What are the main differences between Protecto and SciPhi R2R?

Protecto excels in Industry-leading 99% accuracy retention and Only solution preserving context while masking, scoring 87/100 in user ratings with 93% accuracy. SciPhi R2R leads in State-of-the-art retrieval accuracy and Open-source with strong community with 89/100 overall rating and 92% accuracy. Key technical difference: Protecto focuses on AI-powered PII/PHI/PCI detection while SciPhi R2R emphasizes Multimodal ingestion (.pdf, .json, .png, .mp3, etc.).

Which is better for enterprises: Protecto or SciPhi R2R?

Protecto offers SOC 2 compliance for enterprise security. HIPAA compliance is available in Protecto. For scalability, Protecto supports on-premise deployment for maximum control. SciPhi R2R leads with 89/100 enterprise readiness score (83/100 enterprise features).

How much does Protecto cost vs SciPhi R2R?

Protecto starts at custom pricing/custom (usage-based model), while SciPhi R2R begins at contact sales/monthly (freemium model). Both offer free tiers to get started, and free trials are available. Pricing is relatively comparable. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from Protecto to SciPhi R2R?

Migration from Protecto to SciPhi R2R typically requires API-based data export/import, which SciPhi R2R provides. SciPhi R2R provides comprehensive API access excellent documentation for the migration process. Estimated migration time: 1-2 days for cloud setup. Contact SciPhi R2R support for migration assistance and best practices.

What integrations do Protecto and SciPhi R2R support?

Protecto integrates with Snowflake, Databricks, Azure, Google BigQuery, Amazon Redshift (10 integrations total). SciPhi R2R supports OpenAI, Anthropic Claude, Local LLMs, PostgreSQL, S3 storage (10 integrations available). Both offer REST APIs for custom integrations. Integration coverage is comparable, with webhook support for real-time data sync.

What are the main differences between Protecto and SciPhi?

Protecto offers strong capabilities, scoring 0/100 in user ratings. SciPhi provides unique strengths with 0/100 overall rating. Key technical difference: Protecto focuses on advanced features while SciPhi emphasizes specialized capabilities.

Which is better for enterprises: Protecto or SciPhi?

Both platforms provide enterprise-grade security. HIPAA compliance varies - contact vendors for details. For scalability, both offer cloud-based deployment options. SciPhi leads with strong/100 enterprise readiness score.

How much does Protecto cost vs SciPhi?

Protecto starts at custom pricing/month (subscription model), while SciPhi begins at contact sales/month (subscription model). Both require paid plans. Pricing is relatively comparable. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from Protecto to SciPhi?

Migration from Protecto to SciPhi typically requires manual data transfer since API access may be limited. SciPhi offers standard documentation for the migration process. Estimated migration time: 1-2 days for cloud setup. Contact SciPhi support for migration assistance and best practices.

What integrations do Protecto and SciPhi support?

Protecto integrates with (multiple platforms). SciPhi supports (various tools). API availability varies by plan. Integration coverage is comparable.

Protecto vs SciPhi

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

More about SciPhi More about Protecto Get Started Today

Priyansh KhodiyarDevRel at CustomGPT.ai

Fact checked and reviewed by Bill Cava

Published: 01.04.2025Updated: 25.04.2025

In this comprehensive guide, we compare Protecto and SciPhi 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 Protecto and SciPhi, 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 Protecto if: you value industry-leading 99% accuracy retention
Choose SciPhi if: you value state-of-the-art retrieval accuracy

About Protecto

Protecto is ai data guardrails & privacy protection for llms. Protecto is an AI-driven data privacy platform that secures sensitive data in LLM and RAG applications without compromising accuracy. It offers intelligent tokenization, PII/PHI masking, and compliance automation, achieving 99% accuracy retention while protecting privacy. Founded in 2021, headquartered in United States, the platform has established itself as a reliable solution in the RAG space.

Overall Rating

87/100

Starting Price

Custom

About SciPhi

SciPhi is the most advanced ai retrieval system. R2R is a production-ready AI retrieval system supporting Retrieval-Augmented Generation with advanced features including multimodal ingestion, hybrid search, knowledge graphs, and a Deep Research API for multi-step reasoning across documents and the web. Founded in 2023, headquartered in San Francisco, CA, the platform has established itself as a reliable solution in the RAG space.

