In this comprehensive guide, we compare Dataworkz and Supavec 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 Dataworkz and Supavec, 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 Dataworkz if: you value free tier available for testing
Choose Supavec if: you value 100% open source with no vendor lock-in
About Dataworkz
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
About Supavec
Supavec is the open source rag as a service platform. SupaVec is an open-source RAG platform that serves as an alternative to Carbon.ai. Built on transparency and data sovereignty, it allows developers to build powerful RAG applications with complete control over their infrastructure, supporting any data source at any scale. Founded in 2024, headquartered in Remote, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
84/100
Starting Price
Custom
Key Differences at a Glance
In terms of user ratings, both platforms score similarly in overall satisfaction. From a cost perspective, pricing is comparable. The platforms also differ in their primary focus: RAG Platform 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
Dataworkz
Supavec
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Brings in a mix of knowledge sources through a point-and-click RAG pipeline builder
[MongoDB Reference].
Lets you wire up SharePoint, Confluence, databases, or document repositories with just a few settings.
Gives fine-grained control over chunk sizes and embedding strategies.
Happy to blend multiple sources—pull docs and hit a live database in the same pipeline.
No one-click Google Drive or Notion connectors—you’ll script the fetch and hit the API yourself.
Because it’s open source, you can build connectors to anything—Postgres, Mongo, S3, you name it.
Runs on Supabase and scales sideways, chunking millions of docs for fast retrieval.
Lets you ingest more than 1,400 file formats—PDF, DOCX, TXT, Markdown, HTML, and many more—via simple drag-and-drop or API.
Crawls entire sites through sitemaps and URLs, automatically indexing public help-desk articles, FAQs, and docs.
Turns multimedia into text on the fly: YouTube videos, podcasts, and other media are auto-transcribed with built-in OCR and speech-to-text.
View Transcription Guide
Connects to Google Drive, SharePoint, Notion, Confluence, HubSpot, and more through API connectors or Zapier.
See Zapier Connectors
Supports both manual uploads and auto-sync retraining, so your knowledge base always stays up to date.
Step-by-step debugging shows which tools the agent used and why.
Hooks into external logging systems and supports A/B tests to fine-tune results.
No dashboard baked in—log requests yourself or use Supabase metrics when self-hosting.
Hosted plan shows basic call counts; no transcript analytics out of the box.
Need deep insights? Wire up your own monitoring layer.
Designed to play nicely with external logging tools, not ship its own.
Comes with a real-time analytics dashboard tracking query volumes, token usage, and indexing status.
Lets you export logs and metrics via API to plug into third-party monitoring or BI tools.
Analytics API
Provides detailed insights for troubleshooting and ongoing optimization.
Support & Ecosystem
Geared toward large enterprises with tailored onboarding and solution engineering.
Partners with MongoDB and other enterprise tech—tight integrations available
[Case Study].
Focuses on direct engineer-to-engineer support over broad public forums.
Community help via GitHub/Discord; paid plans unlock email or priority support.
[Docs]
Open-source means forks, PRs, and home-grown connectors are welcome.
Docs are lean—mostly endpoint references rather than big tutorials.
Code samples pop up in the community, but it’s not a huge library yet.
Supplies rich docs, tutorials, cookbooks, and FAQs to get you started fast.
Developer Docs
Offers quick email and in-app chat support—Premium and Enterprise plans add dedicated managers and faster SLAs.
Enterprise Solutions
Benefits from an active user community plus integrations through Zapier and GitHub resources.
Additional Considerations
Supports graph-optimized retrieval for interlinked docs
[MongoDB Reference].
Can act as a central AI orchestration layer—call APIs or trigger actions as part of an answer.
Best for teams with LLMOps expertise who want deep customization, not a prefab chatbot.
Aims for tailor-made AI agents rather than an out-of-box chat tool.
No vendor lock-in: transparent code, offline option, host wherever you like.
Focuses on core RAG—no SSO, dashboards, or fancy UI included.
Great for devs who want full control or must keep data in-house.
Conversation flow, advanced prompts, fancy UI—all yours to build.
Slashes engineering overhead with an all-in-one RAG platform—no in-house ML team required.
Gets you to value quickly: launch a functional AI assistant in minutes.
Stays current with ongoing GPT and retrieval improvements, so you’re always on the latest tech.
Balances top-tier accuracy with ease of use, perfect for customer-facing or internal knowledge projects.
No- Code Interface & Usability
No-code / low-code builder helps set up pipelines, chunking, and data sources.
