In this comprehensive guide, we compare Chatling and Deepset 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 Chatling and Deepset, 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 Chatling if: you value broadest ai model selection (32 models) among no-code platforms - includes gpt-5, claude 4.5, gemini 2.5 with per-block flexibility
Choose Deepset if: you value mature open-source framework (since 2020)
About Chatling
Chatling is no-code ai chatbot platform with 32-model llm selection. No-code AI chatbot platform with 32-model LLM selection and SMB-focused pricing starting at $25/month. Developed by Envision Labs Inc. (Ontario, Canada), Chatling balances visual builder simplicity with REST API v2 access and native WhatsApp integration. 4.8/5 G2 rating (53-63 reviews). Critical gaps: NO SOC 2/HIPAA certifications, NO native human handoff, NO official SDKs, NO source citations. Founded in Year not disclosed, headquartered in Ontario, Canada, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
85/100
Starting Price
$25/mo
About Deepset
Deepset is open-source framework and enterprise platform for llm orchestration. Deepset is the creator of Haystack, the leading open-source framework for building production-ready LLM applications, and offers an enterprise AI platform for developing and deploying custom AI agents and applications. Founded in 2018, headquartered in Berlin, Germany, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
83/100
Starting Price
Custom
Key Differences at a Glance
In terms of user ratings, both platforms score similarly in overall satisfaction. From a cost perspective, Deepset offers more competitive entry pricing. The platforms also differ in their primary focus: Chatbot Platform versus AI Development 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
Chatling
Deepset
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
File Formats: PDF, DOCX, plain text ONLY
CRITICAL LIMITATION: NO CSV, Excel, or structured data format support
Website Crawler: Up to 1,000 pages per domain with automatic content extraction
Sitemap Ingestion: Required for sites larger than 1,000 pages
Help Desk Integration: Zendesk and Zoho for importing help articles
Manual Upload: Files, text snippets, FAQs via dashboard interface
NO Cloud Storage: Google Drive, Dropbox, Notion, OneDrive require manual downloads before upload - significant workflow friction
NO YouTube Transcripts: Video content ingestion not supported
Automatic Syncing: Daily, weekly, or monthly schedules on paid plans - NO real-time continuous sync
API Resync: /resync endpoint for programmatic knowledge base updates
Gives developers a flexible framework to wire up connectors and process nearly any file type or data source with libraries like Unstructured.
Lets you push content into vector stores such as OpenSearch, Pinecone, Weaviate, or Snowflake—pick the backend that fits best. Learn more
Setup is hands-on, but the payoff is deep, domain-specific customization of your ingestion pipelines.
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.
Integrations & Channels
WhatsApp Business API: Native robust integration with full chatbot functionality, media sharing, automated responses
Website Embedding: Floating chat bubble (bottom-left/right), inline iframe, full-page deployment with custom domain support
Zapier Integration: 7,000+ apps with triggers (new contacts/conversations) and actions (send messages)
HTTP Request Actions: Custom API calls within visual builder for order lookups, CRM updates, external automations
CRITICAL GAPS: NO native Slack, Microsoft Teams, or Telegram integrations - significant B2B messaging gap
NO Mobile SDKs: App integration requires webview embedding
Custom Domains: Branded chatbot URLs available
Domain Whitelisting: Embedding control for security
API-first approach—drop the RAG system into your own app through REST endpoints or the Haystack SDK.
Shareable pipeline prototypes are great for demos, but production channels (Slack bots, web chat, etc.) need a bit of custom code. See prototype feature
Embeds easily—a lightweight script or iframe drops the chat widget into any website or mobile app.
Offers ready-made hooks for Slack, Zendesk, Confluence, YouTube, Sharepoint, 100+ more.
Explore API Integrations
Connects with 5,000+ apps via Zapier and webhooks to automate your workflows.
Supports secure deployments with domain allowlisting and a ChatGPT Plugin for private use cases.
Hosted CustomGPT.ai offers hosted MCP Server with support for Claude Web, Claude Desktop, Cursor, ChatGPT, Windsurf, Trae, etc.
