AI Platform Landscape¶

Summary¶

This chapter provides a comprehensive overview of the major generative AI platforms available today. Students will explore OpenAI's GPT models, Anthropic's Claude, Google's Gemini, and other emerging platforms including Perplexity AI and open-source alternatives. Understanding the strengths and differences between platforms is crucial for selecting the right tool for business applications.

Concepts Covered¶

This chapter covers the following 20 concepts from the learning graph:

OpenAI
GPT-4
GPT-4 Turbo
GPT-4o
ChatGPT
Anthropic
Claude
Claude 3 Sonnet
Claude 3 Opus
Google Gemini
Gemini Pro
Gemini Ultra
Perplexity AI
Search-Augmented Gen
xAI Grok
Meta Llama
Mistral AI
Mixtral
Open-Source Models
Proprietary Models

Prerequisites¶

This chapter builds on concepts from:

Learning Objectives¶

After completing this chapter, students will be able to:

Identify the major generative AI platforms and their capabilities
Compare and contrast GPT, Claude, Gemini, and other platforms
Explain the trade-offs between open-source and proprietary models
Evaluate platform suitability for specific business use cases
Navigate the rapidly evolving AI platform landscape

Introduction¶

The generative AI landscape has evolved from a single dominant player to a competitive ecosystem of platforms, each with distinctive capabilities, philosophies, and target use cases. For business professionals, navigating this landscape requires understanding not just the technical specifications of each platform but also their strategic positioning, pricing models, deployment options, and organizational values.

This chapter surveys the major platforms shaping the generative AI market. We examine OpenAI's GPT family, Anthropic's Claude models, Google's Gemini, and emerging competitors including Perplexity AI, xAI's Grok, and open-source alternatives from Meta and Mistral AI. By chapter's end, readers will possess a framework for evaluating platforms against specific business requirements.

A Rapidly Evolving Landscape

The AI platform landscape changes rapidly. Model capabilities, pricing, and availability may shift between publication and reading. The frameworks for evaluation presented here remain applicable even as specific details evolve.

OpenAI: The Pioneer¶

Company Overview¶

OpenAI launched the generative AI revolution with ChatGPT in November 2022, demonstrating to a global audience what large language models could accomplish. Founded in 2015 as a non-profit research organization with a mission to ensure artificial general intelligence benefits humanity, OpenAI transitioned to a "capped-profit" structure in 2019 to attract the capital necessary for frontier AI development.

Key organizational characteristics:

Partnership with Microsoft: Microsoft has invested over $13 billion, integrating GPT models into Azure, Office 365, and Bing
Developer ecosystem: The largest third-party developer community building on generative AI
Consumer reach: ChatGPT achieved 100 million users faster than any application in history
Research leadership: Pioneered RLHF, scaling laws, and many foundational techniques

ChatGPT: The Consumer Interface¶

ChatGPT is OpenAI's conversational interface to its language models. Available as a free tier (GPT-3.5) and paid subscription (ChatGPT Plus with GPT-4), ChatGPT made AI assistants accessible to mainstream users.

ChatGPT features include:

Feature	Free Tier	Plus Tier ($20/mo)	Team/Enterprise
Model Access	GPT-3.5	GPT-4, GPT-4o	GPT-4, GPT-4 Turbo
Image Generation	Limited	DALL-E 3	DALL-E 3
Custom GPTs	View only	Create & use	Create & share
Code Interpreter	No	Yes	Yes
Web Browsing	No	Yes	Yes
Priority Access	No	Yes	Yes

The GPT-4 Family¶

GPT-4, released in March 2023, represented a significant capability leap over GPT-3.5, demonstrating improved reasoning, broader knowledge, and reduced hallucination rates. The GPT-4 family has subsequently expanded:

GPT-4 (Original)

Parameters: Estimated 1.8 trillion (Mixture of Experts architecture)
Context window: 8,192 tokens (32K variant available)
Strengths: Complex reasoning, nuanced instructions, creative writing
Limitations: Higher latency and cost than smaller models

GPT-4 Turbo

Context window: 128,000 tokens
Knowledge cutoff: More recent than original GPT-4
Pricing: Significantly reduced from original GPT-4
Optimizations: Faster inference, improved instruction following

GPT-4o (omni)

Multimodal native: Natively processes text, audio, images, and video
Speed: Faster than GPT-4 Turbo with comparable quality
Real-time: Enables conversational voice interactions
Cost: Further reduced pricing for production workloads