Exposes technical concepts—knowing embeddings and prompts helps.
No end-user UI included; you build the front-end while Dataworkz handles the back-end logic.
No drag-and-drop dashboard—everything's via API or CLI.
Meant for code-first teams who'll bolt it into their own chat or workflow.
Self-hosters can craft custom GUIs on top, but Supavec keeps the slate blank.
If you want a business-user UI like CustomGPT, you'll layer that yourself.
Offers a wizard-style web dashboard so non-devs can upload content, brand the widget, and monitor performance.
Supports drag-and-drop uploads, visual theme editing, and in-browser chatbot testing.
User Experience Review
Uses role-based access so business users and devs can collaborate smoothly.
Competitive Positioning
Market position: Enterprise agentic RAG platform with point-and-click pipeline builder for organizations needing custom AI orchestration without heavy coding
Target customers: Large enterprises with LLMOps expertise, data engineering teams building complex AI agents, and organizations requiring agentic architecture with multi-step reasoning and tool use capabilities
Key competitors: Deepset Cloud, LangChain/LangSmith, Haystack, Vectara.ai, and custom-built RAG solutions using MongoDB Atlas Vector Search
Competitive advantages: Model-agnostic with full control over LLM/embedding choices, agentic architecture for multi-step reasoning and dynamic tool selection, graph-optimized retrieval for interlinked documents, no-code pipeline builder with sandbox testing, MongoDB partnership for enterprise integrations, and bring-your-own-infrastructure flexibility (DB, embeddings, VPC)
Pricing advantage: Custom enterprise contracts with usage-based pricing; no public tiers but typically competitive for organizations with existing infrastructure that want orchestration layer without SaaS lock-in; best value for high-volume, complex use cases
Use case fit: Best for enterprises building sophisticated AI agents requiring multi-step reasoning, organizations needing to blend structured APIs/databases with unstructured documents seamlessly, and teams with ML expertise wanting deep customization of chunking, retrieval algorithms, and orchestration logic without building from scratch
Market position: MIT-licensed open-source RAG API built on Supabase, offering lightweight alternative to Carbon.ai with self-hosting capability and minimal API surface
Target customers: Developers building custom RAG applications on budget, startups wanting to avoid RAG platform costs, and organizations requiring self-hosted solutions with Supabase infrastructure for data sovereignty
Key competitors: Carbon.ai, LangChain, SimplyRetrieve, and hosted RAG APIs like CustomGPT/Pinecone Assistant
Competitive advantages: MIT open-source license with no vendor lock-in, Supabase foundation for familiar infrastructure, model-agnostic with easy LLM swapping (GPT-3.5, GPT-4, self-hosted), REST API simplicity with straightforward endpoints, privacy-focused with self-hosting option keeping data on your servers, and minimal abstraction enabling deep customization
Pricing advantage: Free (MIT license) for self-hosting; hosted plans extremely affordable ($190/year Basic for 750 calls/month, $1,490/year Enterprise for 5K calls/month); best value for low-volume applications or teams with Supabase expertise wanting to avoid expensive RAG platforms; 40-90% cheaper than commercial alternatives
Use case fit: Perfect for developers wanting lightweight RAG backend without heavy frameworks, startups minimizing costs with self-hosting on existing Supabase infrastructure, and teams building custom chatbot front-ends needing simple REST API for retrieval without paying for unused dashboard features
Market position: Leading all-in-one RAG platform balancing enterprise-grade accuracy with developer-friendly APIs and no-code usability for rapid deployment
Target customers: Mid-market to enterprise organizations needing production-ready AI assistants, development teams wanting robust APIs without building RAG infrastructure, and businesses requiring 1,400+ file format support with auto-transcription (YouTube, podcasts)
Key competitors: OpenAI Assistants API, Botsonic, Chatbase.co, Azure AI, and custom RAG implementations using LangChain
Competitive advantages: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, SOC 2 Type II + GDPR compliance, full white-labeling included, OpenAI API endpoint compatibility, hosted MCP Server support (Claude, Cursor, ChatGPT), generous data limits (60M words Standard, 300M Premium), and flat monthly pricing without per-query charges
Pricing advantage: Transparent flat-rate pricing at $99/month (Standard) and $449/month (Premium) with generous included limits; no hidden costs for API access, branding removal, or basic features; best value for teams needing both no-code dashboard and developer APIs in one platform
Use case fit: Ideal for businesses needing both rapid no-code deployment and robust API capabilities, organizations handling diverse content types (1,400+ formats, multimedia transcription), teams requiring white-label chatbots with source citations for customer-facing or internal knowledge projects, and companies wanting all-in-one RAG without managing ML infrastructure