Read more here.
AI Intents: Train on example phrases for intent recognition without exact keyword matching
Visual Flow Builder: No-code interface with drag-and-drop conversation design
HTTP Request Blocks: Real-time API integrations within chatbot flows (e.g., order confirmations, CRM lookups)
Lead Capture: Built-in system variables for name, email, phone collection with embedded forms
Multi-language Detection: 85+ languages with automatic browser-based preference detection
Satisfaction Surveys: Helpful/unhelpful tracking at conversation end with analytics integration
CRITICAL LIMITATION: NO native human handoff - fallback collects contact info for follow-up vs live agent transfer
Third-Party Escalation: Requires Zapier integration to Zendesk, Freshdesk, or similar platforms - adds complexity and latency
AI Agents (Beta): Task-oriented assistants with intent detection, decision-making, API execution beyond simple chatbots
AI Agents with Haystack: Build LLM-powered autonomous agents that can reason, reflect, and act using tools, data, and critical introspection into their own decision-making processes
Building Agents
Spectrum Approach: Combines structured workflows with autonomous capabilities - AI systems exist on a spectrum between linearity and autonomy based on decision-making capability needs
Agentic Spectrum
Planning Mechanisms: Agents break tasks into steps using chain-of-thought or tree-of-thought planning, enabling complex multi-step reasoning and execution
Dynamic Routing: LLMs serve as "brains" of decision systems, using reasoning capabilities to evaluate and choose among multiple tools, courses of action, databases, and resources based on context and goals
Reflection & Self-Correction: Agents analyze intermediate results through reflection mechanisms, improving accuracy and adapting strategies based on outcomes
Tool Integration: Modular pipeline design allows agents to use retriever, reader, reranker components, external API calls, and custom tools for richer autonomous behavior
Agentic RAG Enhancement: Build agentic RAG pipelines in Deepset Studio that combine graphs, agentic properties, multimodal capabilities, and innovations to significantly reduce inaccurate or misleading information
Agentic RAG Guide
Custom Workflows: Create anything from multi-hop retrieval to custom logic to bespoke prompts - modular components enable building specialized agents for domain-specific autonomous workflows
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
Free Tier Access: 8 models available without payment (GPT-4.1, GPT-4o, GPT-4o Mini, Claude 4 Sonnet, etc.)
Hybrid Deployment Capability: Per-block model selection enables using GPT-4o for complex queries, GPT-4o Mini for simple FAQs within same chatbot
Cost Optimization: Model flexibility allows balancing quality vs credit consumption per conversation stage
Temperature & Token Control: Exposed at both global and per-block levels for fine-tuned model behavior
Competitive Advantage: Exceeds CustomGPT, Drift, Yellow.ai, Lindy.ai in sheer model variety and flexibility
Manual Selection Required: NO automatic routing based on query complexity - users must configure model per use case
Credit System Integration: Different models consume different credit amounts - documented per model for budgeting
N/A
N/A
Whats App Native Integration ( Differentiator)
WhatsApp Business API: Native robust integration vs third-party workarounds required by many competitors
Full Chatbot Functionality: All chatbot features work on WhatsApp including AI responses, knowledge base queries, lead capture
Media Sharing: Images, documents, voice messages supported in WhatsApp conversations
Automated Responses: 24/7 WhatsApp availability with AI-powered replies
Consumer-Facing Strength: Strong for e-commerce, SMBs, global markets where WhatsApp dominates customer communication
Competitive Gap: Progress, CustomGPT, many RAG platforms lack native WhatsApp - Chatling advantage for consumer use cases
B2B Messaging Gap: WhatsApp strength doesn't offset missing Slack/Teams integrations for enterprise internal use
N/A
N/A
Multi- Lingual Support
85+ Languages Supported: Broad coverage for global deployments
Automatic Browser-Based Detection: Chatbot detects user language preference from browser settings and responds accordingly
NO Manual Configuration Required: Language switching happens automatically without admin setup
Interface Language: Configurable for chatbot UI elements (buttons, prompts, system messages)
Multi-Language Model Support: All 32 AI models support multilingual conversations
Knowledge Base Processing: Supports multi-language content ingestion and retrieval