Diagram: GPT Model Evolution¶

timeline
    title OpenAI GPT Model Evolution (2018-2024)
    section Foundation
        June 2018 : GPT-1
                  : 117M parameters
                  : Proof of concept
        February 2019 : GPT-2
                      : 1.5B parameters
                      : Coherent text generation
    section Scale Era
        June 2020 : GPT-3
                  : 175B parameters
                  : Few-shot learning
                  : API launch
        March 2022 : InstructGPT
                   : RLHF alignment
                   : Following instructions
    section ChatGPT Moment
        November 2022 : ChatGPT
                      : Consumer interface
                      : 100M users in 2 months
                      : AI goes mainstream
    section Multimodal Era
        March 2023 : GPT-4
                   : Multimodal (text + vision)
                   : Advanced reasoning
        November 2023 : GPT-4 Turbo
                      : 128K context window
                      : Lower cost
        May 2024 : GPT-4o
                 : Native multimodal
                 : Real-time voice

GPT Model Comparison:

Model	Parameters	Context	Key Capability
GPT-1	117M	512	Basic text generation
GPT-2	1.5B	1024	Coherent paragraphs
GPT-3	175B	4K	Few-shot learning
GPT-3.5	~175B	4K-16K	Chat optimization
GPT-4	~1.7T*	8K-32K	Multimodal, reasoning
GPT-4 Turbo	~1.7T*	128K	Extended context
GPT-4o	~1.7T*	128K	Native multimodal

*Estimated, not officially disclosed

Anthropic: The Safety-Focused Challenger¶

Company Overview¶

Anthropic was founded in 2021 by former OpenAI researchers, including Dario and Daniela Amodei, with an explicit focus on AI safety research. The company develops AI systems with an emphasis on reliability, interpretability, and alignment with human values.

Distinguishing characteristics:

Constitutional AI: Training methodology that uses principles rather than human feedback for alignment
Safety research: Significant investment in understanding and mitigating AI risks
Enterprise focus: Strong emphasis on business applications with robust safety guarantees
Transparency: Published research on model behavior and limitations

Claude: The Helpful, Harmless, Honest Assistant¶

Claude is Anthropic's family of AI assistants, designed around the principles of being helpful, harmless, and honest (the "3 H's"). Claude models aim to be genuinely useful while avoiding harmful outputs and acknowledging uncertainty.

The Claude 3 family (released early 2024) includes three tiers:

Model	Positioning	Context	Strengths
Claude 3 Haiku	Fast & affordable	200K tokens	Speed, cost-efficiency, high-volume tasks
Claude 3 Sonnet	Balanced performance	200K tokens	Best price-performance ratio
Claude 3 Opus	Highest capability	200K tokens	Complex reasoning, nuanced understanding

Claude 3.5 Sonnet (mid-2024) achieved benchmark scores exceeding Claude 3 Opus while maintaining Sonnet-tier speed and pricing, demonstrating rapid capability improvements.

Key Claude capabilities:

Extended context: 200,000 token context window standard across all models
Document analysis: Optimized for processing and analyzing long documents
Coding: Strong performance on code generation and debugging
Safety: Reduced harmful outputs while maintaining helpfulness
Artifacts: Can generate and display interactive content in the interface

Choosing Between Claude Models

Use Haiku for high-volume, latency-sensitive tasks where cost matters. Use Sonnet for most business applications balancing quality and cost. Reserve Opus for tasks requiring the deepest reasoning or most nuanced outputs.

Google: The Infrastructure Giant¶

Company Overview¶

Google brings to generative AI its massive infrastructure capabilities, extensive research history (including inventing the transformer architecture), and integration with the world's most popular productivity tools. Google's AI efforts span consumer products (Search, Workspace) and enterprise platforms (Google Cloud, Vertex AI).

Strategic position:

Infrastructure advantage: Tensor Processing Units (TPUs), global data centers
Distribution: Integration with Gmail, Docs, Search reaches billions of users
Research heritage: DeepMind, Google Brain, transformer invention
Enterprise platform: Vertex AI for managed AI/ML services

Google Gemini¶

Gemini is Google's family of multimodal AI models, designed from the ground up to understand and generate across text, code, images, audio, and video.