A I Models
Model-agnostic architecture: Supports GPT-4, Claude, Llama, and other open-source models - full flexibility in LLM selection
Public LLM APIs: Integration with AWS Bedrock and OpenAI APIs for managed model access
Private hosting: Option to host open-source foundation models in your own VPC for data sovereignty and cost control
Composable AI stack: Choose your own embedding model, vector database, chunking strategy, and LLM independently
No vendor lock-in: Flexibility to switch models based on performance, cost, or compliance requirements without platform migration
Model-agnostic architecture: Defaults to GPT-3.5 Turbo for cost-effectiveness, with full support for GPT-4, GPT-4-turbo, and any OpenAI-compatible models
Self-hosted model support: Bring your own LLM - compatible with self-hosted models like Llama, Mistral, or custom fine-tuned models via API endpoints
No model lock-in: Switch between models by changing configuration or prompt path in code without platform restrictions
No markup on AI costs: Users connect their own OpenAI API keys or self-hosted endpoints, paying providers directly without Supavec markup
Note: No built-in model routing: No automatic model selection or load balancing - developers must implement routing logic manually
Note: No prompt optimization layer: Plain RAG implementation without advanced prompt engineering or anti-hallucination guardrails
Quality dependency: Output quality rests entirely on chosen LLM and developer's prompt engineering skills
Primary models: GPT-5.1 and 4 series from OpenAI, and Anthropic's Claude 4.5 (opus and sonnet) for enterprise needs
Automatic model selection: Balances cost and performance by automatically selecting the appropriate model for each request
Model Selection Details
Proprietary optimizations: Custom prompt engineering and retrieval enhancements for high-quality, citation-backed answers
Managed infrastructure: All model management handled behind the scenes - no API keys or fine-tuning required from users
Anti-hallucination technology: Advanced mechanisms ensure chatbot only answers based on provided content, improving trust and factual accuracy
R A G Capabilities
Advanced RAG pipeline: Point-and-click builder for configuring and optimizing each aspect of RAG with fine-grained control
RAG-as-a-Service
Agentic architecture: LLM-powered agents that reason through multi-step tasks, call external tools/APIs, and adapt based on context
Agentic RAG
Hybrid retrieval: Mix semantic and lexical retrieval, or use graph search for sharper context and improved accuracy
Hallucination mitigation: RAG references source data to reduce hallucinations and improve factual accuracy
Graph-optimized retrieval: Specialized for interlinked documents with relationship-aware context
Graph Capabilities
Threshold tuning: Balance precision vs. recall for domain-specific requirements
Dynamic tool selection: Agents decide when to query knowledge bases vs. live databases vs. external APIs based on question context
Standard RAG architecture: Document chunking with vector embeddings stored in Postgres pgvector extension for semantic search
Embedding generation: Automatic embedding creation during document upload using OpenAI embedding models or custom embedding endpoints
Vector search: Postgres vector search with cosine similarity for retrieval, handling millions of chunks efficiently
Re-indexing speed: Almost instant document re-embedding when updating or overwriting knowledge sources
Metadata support: Custom metadata tagging and filtering capabilities for organized knowledge management
Note: No advanced RAG features: No hybrid search (semantic + keyword), no reranking, no multi-query retrieval, no query expansion
Note: No hallucination detection: No built-in citation validation, factual consistency scoring, or confidence thresholds - developers must implement manually
Note: No retrieval parameter controls: Chunking strategy, similarity thresholds, and top-k configuration require code-level changes
Core architecture: GPT-4 combined with Retrieval-Augmented Generation (RAG) technology, outperforming OpenAI in RAG benchmarks
RAG Performance
Anti-hallucination technology: Advanced mechanisms reduce hallucinations and ensure responses are grounded in provided content
Benchmark Details
Automatic citations: Each response includes clickable citations pointing to original source documents for transparency and verification
Optimized pipeline: Efficient vector search, smart chunking, and caching for sub-second reply times
Scalability: Maintains speed and accuracy for massive knowledge bases with tens of millions of words
Context-aware conversations: Multi-turn conversations with persistent history and comprehensive conversation management
Source verification: Always cites sources so users can verify facts on the spot
Use Cases
Retail and e-commerce: Product recommendations, inventory queries, customer service with agentic RAG blending structured data (inventory) and unstructured content (product guides)
Retail Case Study