Global Customer Base: Valuable for international businesses serving diverse markets without language barriers
N/A
N/A
R A G-as-a- Service Assessment
Platform Type: NO-CODE CHATBOT PLATFORM with RAG capabilities - NOT pure RAG-as-a-Service like CustomGPT or Progress
RAG Implementation: Knowledge base grounding embedded within visual chatbot builder vs API-first RAG backend
Developer Access: REST API v2 provides programmatic knowledge base queries (/ai/kb/chat endpoint) but NO official SDKs
Transparency Limitation: NO source citations displayed to end users - responses don't show which documents informed answers
NO Confidence Scoring: Hallucination detection mechanisms not documented - only temperature control
Target Market: SMBs and non-technical teams prioritizing rapid chatbot deployment vs developers needing deep RAG customization
Comparison Validity: Architectural comparison to CustomGPT.ai is partially valid - both offer RAG but Chatling emphasizes no-code chatbot vs developer-first RAG API
Use Case Fit: Organizations prioritizing customer-facing chatbots with simple knowledge retrieval over complex RAG pipelines, embeddings control, or advanced retrieval strategies
Platform Type: HYBRID RAG FRAMEWORK + CLOUD SERVICE - open-source Haystack foundation with enterprise Deepset Cloud offering for heavily customized, domain-specific RAG solutions
Core Architecture: Modular pipeline architecture with retriever + reader + optional reranker components, full control over embedding models, vector databases (OpenSearch, Pinecone, Weaviate, Snowflake), and chunking strategies
Agentic Capabilities: Build autonomous AI agents with planning, routing, reflection mechanisms using Haystack framework - supports agentic RAG pipelines with graphs and multimodal capabilities
Agent Development
Developer Experience: Comprehensive REST API, open-source Haystack SDK, Deepset Studio visual pipeline editor with YAML export for version control - targets ML engineers and development teams
Studio Overview
No-Code Capabilities: Deepset Studio offers drag-and-drop visual editor for pipeline building, but still aimed at developers and ML engineers - not accessible to non-technical users
Target Market: ML engineers and development teams needing deep RAG customization, enterprises requiring domain-specific solutions with modular pipeline architecture, organizations wanting future portability with open-source foundation
RAG Technology Leadership: Advanced RAG with multi-step retrieval, hybrid search (semantic + keyword), custom rerankers for maximum accuracy, model-agnostic support (GPT-4, Llama 2, Claude, Cohere, 80+ providers), and benchmark-proven performance on MTEB
Benchmark Insights
Deployment Flexibility: Free Deepset Studio for development, usage-based Enterprise plans, cloud/VPC/on-prem deployment options, and SOC 2/ISO 27001/GDPR/HIPAA compliance with flexible data residency
Enterprise Readiness: SOC 2 Type II, ISO 27001, GDPR, HIPAA compliance, cloud/hybrid/on-prem deployment, no model training on customer data, and comprehensive audit trails
Use Case Fit: Perfect for teams needing heavily customized domain-specific RAG with multi-hop retrieval and custom rerankers, organizations requiring modular pipeline architecture for complex workflows, ML engineers wanting developer-friendly APIs with future portability
Open-Source Advantage: Haystack framework (14,000+ GitHub stars) free forever with full portability - only pay for managed Deepset Cloud services if needed, avoiding vendor lock-in
NOT Suitable For: Non-technical teams seeking turnkey chatbots, business users wanting no-code deployment, organizations needing pre-built chat widgets or Slack/WhatsApp integrations
Competitive Positioning: Competes with LangChain/LangSmith, Contextual.ai, Dataworkz - differentiates through open-source Haystack foundation, model-agnostic flexibility, visual pipeline editor, and wide vector DB ecosystem
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
Competitive Positioning
Market Position: SMB-focused no-code chatbot platform with strongest appeal to non-technical teams and budget-conscious startups
32-Model Differentiator: Broadest LLM selection among no-code platforms - exceeds competitors in model flexibility
Free Tier Generosity: 100 AI credits, 2 chatbots, 8 models without credit card - strongest trial experience for evaluation
WhatsApp Strength: Native integration vs third-party workarounds - competitive advantage for consumer-facing businesses
G2 Validation: 4.8/5 rating from 53-63 reviews with reliability praised ("chatbots have never gone down")