Gemini model tiers:

Model	Capability Level	Use Cases
Gemini Nano	On-device	Mobile applications, offline tasks
Gemini Pro	Mainstream	Most conversational and productivity tasks
Gemini Ultra	Frontier	Complex reasoning, research, enterprise

Gemini 1.5 Pro introduced breakthrough context length—up to 1 million tokens—enabling analysis of entire codebases, multiple documents, or hours of video in a single prompt. This represents a qualitative shift in what's possible with large-context models.

Key Gemini capabilities:

Native multimodality: Trained on interleaved text, images, audio, video from the start
Long context: Up to 1 million tokens enables unprecedented document analysis
Google integration: Deep integration with Workspace, Search, Cloud
Grounding: Can ground responses in Google Search results for current information

Perplexity AI: Search Meets Generation¶

The Search-Augmented Paradigm¶

Perplexity AI pioneered the search-augmented generation approach, combining real-time web search with language model generation. Rather than relying solely on training data (which has a knowledge cutoff), Perplexity retrieves current information from the web and synthesizes it into coherent responses.

This approach addresses fundamental LLM limitations:

Currency: Access to information published after training cutoff
Verifiability: Citations allow users to check sources
Factual grounding: Reduces hallucination by anchoring responses in retrieved content
Transparency: Users can see what sources informed the response

How Search-Augmented Generation Works¶

The Perplexity pipeline:

Query understanding: Parse user question to identify search intent
Search execution: Query web search engine(s) for relevant results
Content retrieval: Fetch and process relevant web page content
Synthesis: Use LLM to generate coherent response from retrieved content
Citation: Include source links for verification

Diagram: Search-Augmented Generation Architecture¶

flowchart LR
    subgraph Input["User Input"]
        A[User Query]
    end

    subgraph Processing["Query Processing"]
        A --> B[Parse Intent]
        B --> C[Generate Search Terms]
    end

    subgraph Search["Web Search"]
        C --> D[Execute Search]
        D --> E[Top N Results]
    end

    subgraph Retrieval["Content Processing"]
        E --> F[Fetch Page Content]
        F --> G[Chunk Text]
        G --> H[Rank by Relevance]
    end

    subgraph Generation["LLM Synthesis"]
        H --> I[Build Context]
        I --> J[Generate Response]
        J --> K[Add Citations]
    end

    subgraph Output["Final Output"]
        K --> L[Response + Sources]
    end

    style Input fill:#e3f2fd
    style Search fill:#e8f5e9
    style Generation fill:#fff3e0
    style Output fill:#e3f2fd

Search-Augmented Generation Steps:

Stage	Process	Output
1. Query	Parse user question	Search intent
2. Search	Execute web queries	Top 10-20 results
3. Retrieve	Fetch page content	Raw text chunks
4. Rank	Score relevance	Top K chunks
5. Synthesize	LLM generation	Coherent response
6. Cite	Add source links	Verified answer

Key Advantage

Unlike standard LLMs limited to training data, search-augmented systems access real-time information, enabling accurate responses about current events, recent research, and changing facts.

Perplexity Capabilities¶

Perplexity offers multiple modes:

Mode	Description	Best For
Basic Search	Quick answers with citations	Simple factual queries
Pro Search	Multi-step research with follow-up	Complex research questions
Focus Modes	Specialized for Academic, Writing, Wolfram, etc.	Domain-specific queries
Spaces	Persistent research threads	Ongoing projects

The platform has become particularly valuable for:

Research tasks: Academic or market research requiring current data
Fact-checking: Verifying claims with source citations
Current events: Questions about recent developments
Technical queries: Developer documentation and tutorials

Emerging Platforms¶

xAI Grok¶

Grok is the AI assistant developed by xAI, Elon Musk's AI company launched in 2023. Grok is integrated with X (formerly Twitter) and positioned as an AI with "personality" and real-time access to X posts.

Distinguishing features:

X integration: Access to real-time social media content
Personality: Designed to have wit and willingness to engage with edgy topics
Image generation: Includes Grok-created image capabilities
Political positioning: Marketed as less "politically correct" than competitors

Evaluation Considerations

When evaluating any AI platform, consider the source and nature of its training data. Platforms with access to social media content may exhibit different characteristics—both beneficial (real-time awareness) and problematic (misinformation, bias)—than those trained primarily on curated content.

Meta Llama¶

Meta's Llama models represent the most significant open-source contribution to the LLM landscape. Meta has released progressively capable models under permissive licenses, enabling researchers, startups, and enterprises to build on frontier-class technology.