Banking and financial services: Regulatory compliance queries, customer onboarding, risk assessment with enterprise-grade security and auditability
Healthcare: Clinical decision support, patient information systems, medical knowledge bases with HIPAA-compliant deployment options
Enterprise knowledge management: Internal documentation, policy queries, onboarding assistance with multi-source data integration (SharePoint, Confluence, databases)
Customer support: Multi-step troubleshooting, ticket routing, automated responses with tool calling and API integration
Research and analytics: Document analysis, research assistance, data exploration with graph-optimized retrieval for interlinked content
Manufacturing: Equipment manuals, maintenance procedures, supply chain queries with structured and unstructured data blending
Legal and compliance: Contract analysis, regulatory research, compliance checking with audit trails and traceability
Custom chatbot backends: Ideal for developers building custom chat interfaces needing simple RAG API without heavy platform overhead
Self-hosted knowledge retrieval: Perfect for organizations requiring data sovereignty with Supabase infrastructure for compliance (GDPR, HIPAA when self-hosted)
Budget-conscious RAG applications: Startups and small teams minimizing costs with MIT open-source license and affordable hosted plans ($190-$1,490/year)
Supabase-native projects: Teams already using Supabase can integrate Supavec seamlessly without additional infrastructure complexity
Developer-first RAG: Code-first teams wanting full control over RAG implementation, eschewing GUI dashboards for API-driven workflows
Not ideal for: Non-technical users requiring no-code interfaces, enterprises needing advanced RAG features (hybrid search, reranking), or teams requiring built-in analytics/monitoring
Not ideal for: Production applications requiring hallucination detection, citation validation, or confidence scoring without custom development
Customer support automation: AI assistants handling common queries, reducing support ticket volume, providing 24/7 instant responses with source citations
Internal knowledge management: Employee self-service for HR policies, technical documentation, onboarding materials, company procedures across 1,400+ file formats
Sales enablement: Product information chatbots, lead qualification, customer education with white-labeled widgets on websites and apps
Documentation assistance: Technical docs, help centers, FAQs with automatic website crawling and sitemap indexing
Educational platforms: Course materials, research assistance, student support with multimedia content (YouTube transcriptions, podcasts)
Healthcare information: Patient education, medical knowledge bases (SOC 2 Type II compliant for sensitive data)
E-commerce: Product recommendations, order assistance, customer inquiries with API integration to 5,000+ apps via Zapier
SaaS onboarding: User guides, feature explanations, troubleshooting with multi-agent support for different teams
Security & Compliance
Enterprise-grade security: Encryption, compliance, and access controls built for large organizations
Security Features
Audit and traceability: Every interaction, tool invocation, and data access can be audited and traced for compliance and transparency
Data sovereignty: Bring-your-own-infrastructure deployment options - keep data entirely in your environment (databases, embeddings, VPC)
Single-tenant hosting: VPC deployment for strict isolation and compliance with regulatory requirements
Access controls: Role-based access control and fine-grained permissions for multi-team environments
Compliance readiness: Architecture supports GDPR, HIPAA, SOC 2, and other regulatory frameworks through flexible deployment models
Self-hosting advantage: MIT license enables complete data sovereignty - all data stays on your servers for strict compliance requirements
[Privacy note]
Supabase security foundation: Row-level security (RLS) fences off each team's data when using hosted Supavec on Supabase infrastructure
No model training: Your documents never used for LLM training - data remains yours with zero retention by OpenAI or other providers
GDPR/HIPAA ready: Self-hosting enables GDPR and HIPAA compliance when deployed on compliant infrastructure - enterprises can go dedicated or on-premises
Encryption: Standard HTTPS encryption for API calls; at-rest encryption depends on hosting infrastructure (Supabase provides AES-256)
Note: No SOC 2 certification: Open-source project lacks formal SOC 2 Type II, ISO 27001, or other enterprise compliance certifications for hosted plans
Note: No built-in access controls: Authentication, authorization, and RBAC must be implemented by developers in their application layer
Note: Limited hosted security features: Hosted plans lack SSO/SAML, IP whitelisting, or advanced security controls without custom configuration
Encryption: SSL/TLS for data in transit, 256-bit AES encryption for data at rest
SOC 2 Type II certification: Industry-leading security standards with regular third-party audits
Security Certifications
GDPR compliance: Full compliance with European data protection regulations, ensuring data privacy and user rights