vs. CustomGPT: Chatling offers no-code simplicity + WhatsApp vs CustomGPT developer-first RAG with deeper API/SDK access
vs. Progress: Chatling $25/month + visual builder vs Progress $700/month + REMi quality monitoring + enterprise compliance
vs. Drift: Chatling customer support automation vs Drift B2B sales engagement - different use case focus
vs. Lindy.ai: Chatling has REST API v2 vs Lindy NO public API - developer accessibility advantage
Enterprise Gaps: NO SOC 2/HIPAA/ISO 27001, NO SSO, NO human handoff - disqualifies for regulated industries and large enterprises
B2B Messaging Gaps: NO native Slack/Teams/Telegram - limits enterprise internal use cases vs omnichannel competitors
Developer Limitations: NO official SDKs, NO source citations, NO confidence scoring - gaps vs developer-focused RAG platforms
Market Presence: Absent from Product Hunt, AppSumo vs competitors - limited growth marketing exposure
Market position: Developer-first RAG framework (Haystack) with enterprise cloud offering (Deepset Cloud) for heavily customized, domain-specific RAG solutions
Target customers: ML engineers and development teams needing deep RAG customization, enterprises requiring domain-specific solutions with modular pipeline architecture, and organizations wanting future portability with open-source foundation
Key competitors: LangChain/LangSmith, Contextual.ai, Dataworkz, Vectara.ai, and custom implementations using Pinecone/Weaviate
Competitive advantages: Open-source Haystack framework for full portability, model-agnostic with easy model switching via Connections UI, Deepset Studio visual pipeline editor with YAML export for version control, modular components (retriever, reader, reranker) for maximum flexibility, wide ecosystem of vector DB integrations (OpenSearch, Pinecone, Weaviate, Snowflake), and SOC 2/ISO 27001/GDPR/HIPAA compliance with cloud/VPC/on-prem deployment
Pricing advantage: Free Deepset Studio for development, then usage-based Enterprise plans; competitive for teams wanting deep customization without vendor lock-in; best value comes from open-source foundation enabling future migration if needed
Use case fit: Perfect for teams needing heavily customized, domain-specific RAG with multi-hop retrieval and custom rerankers, organizations requiring modular pipeline architecture for complex workflows, and ML engineers wanting developer-friendly APIs with future portability through open-source Haystack foundation
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
Deployment & Infrastructure
Cloud-Only SaaS: NO on-premise or hybrid deployment options - cloud-only hosted on DigitalOcean
Data Center: Amsterdam (DigitalOcean) for GDPR compliance with EU data residency
Website Embedding: Three modes - floating chat bubble (customizable position), inline iframe for page sections, full-page deployment
Custom Domain Support: Branded chatbot URLs available for white-labeled deployments
Domain Whitelisting: Security control limiting widget embedding to authorized domains
Mobile Deployment: NO native SDKs - app integration requires webview embedding
NO Multi-Region: Single data center (Amsterdam) - no US, Asia-Pacific, or other regional options documented
NO On-Premise: Cannot deploy on private infrastructure or air-gapped environments
N/A
N/A
Customer Feedback & Case Studies
G2 Rating: 4.8/5 from 53-63 reviews with strong reliability scores
Trustpilot Rating: 4.3/5 from 8 reviews
Support Quality (G2): 9.2/10 despite email-only channel and response time concerns
Setup Time Praise: "5-minute setup" consistently highlighted by users as genuine rapid deployment
Reliability Testimonial: "Chatbots have never gone down" - uptime performance praised
Support Deflection: One user reported 45% of support questions resolved, reducing email inquiries from 1,500+ monthly
Large-Scale Deployment: User uploaded 4,000+ website URLs with "reliable answers in real time"
Fine-Tuning from Traffic: "Game changer" - ability to improve from live conversation data
Recurring Criticism: Single flow architecture unwieldy for complex bots, NO import/export flows, NO screen reader accessibility, email support can be slow
NO Named Enterprise Customers: Public case studies limited to G2/Trustpilot testimonials vs named Fortune 500 deployments
N/A
N/A
A I Models
Free Tier (8 Models): GPT-4.1, GPT-4o, GPT-4o Mini, GPT-4, Claude 4 Sonnet, Claude 3.5 Haiku without payment
Paid Tiers (32 Total): GPT-5, GPT-5 Mini, GPT-5 Nano, GPT-4.5, o4 Mini, o3 Mini, Claude 4.5 Sonnet, Claude 4.5 Haiku, Claude 4 Opus, Claude 3.7 Sonnet, Gemini 2.5 Flash, Gemini 2.5 Pro, Gemini 2.0 Flash - broadest selection among no-code platforms
