Data Ingestion & Knowledge Sources
Document formats – PDF, DOCX, PPTX, CSV, TXT, HTML; 5MB free tier limit
Website crawling – Hundreds of thousands of pages indexed under 5 minutes
Google Drive – Native integration with real-time sync for cloud content
SQL databases – MySQL, PostgreSQL, Oracle, SQL Server, AWS/Azure/Google Cloud SQL
⚠️ YouTube, Dropbox, Notion, OneDrive – Zapier middleware required (no native integration)
✅ File Format Support – PDF, JSON, Markdown, Word, plain text auto-chunked and embedded. [Pinecone Learn]
✅ Automatic Processing – Chunks, embeds, stores uploads in Pinecone index for fast search.
✅ Metadata Filtering – Add tags to files for smarter retrieval results. [Metadata]
⚠️ No Native Connectors – No web crawler or Drive connector; push files via API/SDK.
✅ Enterprise Scale – Billions of embeddings; preview tier supports 10K files or 10GB per assistant.
1,400+ file formats – PDF, DOCX, Excel, PowerPoint, Markdown, HTML + auto-extraction from ZIP/RAR/7Z archives
Website crawling – Sitemap indexing with configurable depth for help docs, FAQs, and public content
Multimedia transcription – AI Vision, OCR, YouTube/Vimeo/podcast speech-to-text built-in
Cloud integrations – Google Drive, SharePoint, OneDrive, Dropbox, Notion with auto-sync
Knowledge platforms – Zendesk, Freshdesk, HubSpot, Confluence, Shopify connectors
Massive scale – 60M words (Standard) / 300M words (Premium) per bot with no performance degradation
Hybrid Retrieval Architecture ( Core Differentiator)
✅ Three-component system – Elasticsearch + Milvus vectors + XGBoost ML reranking
75.33 NDCG@10 – MTEB vs 73.16 pure vector (3% improvement)
96.50% Recall@20 – Anthropic benchmark vs 90.06% baseline
Models – snowflake-arctic-embed-m, bge-en-icl, voyage-2, OpenAI text-embedding-3-large
✅ Key finding – Open-source models match/exceed paid alternatives in benchmarks
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98.3% response accuracy – Claimed with 1.2-second average response
Source citation – Visual PDF highlighting with page-level references
⚠️ No published uptime SLA – Service reliability not documented
✅ Fast Retrieval – Pinecone vector DB delivers speed; GPT-4/Claude ensures quality answers.
✅ Benchmarked Superior – 12% more accurate vs OpenAI Assistants via optimized retrieval. [Benchmark]
Citations Reduce Hallucinations – Context plus citations tie answers to real data sources.
Evaluation API – Score accuracy against gold-standard datasets for continuous improvement.
Sub-second responses – Optimized RAG with vector search and multi-layer caching
Benchmark-proven – 13% higher accuracy, 34% faster than OpenAI Assistants API
Anti-hallucination tech – Responses grounded only in your provided content
OpenGraph citations – Rich visual cards with titles, descriptions, images
99.9% uptime – Auto-scaling infrastructure handles traffic spikes
Developer Experience ( A P I & S D Ks)
REST API + GraphQL – Bearer token auth with scored passage responses
denser-retriever – MIT-licensed Python package (261 stars, 30 forks)
Docker Compose – Full stack with Elasticsearch and Milvus containers
⚠️ Self-hosted "not production suitable" – Requires additional persistence and secrets config
Rate limits – 200 API calls/month on free tier
✅ Rich SDK Support – Python, Node.js SDKs plus clean REST API. [SDK Support]
Comprehensive Endpoints – Create/delete assistants, upload/list files, run chat/retrieval queries.
✅ OpenAI-Compatible API – Simplifies migration from OpenAI Assistants to Pinecone Assistant.
Documentation – Reference architectures and copy-paste examples for typical RAG flows.
REST API – Full-featured for agents, projects, data ingestion, chat queries
Python SDK – Open-source customgpt-client with full API coverage
Postman collections – Pre-built requests for rapid prototyping
Webhooks – Real-time event notifications for conversations and leads
OpenAI compatible – Use existing OpenAI SDK code with minimal changes
Supported LLMs – GPT-4o, GPT-4o mini, GPT-3.5, Claude (version unspecified)
API keys – Users provide OpenAI or Claude keys via environment
⚠️ No custom fine-tuning – No private model hosting documented
✅ GPT-4 & Claude 3.5 – Pick model per query; supports GPT-4o, GPT-4, Claude Sonnet. [Blog]
⚠️ Manual Model Selection – No auto-routing; explicitly choose GPT-4 or Claude each request.