Overall Rating

89/100

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: Data Privacy 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

Protecto	SciPhi	CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
✅ Enterprise Integrations – APIs connect to Snowflake, Databricks, Salesforce, data lakes ✅ High Volume Processing – Async APIs handle millions/billions of records efficiently PII/PHI Scanning – Detects sensitive data across structured and unstructured sources ⚠️ No File Uploads – Designed for data pipelines, not document upload workflows	Handles 40 + formats—from PDFs and spreadsheets to audio—at massive scale Reference. Async ingest auto-scales, crunching millions of tokens per second—perfect for giant corpora Benchmark details. Ingest via code or API, so you can tap proprietary databases or custom pipelines with ease.	1,400+ file formats – PDF, DOCX, Excel, PowerPoint, Markdown, HTML + auto-extraction from ZIP/RAR/7Z archives Website crawling – Sitemap indexing with configurable depth for help docs, FAQs, and public content Multimedia transcription – AI Vision, OCR, YouTube/Vimeo/podcast speech-to-text built-in Cloud integrations – Google Drive, SharePoint, OneDrive, Dropbox, Notion with auto-sync Knowledge platforms – Zendesk, Freshdesk, HubSpot, Confluence, Shopify connectors Massive scale – 60M words (Standard) / 300M words (Premium) per bot with no performance degradation
Integrations & Channels
Security Middleware – API layer sanitizes data before reaching any LLM ✅ Data Pipeline Integration – Works with Snowflake, Kafka, Databricks for AI workflows ⚠️ No Chat Widgets – Backend security layer, not end-user interface platform	Ships a REST RAG API—plug it into websites, mobile apps, internal tools, or even legacy systems. No off-the-shelf chat widget; you wire up your own front end API snippet.	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
Core Chatbot Features
⚠️ Not a Chatbot – Detects and masks sensitive data, doesn't generate responses ✅ Advanced NER + Regex – Spots PII/PHI while preserving context and accuracy Content Moderation – Safety checks ensure compliance and prevent data exposure	Core RAG engine serves retrieval-grounded answers; hook it to your UI for multi-turn chat. Multi-lingual if the LLM you pick supports it. Lead-capture or human handoff flows are yours to build through the API.	✅ #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 & Branding
⚠️ No Visual Branding – Backend middleware, no UI to customize or brand ✅ Policy Customization – Tailor masking rules via dashboard or config files Compliance-Focused – Configure policies to match GDPR, HIPAA, PCI DSS requirements	Fully bespoke—design any UI you want and skin it to match your brand. SciPhi focuses on the back end, so front-end look-and-feel is entirely up to you.	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
L L M Model Options
✅ Model-Agnostic – Works with any LLM: GPT, Claude, LLaMA, Gemini, custom models ✅ LangChain Integration – Orchestrates multi-model workflows and complex AI pipelines ✅ Context-Preserving – Maintains 99% accuracy (RARI) despite masking sensitive data	LLM-agnostic—GPT-4, Claude, Llama 2, you choose. Pick, fine-tune, or swap models anytime to balance cost and performance Model options.	GPT-5.1 models – Latest thinking models (Optimal & Smart variants) GPT-4 series – GPT-4, GPT-4 Turbo, GPT-4o available Claude 4.5 – Anthropic's Opus available for Enterprise Auto model routing – Balances cost/performance automatically Zero API key management – All models managed behind the scenes
Developer Experience ( A P I & S D Ks)
✅ REST APIs + Python SDK – Straightforward scanning, masking, and tokenizing implementation Detailed Documentation – Step-by-step guides for data pipelines and AI apps Real-Time + Batch – Supports ETL, CI/CD pipelines with comprehensive examples	REST API plus a Python client (`R2RClient`) handle ingest and query tasks Docs and GitHub repos offer deep dives and open-source starter code SciPhi GitHub.	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
Performance & Accuracy
✅ 99% RARI Accuracy – Context-preserving masking vs 70% vanilla masking accuracy ✅ Low Latency – Async APIs and auto-scaling maintain performance at high volume Semantic Preservation – Masked data retains context for accurate LLM responses	Hybrid search (dense + keyword) keeps retrieval fast and sharp. Knowledge-graph boosts (HybridRAG) drive up to 150 % better accuracy Sub-second latency—even at enterprise scale.	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
Customization & Flexibility ( Behavior & Knowledge)
✅ Custom Regex Rules – Fine-tune masking with granular entity types and patterns ✅ Role-Based Access – Privileged users see unmasked data, others see tokens Dynamic Policies – Update masking rules without model retraining for new regulations	Add new sources, tweak retrieval, mix collections—everything’s programmable. Chain API calls, re-rank docs, or build full agentic flows	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
Pricing & Scalability