Model Flexibility: Configure globally per chatbot OR per individual AI response block for hybrid deployments optimizing cost-quality balance
Temperature & Max Tokens: Exposed at both global and per-block levels for fine-tuned control over creativity and verbosity
Manual Selection Only: No query complexity-based automatic model routing - users manually configure model per use case
Credit Consumption: 1 credit per AI response on GPT-4o, consumption varies by model with monthly reset (no carryover)
Competitive Advantage: 32-model roster exceeds most no-code platforms (Botsonic, Chatbase, SiteGPT) in LLM flexibility
Model-agnostic architecture: Supports GPT-4, GPT-3.5, Claude (Anthropic), Llama 2, Cohere, and 80+ model providers through unified interface
Easy model switching: Change models via Connections UI with just a few clicks without code changes
Multiple LLMs per pipeline: Use different models for different pipeline components (retrieval vs generation)
Custom model fine-tuning: Fine-tune on proprietary data for domain-specific terminology and accuracy
Baseline models available: Pre-configured with common models for quick prototyping
Primary models: GPT-4, GPT-3.5 Turbo from OpenAI, and Anthropic's Claude 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
Knowledge Base Training: Upload documents (PDF, DOCX, TXT, CSV) and website URLs to train chatbot on custom content
Retrieval-Augmented Responses: Grounds answers in uploaded knowledge base for factual accuracy and reduced hallucinations compared to pure LLM responses
Auto-Retraining: Daily, weekly, or monthly schedules on paid plans - NO real-time continuous sync with cloud storage
Simple RAG Workflow: No advanced features like semantic chunking controls, confidence scoring, or source citations - basic upload-and-query model
Manual Updates: Knowledge base updates require manual re-upload or retraining via dashboard or API /resync endpoint
NO Source Citations: Responses don't show which specific documents or URLs informed each answer - reduces transparency vs competitors (CustomGPT, Progress)
NO Confidence Scoring: No hallucination detection mechanisms documented - only indirect temperature control
Best for Simple Bots: Works well for small to medium-sized knowledge bases (500K-90M characters) - not designed for massive enterprise deployments
Performance Claims: 45% support question resolution, 4,000+ URLs processed with "reliable answers in real time" per user testimonials
Advanced RAG architecture: Multi-step retrieval, hybrid search (semantic + keyword), and custom rerankers for maximum accuracy
Multi-hop retrieval: Chain multiple retrieval steps for complex queries requiring deep context
Vector database flexibility: OpenSearch, Pinecone, Weaviate, Snowflake, Qdrant, and more - choose your preferred backend
Benchmark-proven performance: Published performance metrics on MTEB and domain-specific benchmarks
Source attribution: Full citation tracking with document references and confidence scores
Haystack framework: Open-source foundation enables complete RAG customization and future portability
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
Website Chatbots: Quick embedding on websites for customer support and lead generation with simple JavaScript widget
WhatsApp Business: Native WhatsApp integration for conversational commerce and customer engagement on mobile-first platforms
Customer Support Automation: FAQ automation and basic support ticket routing reducing email inquiries by 45% (user testimonial: 1,500+ monthly inquiries)
Lead Generation: Built-in lead capture with system variables (name, email, phone) and qualification flows for sales pipeline building
Multi-Language Support: Automatic browser-based language detection across 85+ languages for global SMB audiences
Zapier Workflows: Connect to 7,000+ apps through Zapier for sales/marketing automation without coding
HTTP Request Actions: Custom API calls within visual builder for order lookups, CRM updates, external automations
E-commerce Support: Product information, order status, customer inquiry automation for online stores
SMB-Focused: Designed for small to mid-size businesses with straightforward chatbot needs and limited technical resources (5-minute setup time)
Research and analysis: Multi-hop retrieval for complex research questions across large document corpora
Technical documentation: Developer-focused RAG for code documentation, API references, and technical guides
Compliance and legal: HIPAA/GDPR-compliant RAG systems for regulated industries requiring on-prem deployment