Access controls: Role-based access control (RBAC), two-factor authentication (2FA), SSO integration for enterprise security
Data isolation: Customer data stays isolated and private - platform never trains on user data
Domain allowlisting: Ensures chatbot appears only on approved sites for security and brand protection
Secure deployments: ChatGPT Plugin support for private use cases with controlled access
Pricing & Plans
Enterprise contracts: Custom pricing tailored to organization size, usage volume, and deployment requirements - no public tiers
Credit-based pricing: Credits debited when functions are performed on data (transformations, logic), with 2M rows moved per credit for data movement
Usage-based model: Pay for what you use - ideal for variable workloads and avoiding over-provisioning
AWS Marketplace: Available for procurement through AWS Marketplace for streamlined enterprise purchasing
AWS Marketplace
Scalability: Pricing scales with usage - cost-effective for high-volume, complex use cases where control matters
Open-source (Free): MIT-licensed for self-hosting - pay only your infrastructure costs (Supabase, server, storage) with unlimited API calls and no vendor fees
Hosted Free tier: 100 API calls per month for development and testing
[Pricing]
Basic Plan: $190/year ($15.83/month equivalent) - 750 API calls per month, hosted infrastructure, automatic backups, email support
Enterprise Plan: $1,490/year ($124.17/month equivalent) - 5,000 API calls per month, priority support, SLA guarantees, dedicated resources
No per-document charges: Storage not metered separately - only query volume counts toward plan limits
No user seat fees: Pricing based purely on API call volume, not team size or number of developers
Need more calls? Negotiate custom limits with hosted provider or self-host to eliminate caps entirely
Value proposition: 40-90% cheaper than commercial RAG platforms - Basic plan costs less than 1 month of competing platforms while providing annual service
Standard Plan: $99/month or $89/month annual - 10 custom chatbots, 5,000 items per chatbot, 60 million words per bot, basic helpdesk support, standard security
View Pricing
Premium Plan: $499/month or $449/month annual - 100 custom chatbots, 20,000 items per chatbot, 300 million words per bot, advanced support, enhanced security, additional customization
Enterprise Plan: Custom pricing - Comprehensive AI solutions, highest security and compliance, dedicated account managers, custom SSO, token authentication, priority support with faster SLAs
Enterprise Solutions
7-Day Free Trial: Full access to Standard features without charges - available to all users
Annual billing discount: Save 10% by paying upfront annually ($89/mo Standard, $449/mo Premium)
Flat monthly rates: No per-query charges, no hidden costs for API access or white-labeling (included in all plans)
Managed infrastructure: Auto-scaling cloud infrastructure included - no additional hosting or scaling fees
Support & Documentation
Enterprise onboarding: Tailored onboarding and solution engineering for large organizations with complex requirements
Direct engineering support: Engineer-to-engineer support focused on technical implementation and optimization
Active community: User community plus 5,000+ app integrations through Zapier ecosystem
Regular updates: Platform stays current with ongoing GPT and retrieval improvements automatically
Limitations & Considerations
No built-in UI: Platform is API-first with no prefab chat widget - you must build or bring your own front-end interface
Technical expertise required: Best for teams with LLMOps expertise who understand embeddings, prompts, and RAG architecture - not ideal for non-technical users
Custom pricing only: No transparent public pricing tiers - requires sales engagement for pricing quotes and contracts
Enterprise focus: Designed for large organizations - may be overkill for small teams or simple chatbot use cases
Setup complexity: Point-and-click builder simplifies pipeline creation but still requires understanding of RAG concepts and architecture
Limited pre-built templates: Platform provides flexibility but fewer out-of-box solutions compared to turnkey chatbot platforms
No official SDK: REST/GraphQL integration is straightforward but lacks dedicated client libraries for popular languages
Infrastructure requirements: Bring-your-own-infrastructure model requires existing cloud infrastructure and data engineering capabilities
No GUI/dashboard: Everything via API or CLI - no business-user interface for content management, analytics, or configuration
Developer-only tool: Requires coding skills for setup, integration, and maintenance - non-technical teams cannot use without developer support
Basic RAG only: Standard retrieval-augmented generation without advanced features like hybrid search, query reranking, multi-query fusion, or query expansion
No observability built-in: No metrics dashboard, conversation analytics, or performance monitoring - must wire up your own logging layer
Manual hallucination handling: No built-in citation validation, confidence scoring, or factual consistency checks - developers must implement safeguards