Enterprise Pricing – Custom quotes based on data volume and throughput ✅ Massive Scale – Handles millions/billions of records, cloud or on-prem deployment Volume Discounts – Free trial available, pricing optimized for large organizations	Free tier plus a $25/mo Dev tier for experiments. Enterprise plans with custom pricing and self-hosting for heavy traffic Pricing.	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
Security & Privacy
✅ Privacy-First – Masks PII/PHI before LLM access, meets GDPR/HIPAA/PCI DSS ✅ End-to-End Encryption – TLS in transit, encryption at rest with audit logs ✅ Deployment Flexibility – Public cloud, private cloud, or on-prem for data residency	Customer data stays isolated in SciPhi Cloud; self-host for full control. Standard encryption in transit and at rest; tune self-hosted setups to meet any regulation.	SOC 2 Type II + GDPR – Third-party audited compliance Encryption – 256-bit AES at rest, SSL/TLS in transit Access controls – RBAC, 2FA, SSO, domain allowlisting Data isolation – Never trains on your data
Observability & Monitoring
Comprehensive Audit Logs – Tracks every masking action and sensitive data detection ✅ SIEM Integration – Real-time compliance and performance monitoring with alerting RARI Metrics – Reports accuracy preservation and data protection effectiveness	Dev dashboard shows real-time logs, latency, and retrieval quality Dashboard. Hook into Prometheus, Grafana, or other tools for deep monitoring.	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
Support & Ecosystem
✅ Enterprise Support – Dedicated account managers and SLA-backed assistance Rich Documentation – API guides, whitepapers, and secure AI pipeline best practices Industry Partnerships – Active thought leadership and compliance standards collaboration	Community help via Discord and GitHub; Enterprise customers get dedicated support Open-source core encourages community contributions and integrations.	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
✅ Secure RAG Focus – Protects sensitive data in third-party LLMs while preserving context ✅ On-Prem Deployment – Total isolation for highly regulated sectors Proprietary RARI Metric – Proves aggressive masking maintains 99% model accuracy	Advanced extras like GraphRAG and agentic flows push beyond basic Q&A Great fit for enterprises needing deeply customized, fully integrated AI solutions.	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
No- Code Interface & Usability
⚠️ No Chatbot Builder – Technical dashboard for policy setup, not end-user interface IT/Security Focus – Config panels for technical teams, not wizard-style tools ✅ Guided Presets – HIPAA Mode, GDPR Mode for rapid compliance onboarding	No no-code UI—built for devs to wire into their own front ends. Dashboard is utilitarian: good for testing and monitoring, not for everyday business users.	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
Competitive Positioning
Market position: Enterprise data security middleware for AI, not RAG platform Target customers: Healthcare, finance, government needing GDPR/HIPAA/PCI compliance and on-prem deployment Key competitors: Presidio (Microsoft), Private AI, Nightfall AI, traditional DLP tools ✅ Competitive advantages: 99% RARI vs 70% vanilla, handles billions of records Pricing advantage: Higher cost but prevents regulatory fines (GDPR €20M, HIPAA $1.5M) Use case fit: Critical for healthcare PII/PHI, financial records, government data compliance	Market position – Developer-first RAG infrastructure combining open-source flexibility with managed cloud service Target customers – Dev teams needing high-performance RAG, enterprises requiring millions tokens/second ingestion Key competitors – LangChain/LangSmith, Deepset/Haystack, Pinecone Assistant, custom RAG implementations Competitive advantages – HybridRAG (150% accuracy boost), async auto-scaling, 40+ formats, sub-second latency Pricing advantage – Free tier + $25/mo Dev plan; open-source foundation enables cost optimization Use case fit – Massive document volumes, advanced RAG needs, self-hosting control requirements	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
A I Models
✅ Model-Agnostic: Works with GPT-4, Claude, LLaMA, Gemini, custom models Pre-Processing Layer: Masks data before LLM access, not tied to providers ✅ LangChain Integration: Orchestrates multi-model workflows and complex AI pipelines ✅ Context-Preserving: 99% RARI vs 70% vanilla masking accuracy No Lock-In: Switch LLM providers without changing Protecto configuration	LLM-Agnostic Architecture – GPT-4, GPT-3.5, Claude, Llama 2, and other open-source models Model Flexibility – Easy swapping to balance cost/performance without vendor lock-in Custom Support – Configure any LLM via API including fine-tuned or proprietary models Embedding Providers – Multiple embedding options for semantic search and vector generation ✅ Full control over temperature, max tokens, and generation parameters	OpenAI – GPT-5.1 (Optimal/Smart), GPT-4 series Anthropic – Claude 4.5 Opus/Sonnet (Enterprise) Auto-routing – Intelligent model selection for cost/performance Managed – No API keys or fine-tuning required