Custom AI agents: Build specialized agents with external API calls, tool use, and multi-step reasoning
Enterprise search: Large-scale search across millions of documents with hybrid retrieval and reranking
Future-proof AI: Migrate between LLM providers, vector databases, and hosting options without vendor lock-in
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)
Unlimited Non-AI Chats: All tiers include unlimited non-AI conversations - only AI-powered responses consume credits
14-Day Money-Back Guarantee: Applies to initial purchases for risk-free testing
Add-Ons Available: Extra credits, characters, seats, and chatbots purchasable separately when plan limits exceeded
Credit System: Monthly reset with no carryover - usage planning required to avoid service interruption at 100% consumption
Competitive SMB Positioning: $25/month entry vs $700/month (Progress), $30K+/year (Drift, Yellow.ai) - 10-100x cheaper for small businesses
Transparent Pricing: No hidden fees, confusing tier jumps, or expensive add-on stacking costs
Deepset Studio (Free): Development environment with unlimited files and core features for prototyping
Enterprise pricing: Custom usage-based pricing based on queries, documents indexed, and compute resources
Deployment options pricing: Cloud (managed SaaS), hybrid, or on-premises with separate pricing tiers
No per-seat charges: Usage-based model scales with actual platform usage, not team size
Professional services: Optional consulting, integration support, and custom pipeline development available
Scaling flexibility: Enterprise plans handle huge corpora (millions of documents) and heavy traffic loads
Open-source advantage: Haystack framework free forever - only pay for managed cloud services if needed
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
Email-Only Support: support@chatling.ai - NO live chat, phone support, or published SLAs (email support can be slow per reviews)
G2 Support Rating: 9.2/10 quality despite email-only channel and response time concerns
Comprehensive Documentation: docs.chatling.ai with getting started guides, API reference, integration tutorials, flow builder instructions
Video Tutorials: Supplement written documentation for visual learners
Action Tutorial Library: Practical HTTP request examples for common integrations (e.g., "Fetch and Email Order Confirmation")
Trust Center: trust.chatling.ai for security documentation and compliance details
REST API v2: https://api.chatling.ai/v2/ with Bearer token authentication and 300 requests/minute rate limit
MISSING ECOSYSTEM: NO community forum, Discord, Slack workspace, or public status page for peer support
NO Public Roadmap: Feature development transparency limited compared to competitors
Enterprise Support: Requires contacting sales - no dedicated support tiers publicly documented
Haystack community: Active Discord server and GitHub community (14,000+ stars) with responsive maintainers
Enterprise support tiers: Email, Slack Connect channels, and dedicated support engineers for paid customers
Comprehensive documentation: docs.cloud.deepset.ai with tutorials, API references, and integration guides
Video tutorials: YouTube channel with pipeline building guides and best practices
GitHub examples: Open-source example projects and starter templates for common use cases
Integration ecosystem: Wide community of vector DB providers, model vendors, and tool developers
Professional services: Custom development, architecture consulting, and hands-on implementation support available
Documentation hub: Rich docs, tutorials, cookbooks, FAQs, API references for rapid onboarding
Developer Docs
Email and in-app support: Quick support via email and in-app chat for all users
Premium support: Premium and Enterprise plans include dedicated account managers and faster SLAs
Code samples: Cookbooks, step-by-step guides, and examples for every skill level
API Documentation
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
Single Flow Management: Larger bots get unwieldy because everything lives inside single flow - no folder organization like ManyChat for complex conversation trees
NO Live Chat Support: Doesn't include live chat feature - blended human-AI support approach unavailable without Zapier workarounds
Separate Bots Per Channel: Need to build separate chatbot for website vs WhatsApp - no unified multi-channel bot creation
Limited Advanced Features: Once you need fallback behavior, confidence scoring, or content indexing control, limitations appear
Barebones Analytics: Analytics pretty barebones compared to enterprise platforms with detailed conversation intelligence and custom report builders