Limited Options – GPT-3.5 not in preview; more LLMs coming soon on roadmap.
Standard Vector Search – No proprietary rerank layer; raw LLM handles final answer generation.
GPT-5.1 models – Latest thinking models (Optimal & Smart variants)
GPT-4 series – GPT-4, GPT-4 Turbo, GPT-4o available
Claude 4.5 – Anthropic's Opus available for Enterprise
Auto model routing – Balances cost/performance automatically
Zero API key management – All models managed behind the scenes
Website deployment – JavaScript widget (single line), iFrame, REST API
WordPress – Official plugin with page-specific targeting for no-code install
Zapier – 6,000+ apps with lead form triggers and events
⚠️ No native Slack, Teams, Discord – WhatsApp via Zapier only
⚠️ CRM via Zapier only – HubSpot, Salesforce, Zendesk not native
⚠️ Backend Service Only – No built-in chat widget or turnkey Slack/Teams integration.
Developer-Built Front-Ends – Teams craft custom UIs or integrate via code/Pipedream.
REST API Integration – Embed anywhere by hitting endpoints; no one-click Zapier connector.
✅ Full Flexibility – Drop into any environment with your own UI and logic.
Website embedding – Lightweight JS widget or iframe with customizable positioning
CMS plugins – WordPress, WIX, Webflow, Framer, SquareSpace native support
5,000+ app ecosystem – Zapier connects CRMs, marketing, e-commerce tools
MCP Server – Integrate with Claude Desktop, Cursor, ChatGPT, Windsurf
OpenAI SDK compatible – Drop-in replacement for OpenAI API endpoints
LiveChat + Slack – Native chat widgets with human handoff capabilities
Drag-and-drop builder – Theme colors, logos, button sizing, bubbles
Custom domains – Available on paid tiers for white-labeling
Welcome messages – Configure suggested questions and greetings
✅ 100% Your UI – No default interface; branding baked in by design, fully white-label.
No Pinecone Badge – Zero branding to hide; complete control over look and feel.
Domain Control – Gating and embed rules handled in code via API keys/auth.
✅ Unlimited Freedom – Pinecone ships zero CSS; style however you want.
Full white-labeling included – Colors, logos, CSS, custom domains at no extra cost
2-minute setup – No-code wizard with drag-and-drop interface
Persona customization – Control AI personality, tone, response style via pre-prompts
Visual theme editor – Real-time preview of branding changes
Domain allowlisting – Restrict embedding to approved sites only
No- Code Interface & Usability
Visual builder – Drag-and-drop theme customization without coding
Setup – Single line JavaScript; WordPress plugin for no-code
⚠️ Learning curve – Documentation fragmented across multiple sites
⚠️ ~4-person team – Impacts enterprise support capacity
⚠️ Developer-Centric – No no-code editor or widget; console for quick uploads/tests only.
Code Required – Must code front-end and call Pinecone API for branded chatbot.
No Admin UI – No role-based admin for non-tech staff; build your own if needed.
Perfect for Dev Teams – Not plug-and-play for non-coders; requires development resources.
2-minute deployment – Fastest time-to-value in the industry
Wizard interface – Step-by-step with visual previews
Drag-and-drop – Upload files, paste URLs, connect cloud storage
In-browser testing – Test before deploying to production
Zero learning curve – Productive on day one
Integrated lead capture – Configurable fields (name, email, company, role, phone)
Conversation-triggered forms – Dynamic deployment based on conversation context
Analytics dashboard – Lead quality, satisfaction scores, conversion trends
✅ 24.8% conversion rate – Claimed on homepage demonstrating effectiveness
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Multi- Language & Localization
80+ languages – Automatic language detection for global deployments
Multilingual rerankers – jinaai/jina-reranker-v2-base-multilingual support
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Conversation history – 30-360 days retention by tier
Human handoff – Triggers when complexity exceeds scope
Escalation workflows – Zendesk ticket creation for handoffs
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Observability & Monitoring
Conversation logs – Retention by tier (30-360 days)
User engagement tracking – Common queries, conversation length, drop-off points
⚠️ No A/B testing – No third-party BI integration (Tableau, PowerBI)
⚠️ No real-time alerting – No documented SLA tracking
Dashboard Metrics – Shows token usage, storage, concurrency; no built-in convo analytics. [Token Usage]
Evaluation API – Track accuracy over time against gold-standard benchmarks.
