Perplexity

search engine + LLM, research with citations, Sonar API

Perplexity combines a search engine with an LLM — answers with a list of sources and citations (grounded generation). Sonar API enables programmatic search+answer calls. Pro plan (~$20/mo) gives access to frontier models (GPT-5.4/5.5, Claude 4.6/4.8, Gemini 3.1 Pro) in one interface; Enterprise offers SSO, compliance and higher limits.

Verified: 2026-05-22

Purchase decision (when to choose / when to avoid)

Choose if...

You do research and care about sources/citations (grounded generation).
You want to quickly verify trends/competition and build marketing briefs.
You're building search+answer pipelines via Sonar API.

Avoid if...

You need a 'pure' LLM without search and with full control over behavior/agents.
You have use cases where internet sources are a risk (compliance/PII) — require restrictions and policies.

Cost in practice (scenarios)

Marketer/strategist (SaaS)

Pro/Max depending on research intensity.

briefs, competitive analysis, trends

API (Sonar)

Cost grows with query count and search; plan limits and cache.

RAG grounded on web

These are estimates/scenarios (not an invoice). Actual cost depends on context length, number of users, limits and retention policies.

Deployment / data / enterprise

Deployment channels

Perplexity (SaaS) — research for teams
Sonar API — search+answer in applications

Data policy

Training on data: Depends on plan; for enterprise usually more restrictive terms.
Retention: Depends on plan/service.
Data residency: Depends on enterprise offering.

Key: source usage policies + compliance for sensitive data.

Enterprise readiness

Admin: Enterprise: central management and billing (depending on plan).
SSO/SCIM: Enterprise (depending on plan).
Audit: Enterprise (depending on plan).
DPA: Enterprise (depending on agreement).
Certifications: Depends on agreement.

Worth it when research with citations and verifiability matters.

Best use cases

fast desk research (benchmarks, market trends, tools, case studies)
competitive analysis with verifiable sources and citations
RAG pipelines based on Sonar API (grounded generation from the internet).

Strengths

Grounded generation: answers with source list and citations — verifiability out-of-the-box.
Pro: choice of frontier models (GPT-5.4/5.5, Claude 4.6/4.8, Gemini 3.1 Pro) in one interface.
Very current data — searches the internet in real time.

Weaknesses / risks

Focus on search UI — less 'raw' API than typical LLM providers (limited fine-tuning/agents).
Less control over the base model; Enterprise: custom pricing.

Current models (examples)

Sonar (own model for search+answer); Pro: GPT-5.4/5.5, Claude 4.6/4.8, Gemini 3.1 Pro.

Alternatives (if this model doesn't fit)

Grok

long context and compatible API for agents

OpenAI

fuller LLM API + tools; search as a module

Gemini

grounding and integrations in Google ecosystem (depending on service)

Summary for decision-makers

Type / role: Research / search / grounded generation
When to choose: • fast desk research (benchmarks, market trends, tools, case studies) • competitive analysis with verifiable sources and citations • RAG pipelines based on Sonar API (grounded generation from the internet).