Limited connectors: No one-click Google Drive, Notion, or cloud storage integrations - must script data fetching and API uploads manually
No conversation management: Stateless API calls without chat history, multi-turn context, or session management - build conversation layer yourself
Infrastructure knowledge required: Self-hosting requires Supabase, Postgres, and vector database expertise - not plug-and-play for non-DevOps teams
Minimal abstraction: Intentionally low-level API design provides control but requires more integration work than higher-level RAG platforms
Managed service approach: Less control over underlying RAG pipeline configuration compared to build-your-own solutions like LangChain
Vendor lock-in: Proprietary platform - migration to alternative RAG solutions requires rebuilding knowledge bases
Model selection: Limited to OpenAI (GPT-5.1 and 4 series) and Anthropic (Claude, opus and sonnet 4.5) - no support for other LLM providers (Cohere, AI21, open-source models)
Pricing at scale: Flat-rate pricing may become expensive for very high-volume use cases (millions of queries/month) compared to pay-per-use models
Customization limits: While highly configurable, some advanced RAG techniques (custom reranking, hybrid search strategies) may not be exposed
Language support: Supports 90+ languages but performance may vary for less common languages or specialized domains
Real-time data: Knowledge bases require re-indexing for updates - not ideal for real-time data requirements (stock prices, live inventory)
Enterprise features: Some advanced features (custom SSO, token authentication) only available on Enterprise plan with custom pricing
Core Agent Features
Agentic RAG Architecture: LLM-powered agents that reason through multi-step tasks, call external tools/APIs, and adapt based on context - built for autonomous operation
Agentic Capabilities
Agent Memory System: Derived from three key artifacts - conversational history, user preferences, and business context from external sources via RAG pipelines and enterprise knowledge graphs
Complex Task Execution: Reasoning capabilities decompose complex tasks into multiple interdependent sub-tasks represented as directed acyclic graphs (DAGs) for parallel execution where possible
Multi-Step Reasoning
LLM Compiler Integration: Identifies optimal sequence for executing sub-tasks with parallel execution when dependencies allow - implements advanced task orchestration patterns
Dynamic Tool Selection: Agents decide when to query knowledge bases versus live databases versus external APIs based on question context and system state
External API Integration: Invoke external APIs to create CRM leads, create support tickets, lookup order details, or trigger actions as part of generating answers
Agent Builder
Continuous Learning & Adaptation: Agent frameworks support continuous learning and context switching across workflows - agents not only retrieve and generate but also plan multi-step tasks and adapt over time
Agent Builder Interface: Easy-to-use interface to assemble Agentic RAG Applications with minimal technical knowledge - takes business requirements and generates agent definitions
Stateless RAG Architecture: Pure retrieval and generation without built-in conversation state—developers implement multi-turn context and session management in application layer
Model-Agnostic Generation: Defaults to GPT-3.5 but supports GPT-4, self-hosted LLMs (Llama, Mistral), and any OpenAI-compatible models—no vendor lock-in for generation
Postgres Vector Search: Fast approximate nearest neighbor search using pgvector extension with cosine similarity—handles millions of chunks efficiently at enterprise scale
Metadata Filtering: Custom metadata tagging and filtering capabilities enabling organized knowledge management and multi-tenant architectures
Real-Time Re-Indexing: Almost instant document re-embedding when updating or overwriting knowledge sources—no lengthy reprocessing delays
REST API Foundation: Straightforward endpoints for file uploads, text uploads, and search with plain-JSON responses—easy integration from any programming language
Supabase Integration: Built on Supabase infrastructure leveraging PostgreSQL, Row-Level Security (RLS), and battle-tested backend for familiar deployment
LIMITATION - No Built-In Chat UI: API-only platform requiring developers to build custom chat interfaces—not a turnkey chatbot solution with widgets
LIMITATION - No Lead Capture: No built-in lead generation, email collection, or CRM integration capabilities—must be implemented at application layer
LIMITATION - No Human Handoff: No escalation workflows, live agent transfer, or fallback mechanisms—conversational features are developer responsibility
LIMITATION - No Multi-Channel Integrations: No native Slack, Teams, WhatsApp, or messaging platform connectors—developers build integration layer
LIMITATION - No Session Management: Stateless API design without conversation history tracking or multi-turn context retention—application must manage state
LIMITATION - No Advanced RAG: Missing hybrid search, reranking, knowledge graphs, multi-query retrieval, query expansion found in enterprise platforms