⚠️ Manual Chat Logs – Dev teams handle chat-log storage if transcripts needed.
External Integration – Easy to pipe metrics into Datadog, Splunk via API logs.
Real-time dashboard – Query volumes, token usage, response times
Customer Intelligence – User behavior patterns, popular queries, knowledge gaps
Conversation analytics – Full transcripts, resolution rates, common questions
Export capabilities – API export to BI tools and data warehouses
S Q L Database Chat ( Unique Feature)
✅ Direct SQL connectivity – Conversational BI across major databases
Supported databases – MySQL, PostgreSQL, Oracle, SQL Server, AWS/Azure/Google Cloud SQL
Natural language to SQL – Ask questions, receive database query results
AES-256 encryption – Secure connections with read-only access requirement
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Free – $0: 1 chatbot, 20 queries/month, 5MB limit
Starter – $19-29/month: 2 chatbots, 1,500 queries/month, 30-day logs
Standard – $89-119/month: 4 chatbots, 7,500 queries/month, custom domain
Business – $399-799/month: 8 chatbots, 15,000 queries/month, priority support
Enterprise – Custom: Private cloud, dedicated support, AWS Marketplace
⚠️ User feedback – "Plans quite restrictive, credit limits reached sooner"
Usage-Based Model – Free Starter, then pay for storage/tokens/assistant fee. [Pricing]
Sample Costs – ~$3/GB-month storage, $8/M input tokens, $15/M output tokens, $0.20/day per assistant.
✅ Linear Scaling – Costs scale with usage; ideal for growing applications over time.
Enterprise Tier – Higher concurrency, multi-region, volume discounts, custom SLAs.
Standard: $99/mo – 60M words, 10 bots
Premium: $449/mo – 300M words, 100 bots
Auto-scaling – Managed cloud scales with demand
Flat rates – No per-query charges
⚠️ NO SOC 2, HIPAA, ISO 27001, GDPR certifications – Not for regulated industries
Private cloud deployments – Enterprise tier for data sovereignty
AES-256 encryption – Database connections with read-only access
AWS infrastructure – Data storage and processing on AWS
✅ Data Isolation – Files encrypted and siloed; never used to train models. [Privacy]
✅ SOC 2 Type II – Compliant with strong encryption and optional dedicated VPC.
Full Content Control – Delete or replace content anytime; control what assistant remembers.
Enterprise Options – SSO, advanced roles, custom hosting for strict compliance requirements.
SOC 2 Type II + GDPR – Third-party audited compliance
Encryption – 256-bit AES at rest, SSL/TLS in transit
Access controls – RBAC, 2FA, SSO, domain allowlisting
Data isolation – Never trains on your data
✅ denser-retriever – MIT-licensed, 261 GitHub stars, full RAG transparency
Docker Compose deployment – Local experimentation with Elasticsearch and Milvus
Validate benchmarks – Test embeddings, rerankers, chunking on own data
⚠️ Self-hosted "not production suitable" – Denser recommends managed SaaS
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Founded 2023 – Silicon Valley startup, ~4 employees (bootstrapped)
✅ Founder Zhiheng Huang – Former Amazon Kendra scientist, Amazon Q lead
70+ research papers – 14,000+ citations; BLSTM-CRF 5,400+ citations
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R A G-as-a- Service Assessment
✅ TRUE RAG PLATFORM – Hybrid retrieval with open-source transparency
Data source flexibility – Good (documents, websites, Google Drive, SQL)
LLM model options – Good (GPT-4o, Claude, multiple embeddings/rerankers)
✅ Open-source transparency – Excellent (MIT-licensed core, published benchmarks)
⚠️ Compliance & certifications – Poor (no SOC 2, HIPAA, ISO 27001)
Best for – Technical teams prioritizing retrieval accuracy and validation
✅ TRUE RAG-AS-A-SERVICE – Managed backend API abstracting chunking, embedding, storage, retrieval, reranking, generation.
