second latency – Optimized vector search and caching
  • Scale to 300M words – No performance degradation at scale
Customization & Flexibility ( Behavior & Knowledge)
  • Real-time knowledge updates – Manual retraining available for immediate updates
  • Focus Mode – Inject up to 1,000 specific documents for targeted context
  • Bot personality – Adjust behavior, tone, focus with custom starters
  • ⚠️ NO programmatic personality – Dashboard-only, cannot modify per-user or via API
  • ✅ Multi-step reasoning – Scenario logic, tool calls, unified agent workflows
  • ✅ Data blending – Combine structured APIs/DBs with unstructured docs seamlessly
  • ✅ Full retrieval control – Customize chunking, metadata, and retrieval algorithms completely
  • Live content updates – Add/remove content with automatic re-indexing
  • System prompts – Shape agent behavior and voice through instructions
  • Multi-agent support – Different bots for different teams
  • Smart defaults – No ML expertise required for custom behavior
Limitations & Considerations
  • Learning curve – Easy setup but customization requires expertise for specific needs
  • Accuracy data-dependent – Response quality relies heavily on knowledge base quality
  • Complex coding challenges – Struggles with deeper logic, scalability, multi-step coding
  • ⚠️ NO YouTube/cloud integrations – No video transcripts, Google Drive/Dropbox/Notion need Zapier
  • Performance with large data – Speed may slow with large datasets on lower-end systems
  • ⚠️ No built-in UI – API-first platform requires you to build front-end interface
  • ⚠️ Technical expertise required – Best for LLMOps teams understanding embeddings, prompts, RAG architecture
  • ⚠️ Custom pricing only – No transparent public tiers, requires sales engagement for quotes
  • ⚠️ Enterprise focus – May be overkill for small teams or simple chatbot cases
  • ⚠️ Infrastructure requirements – BYOI model needs existing cloud infrastructure and data engineering capabilities
  • Managed service – Less control over RAG pipeline vs build-your-own
  • Model selection – OpenAI + Anthropic only; no Cohere, AI21, open-source
  • Real-time data – Requires re-indexing; not ideal for live inventory/prices
  • Enterprise features – Custom SSO only on Enterprise plan
Customization & Branding
N/A
  • ✅ 100% front-end control – No built-in UI means complete look and feel ownership
  • ✅ Deep behavior tweaks – Customize prompt templates and scenario configs extensively
  • ✅ Multiple personas – Create unlimited agent personas with different rule sets
  • Full white-labeling included – Colors, logos, CSS, custom domains at no extra cost
  • 2-minute setup – No-code wizard with drag-and-drop interface
  • Persona customization – Control AI personality, tone, response style via pre-prompts
  • Visual theme editor – Real-time preview of branding changes
  • Domain allowlisting – Restrict embedding to approved sites only
Additional Considerations
N/A
  • ✅ Graph-optimized retrieval – Specialized for interlinked docs with relationships [MongoDB Reference]
  • ✅ AI orchestration layer – Call APIs or trigger actions as part of answers
  • ⚠️ Requires LLMOps expertise – Best for teams wanting deep customization, not prefab chatbots
  • ✅ Tailor-made agents – Focuses on custom AI agents vs out-of-box chat tool
  • Time-to-value – 2-minute deployment vs weeks with DIY
  • Always current – Auto-updates to latest GPT models
  • Proven scale – 6,000+ organizations, millions of queries
  • Multi-LLM – OpenAI + Claude reduces vendor lock-in
Security & Compliance
N/A
  • ✅ Enterprise-grade – Encryption, compliance, access controls for large organizations [Security Features]
  • ✅ Audit trails – Every interaction, tool call, data access audited for transparency
  • ✅ Data sovereignty – Bring-your-own-infrastructure keeps data in your environment completely
  • ✅ Compliance ready – Architecture supports GDPR, HIPAA, SOC 2 through flexible deployment
  • SOC 2 Type II + GDPR – Regular third-party audits, full EU compliance
  • 256-bit AES encryption – Data at rest; SSL/TLS in transit
  • SSO + 2FA + RBAC – Enterprise access controls with role-based permissions
  • Data isolation – Never trains on customer data
  • Domain allowlisting – Restrict chatbot to approved domains
Pricing & Plans
N/A
  • ⚠️ Custom contracts – Tailored pricing, no public tiers, requires sales engagement
  • ✅ Credit-based usage – 2M rows per credit for data movement, usage-based model
  • ✅ AWS Marketplace – Available for streamlined enterprise procurement [AWS Marketplace]
  • ✅ BYOI savings – Use existing infrastructure (databases, vector stores) to reduce costs
  • Standard: $99/mo – 10 chatbots, 60M words, 5K items/bot
  • Premium: $449/mo – 100 chatbots, 300M words, 20K items/bot
  • Enterprise: Custom – SSO, dedicated support, custom SLAs
  • 7-day free trial – Full Standard access, no charges
  • Flat-rate pricing – No per-query charges, no hidden costs
Support & Documentation
N/A
  • ✅ Enterprise onboarding – Tailored solution engineering for large organizations with complex needs
  • ✅ Direct engineering support – Engineer-to-engineer technical implementation and optimization assistance
  • ✅ Product documentation – Platform setup, pipeline config, agentic workflows covered [Product Docs]
  • ✅ MongoDB partnership – Joint support for Atlas Vector Search and enterprise deployments
  • Documentation hub – Docs, tutorials, API references
  • Support channels – Email, in-app chat, dedicated managers (Premium+)
  • Open-source – Python SDK, Postman, GitHub examples
  • Community – User community + 5,000 Zapier integrations
Core Agent Features
N/A
  • ✅ Agentic RAG – Multi-step reasoning, external tools, adaptive context-based operation [Agentic Capabilities]
  • ✅ Agent memory – Conversational history, user preferences, business context via RAG pipelines
  • ✅ DAG task execution – Complex tasks decomposed into interdependent sub-tasks with parallelization [Multi-Step Reasoning]
  • ✅ LLM Compiler – Identifies optimal sub-task sequence with parallel execution when possible
  • ✅ External API integration – Create CRM leads, support tickets, trigger actions dynamically [Agent Builder]
  • ✅ Continuous learning – Agent frameworks support context switching and adaptation over time
  • Custom AI Agents – Autonomous GPT-4/Claude agents for business tasks
  • Multi-Agent Systems – Specialized agents for support, sales, knowledge
  • Memory & Context – Persistent conversation history across sessions
  • Tool Integration – Webhooks + 5,000 Zapier apps for automation
  • Continuous Learning – Auto re-indexing without manual retraining