Llama model evolution:

Version	Parameters	Release	License
Llama 1	7B-65B	Feb 2023	Research only
Llama 2	7B-70B	July 2023	Commercial use allowed
Llama 3	8B-70B	April 2024	Permissive commercial
Llama 3.1	8B-405B	July 2024	Most permissive

Llama 3.1 405B represents Meta's frontier model, competitive with GPT-4 and Claude 3 Opus on many benchmarks while being freely available for fine-tuning and self-hosting.

Benefits of open-source models:

Control: Full control over model deployment and data handling
Customization: Can fine-tune for specific domains or tasks
Cost: No per-token API fees for inference
Privacy: Data never leaves your infrastructure
Transparency: Model weights and architecture fully visible

Mistral AI¶

Mistral AI, a French startup founded by former DeepMind and Meta researchers, has rapidly established itself as a leading provider of efficient, high-performance open-source models.

Key Mistral models:

Model	Architecture	Parameters	Highlights
Mistral 7B	Dense	7B	Best-in-class for its size
Mixtral 8x7B	MoE	47B (13B active)	Efficient sparse architecture
Mixtral 8x22B	MoE	176B (39B active)	Near-frontier performance
Mistral Large	Dense	Undisclosed	Flagship commercial model

Mixtral models use Mixture of Experts (MoE) architecture, activating only a subset of parameters for each token. This enables larger effective model size with smaller inference cost.

Open-Source vs. Proprietary Models¶

The Strategic Trade-Off¶

Organizations face a fundamental choice between proprietary models (accessed via API from OpenAI, Anthropic, Google) and open-source models (deployed on owned infrastructure or cloud providers).

Factor	Proprietary API	Open-Source Self-Hosted
Upfront cost	Low (pay-per-use)	High (infrastructure)
Marginal cost	Per-token pricing	Minimal after setup
Data privacy	Data sent to provider	Data stays internal
Customization	Limited (prompting, some fine-tuning)	Full control
Maintenance	Provider handles	Internal responsibility
Capability	Frontier access	Slightly behind frontier
Latency	Network-dependent	Infrastructure-dependent
Compliance	Depends on provider	Full control

Decision Framework¶

Diagram: Model Selection Decision Tree¶

The following decision tree helps organizations choose between proprietary APIs and self-hosted open-source models based on key requirements.

flowchart TD
    START["🎯 Model Deployment Decision"]

    Q1{"Data Sensitivity?"}
    START --> Q1

    Q1 -->|"High<br/>(Regulated, Proprietary)"| OS1["🟢 Lean: Open-Source"]
    Q1 -->|"Medium/Low"| Q2

    Q2{"Usage Volume?"}
    Q2 -->|"High<br/>(>1M queries/month)"| OS2["🟢 Lean: Open-Source<br/>(Cost advantage)"]
    Q2 -->|"Medium/Low"| Q3

    Q3{"Customization Needs?"}
    Q3 -->|"Fine-tuning required"| OS3["🟢 Lean: Open-Source"]
    Q3 -->|"Prompting sufficient"| Q4

    Q4{"Latency Requirements?"}
    Q4 -->|"<50ms p95"| OS4["🟢 Lean: Open-Source<br/>(Control needed)"]
    Q4 -->|"Flexible"| Q5

    Q5{"ML Engineering Capacity?"}
    Q5 -->|"Strong team"| HYBRID["🟡 Hybrid Approach"]
    Q5 -->|"Limited"| PROP["🔵 Proprietary API"]

    style START fill:#E3F2FD,stroke:#1565C0,stroke-width:2px
    style OS1 fill:#C8E6C9,stroke:#388E3C
    style OS2 fill:#C8E6C9,stroke:#388E3C
    style OS3 fill:#C8E6C9,stroke:#388E3C
    style OS4 fill:#C8E6C9,stroke:#388E3C
    style HYBRID fill:#FFF9C4,stroke:#F9A825
    style PROP fill:#BBDEFB,stroke:#1976D2

Decision Factor Summary:

Factor	Favors Open-Source	Favors Proprietary
Data Sensitivity	High (regulatory, privacy)	Low (public data OK)
Volume	High (millions/month)	Low to moderate
Customization	Fine-tuning needed	Prompting sufficient
Latency	<50ms required	Flexible requirements
ML Capacity	Strong team available	Limited ML expertise
Budget	Variable (TCO depends)	Predictable per-token

Terminal Recommendations:

Outcome	Description	Example Organization
🟢 Open-Source	Self-host for control, cost, or compliance	Healthcare company with PHI data
🔵 Proprietary	Use APIs for simplicity and access to frontier models	Startup with small team, moderate volume
🟡 Hybrid	Mix strategies based on use case	Enterprise with varied requirements

Hybrid Strategy Benefits

Many organizations use proprietary APIs for prototyping and complex tasks while deploying open-source models for high-volume, production workloads. This provides flexibility without over-committing to either approach.