API-First Service – Pure backend with Python/Node SDKs; developers build custom front-ends on top.
✅ Pinecone Vector DB Foundation – Built on proven database supporting billions of embeddings at enterprise scale.
OpenAI-Compatible – Simplifies migration from OpenAI Assistants to Pinecone Assistant seamlessly.
⚠️ Key Difference – No no-code UI/widgets vs full-stack platforms (CustomGPT) with embeddable chat.
Platform type – TRUE RAG-AS-A-SERVICE with managed infrastructure
API-first – REST API, Python SDK, OpenAI compatibility, MCP Server
No-code option – 2-minute wizard deployment for non-developers
Hybrid positioning – Serves both dev teams (APIs) and business users (no-code)
Enterprise ready – SOC 2 Type II, GDPR, WCAG 2.0, flat-rate pricing
vs CustomGPT – Superior retrieval transparency, SQL chat; gaps in compliance
vs Glean – Open-source vs proprietary, lower cost; lacks permissions-aware AI
✅ Unique strengths – Hybrid retrieval benchmarks, founder pedigree, SQL chat
Target audience – Developers building AI chatbots without strict compliance
Market Position – Developer-focused RAG backend on top-ranked vector database (billions of embeddings).
Target Customers – Dev teams building custom RAG apps requiring massive scale and concurrency.
Key Competitors – OpenAI Assistants API, Weaviate, Milvus, CustomGPT, Vectara, DIY solutions.
✅ Competitive Advantages – Proven infrastructure, auto chunking/embedding, OpenAI-compatible API, GPT-4/Claude choice, SOC 2.
Best Value For – High-volume apps needing enterprise vector search without managing infrastructure.
Market position – Leading RAG platform balancing enterprise accuracy with no-code usability. Trusted by 6,000+ orgs including Adobe, MIT, Dropbox.
Key differentiators – #1 benchmarked accuracy • 1,400+ formats • Full white-labeling included • Flat-rate pricing
vs OpenAI – 10% lower hallucination, 13% higher accuracy, 34% faster
vs Botsonic/Chatbase – More file formats, source citations, no hidden costs
vs LangChain – Production-ready in 2 min vs weeks of development
✅ Hybrid retrieval – ES + Milvus vectors + XGBoost reranking
75.33 NDCG@10 on MTEB – vs 73.16 pure vector (3% improvement)
96.50% Recall@20 – Anthropic benchmark vs 90.06% baseline
Source citation – Visual PDF highlighting with page references
98.3% accuracy claimed – 1.2-second average response time
✅ Automatic Chunking – Document segmentation and vector generation automatic; no manual preprocessing.
✅ Pinecone Vector DB – High-speed database supporting billions of embeddings at enterprise scale.
✅ Metadata Filtering – Smart retrieval using tags/attributes for narrowing results at query time.
✅ Citations Reduce Hallucinations – Responses include source citations tying answers to real documents.
Evaluation API – Score accuracy against gold-standard datasets for continuous quality improvement.
GPT-4 + RAG – Outperforms OpenAI in independent benchmarks
Anti-hallucination – Responses grounded in your content only
Automatic citations – Clickable source links in every response
Sub-second latency – Optimized vector search and caching
Scale to 300M words – No performance degradation at scale
Customer support chatbots – Website deployment with 24.8% conversion rate
✅ SQL database chat (unique) – Natural language queries against major databases
Technical documentation – Hundreds of thousands of pages indexed under 5 minutes
Multilingual support – 80+ languages with automatic detection
Developer-focused RAG – MIT-licensed denser-retriever for validation
Financial & Legal – Compliance assistants, portfolio analysis, case law research, contract analysis at scale.
Technical Support – Documentation search for resolving issues with accurate, cited technical answers.
Enterprise Knowledge – Self-serve knowledge bases for teams searching corporate documentation internally.
Shopping Assistants – Help customers navigate product catalogs with semantic search capabilities.
⚠️ NOT SUITABLE FOR – Non-technical teams wanting turnkey chatbot with UI; developer-centric only.