Knowledge Base Management Challenges: For e-shop or site with lots of pages, nightmare to sort which pages to add - no Excel import for bulk management
Data Quality Dependency: If data isn't clean, bot might pull irrelevant answers - heavily dependent on training data quality and curation
NO Flow Import/Export: Cannot import or export flows, no option to copy or duplicate full group of blocks for version control
Screen Reader Accessibility: Does not support accessibility for blind users using screen readers - inclusivity limitation cited in reviews
Setup Time Investment: Configuring chatbot tone takes manual effort, assembling strong knowledge base not plug-and-play despite 5-minute claims
Learning Curve: Takes while to learn how to use builder and tools despite drag-and-drop interface and visual design
Integration Gaps: Heavy reliance on Zapier might limit functionality if service experiences downtime - not all third-party platforms supported natively
Interface Overwhelm: Drag-and-drop can be overwhelming for new users unfamiliar with chatbot design principles and flow logic
Best for Small-to-Medium Bots: Works best for small to medium-sized bots rather than massive enterprise-level projects with complex requirements
B2B Messaging Gaps: NO native Slack, Microsoft Teams, or Telegram integrations - limits enterprise internal use cases
NO Official SDKs: Must build own HTTP clients or rely on community implementations - no official JavaScript or Python SDKs
Enterprise Compliance Gaps: NO SOC 2, HIPAA, ISO 27001 certifications disqualifies platform for regulated industries (healthcare, finance, government)
Steeper learning curve: Developer-first platform requires ML/engineering skills - not ideal for non-technical users
Custom UI required: No drag-and-drop chat widget - must build production interfaces from scratch
Hands-on setup: More initial configuration effort compared to plug-and-play SaaS platforms
Deepset Studio limitations: Visual editor still aimed at technical users - requires understanding of RAG concepts
Production readiness: Moving from Studio prototype to production deployment requires additional DevOps work
Enterprise costs: Usage-based pricing can become expensive at high query volumes without careful optimization
Best for technical teams: Maximum value requires ML engineers and developers - not suited for business users seeking no-code solutions
Integration effort: Native integrations like Slack bots require custom code vs turnkey options from competitors
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-4, GPT-3.5) and Anthropic (Claude) - 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
Additional Considerations
Simplicity as Strength: Platform strongest feature is simplicity designed so anyone regardless of technical background can build powerful GPT-enabled chatbot quickly
No-Code Accessibility: Drag-and-drop interface makes creating AI chatbots accessible to non-technical users with minimal learning curve
Multilingual Versatility: Supports over 85 languages ensuring chatbot can communicate with diverse linguistic backgrounds automatically
Integration Flexibility: Seamless integration with HubSpot, Zendesk, Zoho, Google Sheets, Cal.com, and Zapier for workflow automation
Cost-Effective Free Plan: Unique free plan doesn't cap conversations - if you don't need AI-powered replies, stay free forever making it most cost-effective for SMBs
Latest AI Models: Powered by latest large language models including GPT, Gemini, and Claude ensuring cutting-edge performance
WhatsApp Native Integration: Works seamlessly on websites and WhatsApp providing mobile-first customer engagement capabilities
Proven Reliability: G2 reviews praise "chatbots have never gone down" with 4.8/5 rating from 53-63 reviews demonstrating strong uptime
Support Deflection Success: User reported 45% of support questions resolved reducing email inquiries from 1,500+ monthly for efficiency gains
Security & Privacy: Industry-standard security with data encryption in transit and at rest, GDPR compliant with regular security audits
Training Flexibility: Upload documents, add websites, connect data sources to train AI chatbot automatically on custom content
Trade-Off: Simplicity vs Advanced Features: Exceptional usability and ease comes at cost of advanced features like custom flows, live chat, enterprise compliance
Best Fit: Small to mid-size businesses prioritizing rapid deployment, simplicity, and cost-effectiveness over enterprise-grade features and compliance
Perfect for teams that need heavily customized, domain-specific RAG solutions.