The Hybrid Approach¶

Many organizations adopt hybrid strategies:

Proprietary for exploration: Use GPT-4 or Claude for prototyping, experimentation, and low-volume applications
Open-source for production: Migrate proven use cases to self-hosted Llama or Mistral for cost control
Specialized models: Fine-tune open-source models for specific domains while using proprietary for general tasks
Fallback chains: Route to open-source for simple queries, escalate complex queries to proprietary

Platform Comparison Framework¶

Evaluation Dimensions¶

When comparing platforms, consider these dimensions:

Capability Dimensions

Reasoning and analysis depth
Code generation quality
Creative writing ability
Instruction following precision
Multimodal capabilities (vision, audio, video)
Context window size

Operational Dimensions

API reliability and uptime
Latency (time to first token, throughput)
Rate limits and scaling
Pricing (input tokens, output tokens, features)

Strategic Dimensions

Data handling and privacy policies
Compliance certifications
Enterprise support availability
Ecosystem and integrations
Company stability and trajectory

Diagram: Platform Comparison Matrix¶

The following matrix enables side-by-side comparison of major AI platforms across key dimensions for informed selection decisions.

AI Platform Comparison Matrix (Last updated: January 2026)

Dimension	OpenAI GPT-4o	Claude 3.5 Sonnet	Gemini 1.5 Pro	Llama 3.3 70B	Mistral Large
Reasoning	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
Coding	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
Context Window	128K	200K	2M	128K	128K
Multimodal	✅ Vision+Audio	✅ Vision	✅ Vision+Audio	⚠️ Limited	✅ Vision
Input Price	$2.50/1M	$3.00/1M	$1.25/1M	Self-host	$2.00/1M
Output Price	$10.00/1M	$15.00/1M	$5.00/1M	Self-host	$6.00/1M
Latency	Fast	Fast	Medium	Variable	Fast
Data Privacy	API only	API only	API only	✅ Self-host	API + Self-host
Fine-tuning	✅ Available	⚠️ Limited	✅ Available	✅ Full control	✅ Available
Enterprise	✅ Strong	✅ Strong	✅ Strong	Community	⚠️ Growing

Legend: ⭐ = Capability rating (1-5), ✅ = Available, ⚠️ = Limited, ❌ = Not available

flowchart LR
    subgraph Selection["Platform Selection by Use Case"]
        direction TB
        UC1["🤖 Customer Service<br/>Chatbot"] --> R1["Claude, GPT-4o<br/>(Safety, instruction following)"]
        UC2["💻 Code Generation"] --> R2["GPT-4o, Claude<br/>(Strong reasoning)"]
        UC3["🔍 Research Assistant"] --> R3["Gemini, Perplexity<br/>(Real-time info)"]
        UC4["📄 Long Document<br/>Analysis"] --> R4["Gemini 1.5, Claude<br/>(Large context)"]
        UC5["💰 Cost-Sensitive<br/>Production"] --> R5["Llama, Mistral<br/>(No API costs)"]
        UC6["🏥 Regulated Industry"] --> R6["Self-hosted Llama<br/>(Data sovereignty)"]
    end

    style Selection fill:#F5F5F5,stroke:#757575
    style R1 fill:#E3F2FD,stroke:#1565C0
    style R2 fill:#E8F5E9,stroke:#388E3C
    style R3 fill:#FFF3E0,stroke:#F57C00
    style R4 fill:#FCE4EC,stroke:#C2185B
    style R5 fill:#E1BEE7,stroke:#7B1FA2
    style R6 fill:#FFECB3,stroke:#FF8F00

Rapid Evolution

This comparison reflects capabilities as of early 2026. The AI platform landscape evolves rapidly—new models launch frequently, pricing changes, and capabilities improve. Always verify current specifications before making deployment decisions.