Customer support – 24/7 AI handling common queries with citations
Internal knowledge – HR policies, onboarding, technical docs
Sales enablement – Product info, lead qualification, education
Documentation – Help centers, FAQs with auto-crawling
E-commerce – Product recommendations, order assistance
Documentation – docs.denser.ai, retriever.denser.ai, GitHub READMEs
⚠️ Documentation fragmented – Information scattered across multiple sites
~4-person team – Impacts enterprise support capacity
Open-source community – 261 GitHub stars, 30 forks, MIT license
✅ Comprehensive Docs – docs.pinecone.io with guides, API reference, and copy-paste RAG examples.
Developer Community – Forums, Slack/Discord channels, and Stack Overflow tags for peer support.
Python & Node SDKs – Feature-rich libraries with clean REST API fallback option.
Enterprise Support – Email/priority support for paid tiers with custom SLAs for Enterprise.
Documentation hub – Docs, tutorials, API references
Support channels – Email, in-app chat, dedicated managers (Premium+)
Open-source – Python SDK, Postman, GitHub examples
Community – User community + 5,000 Zapier integrations
Limitations & Considerations
⚠️ No compliance certifications – Missing SOC 2, HIPAA, ISO 27001, GDPR
⚠️ Small team (~4 people) – Potential scaling constraints for enterprise
⚠️ Heavy Zapier dependency – No native Slack, Teams, CRM integrations
⚠️ Fragmented documentation – Scattered across docs, retriever docs, GitHub
⚠️ User feedback – "Plans restrictive, credit limits reached sooner"
⚠️ Developer-Centric – No no-code editor or chat widget; requires coding for UI.
⚠️ Stateless Architecture – Long-term memory, multi-agent flows, conversation state in app code.
⚠️ Limited Models – GPT-4 and Claude 3.5 only; GPT-3.5 not in preview.
File Restrictions – Scanned PDFs and OCR not supported; images in documents ignored.
⚠️ NO Business Features – No lead capture, handoff, or chat logs; pure RAG backend.
Managed service – Less control over RAG pipeline vs build-your-own
Model selection – OpenAI + Anthropic only; no Cohere, AI21, open-source
Real-time data – Requires re-indexing; not ideal for live inventory/prices
Enterprise features – Custom SSO only on Enterprise plan
AI agent capabilities – Process data for intelligent automation with customization
Multi-platform deployment – Launch across websites and messaging with single line
Adaptive learning – Chatbot learns over time using conversation analysis
24/7 availability – Smart AI support with instant answers
✅ Context API – Delivers structured context with relevancy scores for agentic systems requiring verification.
✅ MCP Server Integration – Every Assistant is MCP server; connect as context tool since Nov 2024.
Custom Instructions – Metadata filters restrict vector search; instructions tailor responses with directives.
Retrieval-Only Mode – Use purely for context retrieval; agents gather info then process with logic.
⚠️ Agent Limitations – Stateless design; orchestration logic, multi-agent coordination in application layer.
Custom AI Agents – Autonomous GPT-4/Claude agents for business tasks
Multi-Agent Systems – Specialized agents for support, sales, knowledge
Memory & Context – Persistent conversation history across sessions
Tool Integration – Webhooks + 5,000 Zapier apps for automation
Continuous Learning – Auto re-indexing without manual retraining
Conversational interface – Chat with customers in friendly manner
Business knowledge integration – Trained on documents, websites, Google Drive
Multi-language support – 80+ languages with automatic detection
Lead capture – Integrated forms (name, email, company, role)
Human handoff – Triggers on complexity with Zendesk tickets
Multi-Turn Q&A – GPT-4 or Claude; stateless conversation requires passing prior messages yourself.
⚠️ No Business Extras – No lead capture, handoff, or chat logs; add in app layer.
✅ Context-Grounded Answers – Returns cited responses tied to your documents reducing hallucinations.
Core Focus – Rock-solid retrieval plus response; business features in your codebase.