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Final Thoughts

Final Verdict: CODY AI vs Dataworkz

After analyzing features, pricing, performance, and user feedback, both CODY AI and Dataworkz are capable platforms that serve different market segments and use cases effectively.

When to Choose CODY AI

  • You value true rag architecture with pinecone vector database and configurable retrieval parameters (relevance score, token distribution, focus mode)
  • Source attribution with every response - click-through to exact documents used for generation (transparency and trust differentiator)
  • Focus Mode unique capability: inject up to 1,000 specific documents into conversation context for targeted responses vs full knowledge base

Best For: TRUE RAG architecture with Pinecone vector database and configurable retrieval parameters (relevance score, token distribution, Focus Mode)

When to Choose Dataworkz

  • You value free tier available for testing
  • No-code approach simplifies development
  • Flexible LLM and vector database choices

Best For: Free tier available for testing

Migration & Switching Considerations

Switching between CODY AI and Dataworkz requires careful planning. Consider data export capabilities, API compatibility, and integration complexity. Both platforms offer migration support, but expect 2-4 weeks for complete transition including testing and team training.

Pricing Comparison Summary

CODY AI starts at $29/month, while Dataworkz begins at custom pricing. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.

Our Recommendation Process

  1. Start with a free trial - Both platforms offer trial periods to test with your actual data
  2. Define success metrics - Response accuracy, latency, user satisfaction, cost per query
  3. Test with real use cases - Don't rely on generic demos; use your production data
  4. Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
  5. Check vendor stability - Review roadmap transparency, update frequency, and support quality

For most organizations, the decision between CODY AI and Dataworkz comes down to specific requirements rather than overall superiority. Evaluate both platforms with your actual data during trial periods, focusing on accuracy, latency, ease of integration, and total cost of ownership.

📚 Next Steps

Ready to make your decision? We recommend starting with a hands-on evaluation of both platforms using your specific use case and data.

  • • Review: Check the detailed feature comparison table above
  • • Test: Sign up for free trials and test with real queries
  • • Calculate: Estimate your monthly costs based on expected usage
  • • Decide: Choose the platform that best aligns with your requirements

Last updated: February 21, 2026 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.

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Priyansh Khodiyar's avatar

Priyansh Khodiyar

DevRel at CustomGPT.ai. Passionate about AI and its applications. Here to help you navigate the world of AI tools and make informed decisions for your business.

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