LIMITATION - No Observability Dashboard: No analytics, conversation metrics, or performance monitoring UI—must integrate external logging tools
Custom AI Agents: Build autonomous agents powered by GPT-4 and Claude that can perform tasks independently and make real-time decisions based on business knowledge
Decision-Support Capabilities: AI agents analyze proprietary data to provide insights, recommendations, and actionable responses specific to your business domain
Multi-Agent Systems: Deploy multiple specialized AI agents that can collaborate and optimize workflows in areas like customer support, sales, and internal knowledge management
Memory & Context Management: Agents maintain conversation history and persistent context for coherent multi-turn interactions
View Agent Documentation
Tool Integration: Agents can trigger actions, integrate with external APIs via webhooks, and connect to 5,000+ apps through Zapier for automated workflows
Hyper-Accurate Responses: Leverages advanced RAG technology and retrieval mechanisms to deliver context-aware, citation-backed responses grounded in your knowledge base
Continuous Learning: Agents improve over time through automatic re-indexing of knowledge sources and integration of new data without manual retraining
R A G-as-a- Service Assessment
Platform Type: TRUE RAG-AS-A-SERVICE PLATFORM - enterprise agentic RAG orchestration layer designed for custom AI agent development with point-and-click pipeline builder
Core Architecture: Model-agnostic RAG infrastructure with full control over LLM selection, embedding models, vector databases, and chunking strategies - composable AI stack approach
Agentic Focus: Built around LLM-powered autonomous agents that reason through multi-step tasks, call external tools/APIs, and adapt based on user interactions - not simple Q&A chatbots
Agentic RAG
Developer Experience: Point-and-click pipeline builder with sandbox testing, REST/GraphQL API integration, and agent builder for minimal-code assembly - targets LLMOps-savvy teams
No-Code Capabilities: Agent Builder interface and pipeline configuration UI reduce coding requirements, but platform still assumes technical knowledge of RAG concepts and architectures
Target Market: Large enterprises with data engineering teams building sophisticated AI agents, organizations requiring agentic architecture with multi-step reasoning, and teams wanting deep customization without building RAG from scratch
RAG Technology Differentiation: Graph-optimized retrieval for interlinked documents, hybrid retrieval (semantic + lexical), threshold tuning for precision/recall balance, and agentic task decomposition via DAG execution
Graph Capabilities
Deployment Flexibility: Bring-your-own-infrastructure model with MongoDB partnership - deploy on your cloud/VPC with full data sovereignty and infrastructure control
Enterprise Readiness: Enterprise-grade security and scalability, audit trails for every interaction, data sovereignty options, and custom enterprise contracts with usage-based pricing
Enterprise Security
Use Case Fit: Best for enterprises building sophisticated AI agents requiring multi-step reasoning, organizations needing to blend structured APIs/databases with unstructured documents seamlessly, and teams with ML expertise wanting deep RAG customization
NOT Suitable For: Non-technical teams seeking turnkey chatbots, organizations without existing infrastructure, small businesses needing simple Q&A bots, or teams wanting pre-built UI widgets
Competitive Positioning: Competes with Deepset Cloud, LangChain/LangSmith, and custom RAG builds - differentiates through agentic architecture, no-code pipeline builder, and MongoDB partnership for enterprise scalability
Platform Type: TRUE RAG-AS-A-SERVICE API - Lightweight MIT-licensed open-source RAG backend built on Supabase with self-hosting capability and minimal API surface
Core Mission: Provide transparent, open-source alternative to proprietary RAG services (Carbon.ai shutdown response) with full cost control and no vendor lock-in
Target Market: Developers building custom RAG applications on budget, startups minimizing costs with self-hosting, organizations requiring data sovereignty with Supabase infrastructure
RAG Implementation: Standard RAG architecture with document chunking, OpenAI embeddings, Postgres pgvector semantic search—focused on simplicity over advanced techniques
API-First Design: Pure REST API for retrieval and generation without GUI, widgets, or conversational features—intentionally minimal abstraction for developer control
Self-Hosting Advantage: MIT license enables complete on-premises deployment keeping all data on your servers—ideal for GDPR, HIPAA, data residency compliance
Managed Service Option: Cloud-hosted plans (Free: 100 calls/month, Basic: $190/year for 750 calls/month, Enterprise: $1,490/year for 5K calls/month) eliminate infrastructure management
Pricing Model: Free self-hosting (MIT license) or extremely affordable hosted plans—40-90% cheaper than commercial RAG platforms with no per-document charges or user seat fees