✅ #1 accuracy – Median 5/5 in independent benchmarks, 10% lower hallucination than OpenAI
✅ Source citations – Every response includes clickable links to original documents
✅ 93% resolution rate – Handles queries autonomously, reducing human workload
✅ 92 languages – Native multilingual support without per-language config
✅ Lead capture – Built-in email collection, custom forms, real-time notifications
✅ Human handoff – Escalation with full conversation context preserved
Customization & Flexibility ( Behavior & Knowledge)
Behavior customization – Define name, tone, response preferences
File support – PDF, DOCX, XLSX, PPTX, TXT, HTML, CSV, XML
Website crawling – Train bot by crawling URLs for knowledge base
Easy knowledge updates – Add documents, re-crawl, update without rebuild
Flexible deployment – Web widget, dashboard, or API integration
Custom System Prompts – Add persona control per call; persistent UI not in preview yet.
✅ Real-Time Updates – Add, update, delete files anytime; changes reflect immediately in answers.
Metadata Filtering – Narrow retrieval by tags/attributes at query time for smarter results.
⚠️ Stateless Design – Long-term memory or multi-agent logic lives in your app code.
Live content updates – Add/remove content with automatic re-indexing
System prompts – Shape agent behavior and voice through instructions
Multi-agent support – Different bots for different teams
Smart defaults – No ML expertise required for custom behavior
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✅ Lively Community – Forums, Slack/Discord, Stack Overflow tags with active developers.
Extensive Documentation – Quickstarts, RAG best practices, and comprehensive API reference.
Support Tiers – Email/priority support for paid; Enterprise adds custom SLAs and engineers.
Framework Integration – Smooth integration with LangChain, LlamaIndex, open-source RAG frameworks.
Comprehensive docs – Tutorials, cookbooks, API references
Email + in-app support – Under 24hr response time
Premium support – Dedicated account managers for Premium/Enterprise
Open-source SDK – Python SDK, Postman, GitHub examples
5,000+ Zapier apps – CRMs, e-commerce, marketing integrations
Additional Considerations N/A
⚠️ Developer Platform Only – Super flexible but no off-the-shelf UI or business extras.
✅ Pinecone Vector DB – Built on blazing vector database for massive data/high concurrency.
Evaluation Tools – Iterate quickly on retrieval and prompt strategies with built-in testing.
Custom Business Logic – No-code tools, multi-agent flows, lead capture require custom development.
Time-to-value – 2-minute deployment vs weeks with DIY
Always current – Auto-updates to latest GPT models
Proven scale – 6,000+ organizations, millions of queries
Multi-LLM – OpenAI + Claude reduces vendor lock-in
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✅ GPT-4 Support – GPT-4o and GPT-4 from OpenAI for top-tier quality.
✅ Claude 3.5 Sonnet – Anthropic's safety-focused model available for all queries.
⚠️ Manual Model Selection – Explicitly choose model per request; no auto-routing based on complexity.
Roadmap Expansion – More LLM providers coming; GPT-3.5 not in current preview.
OpenAI – GPT-5.1 (Optimal/Smart), GPT-4 series
Anthropic – Claude 4.5 Opus/Sonnet (Enterprise)
Auto-routing – Intelligent model selection for cost/performance
Managed – No API keys or fine-tuning required
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✅ SOC 2 Type II – Enterprise-grade security validation from independent third-party audits.
✅ HIPAA Certified – Available for healthcare applications processing PHI with appropriate agreements.
Data Encryption – Files encrypted and siloed; never used to train global models.
Enterprise Features – Optional dedicated VPC, SSO, advanced roles, custom hosting for compliance.
SOC 2 Type II + GDPR – Regular third-party audits, full EU compliance
256-bit AES encryption – Data at rest; SSL/TLS in transit
SSO + 2FA + RBAC – Enterprise access controls with role-based permissions
Data isolation – Never trains on customer data
Domain allowlisting – Restrict chatbot to approved domains
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Free Starter Tier – 1GB storage, 200K output tokens, 1.5M input tokens for evaluation/development.
Standard Plan – $50/month minimum with pay-as-you-go beyond minimum usage credits included.
Token & Storage Costs – ~$8/M input, ~$15/M output tokens, ~$3/GB-month storage, $0.20/day per assistant.
✅ Linear Scaling – Costs scale with usage; Enterprise adds volume discounts and multi-region.
Standard: $99/mo – 10 chatbots, 60M words, 5K items/bot
Premium: $449/mo – 100 chatbots, 300M words, 20K items/bot
Enterprise: Custom – SSO, dedicated support, custom SLAs
7-day free trial – Full Standard access, no charges
Flat-rate pricing – No per-query charges, no hidden costs
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