R A G Capabilities
⚠️ NOT A RAG PLATFORM: Security middleware only, not retrieval-augmented generation platform RAG Protection Layer: Masks PII/PHI before RAG indexing and vector database storage ✅ Real-Time Sanitization: Intercepts data to/from RAG systems preventing sensitive data leakage ✅ Context Preservation: Maintains semantic meaning for accurate RAG retrieval despite masking Query + Response Security: Masks sensitive data in queries and post-processes responses Integration Point: Security middleware between data sources and RAG platforms	HybridRAG Technology – Vector search + knowledge graphs for 150% accuracy improvement Hybrid Search – Dense vector + keyword with reciprocal rank fusion Agentic RAG – Reasoning agent for autonomous research across documents and web Multimodal Ingestion – 40+ formats (PDFs, spreadsheets, audio) at massive scale ✅ Millions of tokens/second async auto-scaling ingestion throughput ✅ Sub-second latency even at enterprise scale with optimized operations	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
Use Cases
Healthcare AI: HIPAA-compliant patient analysis, clinical support, PHI masking in medical records Financial Services: PCI DSS payment data compliance, financial records, customer service chatbots Government & Defense: Classified data protection, citizen privacy, strict data residency requirements Customer Support: Secure analysis of tickets, emails, transcripts with PII for AI insights Multi-Agent Workflows: Role-based data access across AI agents for global enterprises Claims Processing: Insurance PHI protection for accurate, privacy-preserving RAG workflows	Enterprise Knowledge – Process millions of documents with knowledge graph relationships Support Automation – RAG-powered support bots with accurate, grounded responses Research & Analysis – Agentic RAG for autonomous research across collections and web Compliance & Legal – Large document repositories with precise citation tracking Internal Docs – Developer-focused RAG for code, API references, technical knowledge Custom AI Apps – API-first architecture integrates into any application or workflow	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
Security & Compliance
✅ GDPR/HIPAA/PCI DSS: Pre-configured policies, BAA support, Safe Harbor PHI masking PDPL/DPDP Compliance: Saudi Arabia PDPL, India DPDP with regional policies ✅ End-to-End Encryption: TLS in transit, encryption at rest with audit logs ✅ Role-Based Access: Privileged users see unmasked data, others see tokens ✅ Deployment Flexibility: SaaS, VPC, on-prem for strict data residency Zero Data Egress: On-prem ensures data never leaves organizational boundaries	Data Isolation – Single-tenant architecture with isolated customer data in SciPhi Cloud Self-Hosting Option – On-premise deployment for complete data control in regulated industries Encryption Standards – TLS in transit, AES-256 at rest encryption Access Controls – Document-level granular permissions with role-based access control (RBAC) ✅ Open-source R2R core enables security audits and compliance validation ✅ Self-hosted deployments tunable for HIPAA, SOC 2, and other regulations	SOC 2 Type II + GDPR – Regular third-party audits, full EU compliance 256-bit AES encryption – Data at rest; SSL/TLS in transit SSO + 2FA + RBAC – Enterprise access controls with role-based permissions Data isolation – Never trains on customer data Domain allowlisting – Restrict chatbot to approved domains
Pricing & Plans
Enterprise Pricing: Custom quotes based on volume, throughput, deployment model ✅ Free Trial: Test platform capabilities before commitment with hands-on evaluation Volume Discounts: Pricing scales with usage, better rates for higher volumes Cost Justification: Prevents regulatory fines (GDPR €20M, HIPAA $1.5M penalties) ⚠️ No Public Pricing: Contact sales for custom quotes tailored to needs	Free Tier – Generous no-credit-card tier for experimentation and development Developer Plan – $25/month for individual developers and small projects Enterprise Plans – Custom pricing based on scale, features, and support Self-Hosting – Open-source R2R available free (infrastructure costs only) ✅ Flat subscription pricing without per-query or per-document charges ✅ Managed cloud handles infrastructure, deployment, scaling, updates, maintenance	Standard: $99/mo – 10 chatbots, 60M words, 5K items/bot Premium: $449/mo – 100 chatbots, 300M words, 20K items/bot Enterprise: Custom – SSO, dedicated support, custom SLAs 7-day free trial – Full Standard access, no charges Flat-rate pricing – No per-query charges, no hidden costs
Support & Documentation