Data Sources: File uploads (PDF, Markdown, TXT) via REST API or raw text ingestion—NO pre-built Google Drive, Notion, or cloud storage connectors (manual scripting required)
Model Flexibility: Model-agnostic with GPT-3.5 default, GPT-4, or self-hosted LLM support—users connect own OpenAI API keys without Supavec markup on AI costs
Security Foundation: Supabase Row-Level Security (RLS) for multi-tenant data isolation, HTTPS encryption, AES-256 at-rest encryption—self-hosting enables GDPR/HIPAA compliance
Support Model: Community GitHub/Discord support for free tier, email support for paid plans—no dedicated CSMs, SLAs, or enterprise account management
Open-Source Ecosystem: Transparent code on GitHub welcoming PRs, forks, and community contributions—no proprietary components or vendor lock-in
LIMITATION - Developer-Only Platform: Requires coding skills for setup, integration, and maintenance—non-technical teams cannot use without developer support
LIMITATION - Basic RAG Features: Standard retrieval without hybrid search, reranking, knowledge graphs, multi-query fusion, or hallucination detection—advanced features require custom development
LIMITATION - No Turnkey Features: No GUI dashboard, conversation management, lead capture, analytics, or multi-channel integrations—pure RAG API requiring application layer development
Comparison Validity: Architectural comparison to full-featured chatbot platforms like CustomGPT.ai requires context—Supavec is lightweight RAG backend API vs complete no-code chatbot builder
Use Case Fit: Perfect for developers wanting lightweight RAG backend without heavy frameworks, startups minimizing costs with Supabase self-hosting, teams building custom chatbots needing simple REST API for retrieval without paying for unused dashboard features
Core Architecture: Serverless RAG infrastructure with automatic embedding generation, vector search optimization, and LLM orchestration fully managed behind API endpoints
API-First Design: Comprehensive REST API with well-documented endpoints for creating agents, managing projects, ingesting data (1,400+ formats), and querying chat
API Documentation
Developer Experience: Open-source Python SDK (customgpt-client), Postman collections, OpenAI API endpoint compatibility, and extensive cookbooks for rapid integration
No-Code Alternative: Wizard-style web dashboard enables non-developers to upload content, brand widgets, and deploy chatbots without touching code
Hybrid Target Market: Serves both developer teams wanting robust APIs AND business users seeking no-code RAG deployment - unique positioning vs pure API platforms (Cohere) or pure no-code tools (Jotform)
RAG Technology Leadership: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, proprietary anti-hallucination mechanisms, and citation-backed responses
Benchmark Details
Deployment Flexibility: Cloud-hosted SaaS with auto-scaling, API integrations, embedded chat widgets, ChatGPT Plugin support, and hosted MCP Server for Claude/Cursor/ChatGPT
Enterprise Readiness: SOC 2 Type II + GDPR compliance, full white-labeling, domain allowlisting, RBAC with 2FA/SSO, and flat-rate pricing without per-query charges
Use Case Fit: Ideal for organizations needing both rapid no-code deployment AND robust API capabilities, teams handling diverse content types (1,400+ formats, multimedia transcription), and businesses requiring production-ready RAG without building ML infrastructure from scratch
Competitive Positioning: Bridges the gap between developer-first platforms (Cohere, Deepset) requiring heavy coding and no-code chatbot builders (Jotform, Kommunicate) lacking API depth - offers best of both worlds
After analyzing features, pricing, performance, and user feedback, both Dataworkz and Supavec are capable platforms that serve different market segments and use cases effectively.
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
When to Choose Supavec
You value 100% open source with no vendor lock-in
Complete control over data and infrastructure
Strong privacy with Supabase RLS integration
Best For: 100% open source with no vendor lock-in
Migration & Switching Considerations
Switching between Dataworkz and Supavec 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
Dataworkz starts at custom pricing, while Supavec 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
Start with a free trial - Both platforms offer trial periods to test with your actual data
Define success metrics - Response accuracy, latency, user satisfaction, cost per query
Test with real use cases - Don't rely on generic demos; use your production data
Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
Check vendor stability - Review roadmap transparency, update frequency, and support quality
For most organizations, the decision between Dataworkz and Supavec 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: December 11, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
The most accurate RAG-as-a-Service API. Deliver production-ready reliable RAG applications faster. Benchmarked #1 in accuracy and hallucinations for fully managed RAG-as-a-Service API.
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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