Evaluation Criteria Definitions:

Dimension	How to Evaluate	What "Best" Means
Reasoning	Complex problem-solving, logical inference	Handles multi-step reasoning accurately
Coding	Code generation, debugging, explanation	Produces working code, understands context
Context Window	Maximum input tokens	Longer = more context can be included
Multimodal	Image, audio, video understanding	Can process multiple modalities
Pricing	Cost per million tokens	Lower cost per quality unit
Latency	Time to first token, streaming	Faster response times
Data Privacy	Where data is processed	Self-hosting = full control

Matching Platform to Use Case¶

Use Case	Recommended Platform(s)	Rationale
Customer service chatbot	Claude, GPT-4	Safety, instruction following
Code generation	GPT-4, Claude	Strong reasoning, code quality
Research assistant	Perplexity, Gemini	Real-time information, citations
Document analysis	Claude (long context), Gemini 1.5	Extended context windows
Cost-sensitive production	Llama, Mistral	No per-token API costs
Regulated industry	Self-hosted open-source	Data sovereignty, compliance
Creative writing	GPT-4, Claude Opus	Nuanced, high-quality output
Real-time applications	Optimized open-source	Latency control

Navigating Platform Evolution¶

Staying Current¶

The AI platform landscape evolves rapidly. Strategies for staying current:

Follow release announcements: Subscribe to platform blogs and changelogs
Monitor benchmarks: Track evaluations like LMSYS Chatbot Arena, MMLU, HumanEval
Experiment continuously: Maintain test harnesses to evaluate new models quickly
Community engagement: Participate in developer communities for real-world insights
Avoid lock-in: Design applications with abstraction layers for model swapping

Future Directions¶

Trends shaping platform evolution:

Multimodality: Native understanding of images, audio, video becoming standard
Agentic capabilities: Models that can take actions, use tools, execute multi-step plans
Specialization: Domain-specific models optimized for medicine, law, finance, code
Efficiency: Smaller, faster models approaching larger model quality
On-device: Capable models running locally on phones and laptops
Real-time: Voice and video interactions at conversational speed

Key Takeaways¶

OpenAI pioneered the commercial LLM market; GPT-4 and ChatGPT remain industry benchmarks with the largest developer ecosystem
Anthropic Claude prioritizes safety and offers the largest standard context window (200K tokens); Claude 3.5 Sonnet provides excellent price-performance
Google Gemini brings infrastructure scale and integration with Google services; Gemini 1.5 Pro's million-token context enables unprecedented document analysis
Perplexity AI demonstrates the power of search-augmented generation for current, cited information
Open-source models (Llama, Mistral) offer control, customization, and cost benefits at near-frontier performance
Platform selection should consider capability requirements, data sensitivity, volume economics, and organizational capacity
Hybrid approaches often optimize for both flexibility and cost by mixing proprietary and open-source models
The landscape evolves rapidly; design for flexibility and maintain evaluation frameworks

Review Questions¶

What are the key differences between OpenAI's GPT-4, GPT-4 Turbo, and GPT-4o?

GPT-4 (original): First frontier multimodal model with strong reasoning; 8K/32K context; higher cost and latency. GPT-4 Turbo: Extended context to 128K tokens; more recent knowledge; significantly reduced pricing; faster inference. GPT-4o: Native multimodal (text, audio, images, video processed together); fastest variant; enables real-time voice conversation; further cost reduction. The progression shows OpenAI optimizing for speed, cost, and multimodal integration while maintaining capability.

Why might an organization choose self-hosted open-source models over proprietary APIs?

Key reasons include: (1) Data privacy: Sensitive data never leaves internal infrastructure, (2) Cost at scale: No per-token fees make high-volume use economical, (3) Customization: Full fine-tuning control for domain-specific applications, (4) Compliance: Easier to meet regulatory requirements when controlling the stack, (5) Latency: Potential for lower latency with optimized infrastructure. Trade-offs include upfront infrastructure costs, maintenance burden, and potentially lagging behind frontier capabilities.

How does Perplexity's search-augmented generation address LLM limitations?

Traditional LLMs have knowledge cutoffs and can hallucinate facts. Perplexity addresses this by: (1) Executing real-time web searches for current information, (2) Retrieving and processing source content, (3) Grounding responses in retrieved content to reduce hallucination, (4) Providing citations so users can verify claims, (5) Synthesizing information from multiple sources into coherent responses. This approach trades off the self-contained nature of pure LLMs for access to current, verifiable information.