Full control and future portability—but expect a steeper learning curve and more dev effort. More details
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.
Core Chatbot Features
AI-Powered Responses: Accurate, round-the-clock customer support trained on business data from URLs, FAQs, knowledge bases, documents, text inputs
No-Code Visual Builder: Intuitive drag-and-drop builder requiring no coding expertise - heart of Chatling 2.0 update and game-changer for non-technical users
Multi-Turn Conversations: Maintains conversation history and context for natural, flowing dialogues rather than treating each query independently
Multi-Language Support: 85+ languages with automatic browser-based language detection - bot responds in user's detected language without manual configuration
24/7 Availability: Operates around the clock ensuring customers receive feedback when needed without human intervention
Lead Capture Forms: Built-in form builder for embedding within conversation flows to collect customer information seamlessly
Analytics & Insights: Tracks customer conversations to identify gaps in support resources with visual metrics, heatmaps, and trend graphs
Customization Options: Tailor every aspect from chat interface to conversational logic matching brand tone and style with color picker, icon uploader, settings toggles
Integration Capabilities: Easily integrates with websites (WordPress, Squarespace, Shopify) and platforms like HubSpot, Zendesk, Zoho, Zapier
Multiple Chatbots: Create multiple chatbots per account (1 on Free, 2 on Pro, 5 on Pro, 35 on Ultimate) for different use cases
Conversation Management: Real-time monitoring, message history viewing, popular question identification for knowledge base optimization
45% Resolution Rate: User testimonial reports 45% of support questions successfully resolved with email reduction from 1,500+ monthly inquiries
Builds RAG agents as modular pipelines—retriever + reader, plus optional rerankers or multi-step logic.
Multi-turn chat? Source attributions? Fine-grained retrieval tweaks? All possible with the right config. Pipeline overview
Advanced users can layer in tool use and external API calls for richer agent behavior.
Reduces hallucinations by grounding replies in your data and adding source citations for transparency.
Benchmark Details
Handles multi-turn, context-aware chats with persistent history and solid conversation management.
Speaks 90+ languages, making global rollouts straightforward.
Includes extras like lead capture (email collection) and smooth handoff to a human when needed.
After analyzing features, pricing, performance, and user feedback, both Chatling and Deepset are capable platforms that serve different market segments and use cases effectively.
When to Choose Chatling
You value broadest ai model selection (32 models) among no-code platforms - includes gpt-5, claude 4.5, gemini 2.5 with per-block flexibility
Generous free tier: 100 AI credits, 2 chatbots, 8 models, 500K characters - meaningful testing capacity without credit card
Unlimited non-AI chats across all tiers reduces usage anxiety and cost unpredictability
Best For: Broadest AI model selection (32 models) among no-code platforms - includes GPT-5, Claude 4.5, Gemini 2.5 with per-block flexibility
When to Choose Deepset
You value mature open-source framework (since 2020)
Production-ready from day one
Highly modular and customizable
Best For: Mature open-source framework (since 2020)
Migration & Switching Considerations
Switching between Chatling and Deepset 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
Chatling starts at $25/month, while Deepset 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 Chatling and Deepset 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 6, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
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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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