✅ Enterprise Support: Dedicated account managers, SLA-backed assistance for large deployments Comprehensive Docs: REST API, Python SDK, integration guides for data pipelines Whitepapers & Best Practices: Security frameworks, compliance guides, AI pipeline architectures Integration Guides: Snowflake, Databricks, Kafka, LangChain, CrewAI, model gateways Professional Services: Implementation help, custom policy setup, security workflow design ✅ Training Resources: HIPAA Mode, GDPR Mode presets for rapid deployment	Comprehensive Docs – Detailed docs at r2r-docs.sciphi.ai covering all features and endpoints GitHub Repository – Active open-source development at github.com/SciPhi-AI/R2R with code examples Community Support – Discord community and GitHub issues for peer support Enterprise Support – Dedicated channels for enterprise customers with SLAs ✅ Python client (R2RClient) with extensive examples and starter code ✅ Developer dashboard with real-time logs, latency, and retrieval quality metrics	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
⚠️ NOT A RAG PLATFORM: Requires separate RAG/LLM infrastructure for complete solution ⚠️ NO Chat UI: Technical dashboard only, not end-user chatbot interface ⚠️ Developer Integration Required: APIs/SDKs need coding expertise for pipeline integration Higher Cost: Enterprise pricing but prevents GDPR €20M, HIPAA $1.5M fines Performance Overhead: Real-time masking adds sub-second latency in high-throughput systems Best For: Regulated industries (healthcare, finance, government) requiring compliance, not general-purpose	⚠️ Developer-Focused – Requires technical expertise to build and wire custom front ends ⚠️ Infrastructure Requirements – Self-hosting needs GPU infrastructure and DevOps expertise ⚠️ Integration Effort – API-first design means building your own chat UI ⚠️ Learning Curve – Advanced features like knowledge graphs require RAG concept understanding ⚠️ Community Support Limits – Open-source support relies on community unless enterprise plan	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
Core Agent Features
✅ Multi-Agent Access Control: Fine-grained identity-based access enforcement across agentic workflows ✅ Role-Based Security: Controls who sees what at inference time with role-specific permissions LangChain/CrewAI Integration: Comprehensive agentic workflow protection with major orchestration frameworks Agent Context Sanitization: Masks PII/PHI in prompts, context, and responses during multi-step reasoning SecRAG for Agents: RBAC integrated into retrieval, checks authorization before agent access ⚠️ NOT Agent Orchestration: Secures workflows but requires LangChain/CrewAI for coordination	Agentic RAG – Reasoning agent for autonomous research across documents/web with multi-step problem solving Advanced Toolset – Semantic search, metadata search, document retrieval, web search, web scraping capabilities Multi-Turn Context – Stateful dialogues maintaining conversation history via conversation_id for follow-ups Citation Transparency – Detailed responses with source citations for fact-checking and verification ⚠️ No Pre-Built UI – API-first platform requires custom front-end development ⚠️ No Lead Analytics – Lead capture and dashboards must be implemented at 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
R A G-as-a- Service Assessment
⚠️ NOT RAG-AS-A-SERVICE: Data security middleware, not retrieval-augmented generation platform Security Middleware: Sits between data sources and RAG platforms as protection layer RAG Protection: Sanitizes documents before indexing, queries before retrieval, responses before delivery ✅ Context-Preserving RAG: 99% RARI vs 70% vanilla masking for accurate retrieval Stack Position: Protecto (security) + CustomGPT/Vectara (RAG) + OpenAI (LLM) = complete solution Best Comparison: Compare to Presidio, Private AI, Nightfall AI, not RAG platforms	Platform Type – HYBRID RAG-AS-A-SERVICE combining open-source R2R with managed SciPhi Cloud Core Mission – Bridge experimental RAG models to production-ready systems with deployment flexibility Developer Target – Built for OSS community, startups, enterprises emphasizing developer flexibility and control RAG Leadership – HybridRAG (150% accuracy), millions tokens/second, 40+ formats, sub-second latency ✅ Open-source R2R core on GitHub enables customization, portability, avoids vendor lock-in ⚠️ NO no-code features – No chat widgets, visual builders, pre-built integrations or dashboards	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

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Final Thoughts

Final Verdict: Protecto vs SciPhi

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

When to Choose Protecto

You value industry-leading 99% accuracy retention
Only solution preserving context while masking
3000+ enterprise customers already secured

Best For: Industry-leading 99% accuracy retention

When to Choose SciPhi

You value state-of-the-art retrieval accuracy
Open-source with strong community
Production-ready with proven scalability

Best For: State-of-the-art retrieval accuracy

Migration & Switching Considerations

Switching between Protecto and SciPhi 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

Protecto starts at custom pricing, while SciPhi 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 Protecto and SciPhi 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 28, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.

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Priyansh Khodiyar

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

Protecto vs SciPhi