Exa Alternative

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Valyu vs Exa Comparison

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Why Teams Switch to Valyu

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1. Proprietary Data Access

• 37M+ PubMed papers - Full-text biomedical literature
• 3M+ SEC filings - Corporate disclosures with semantic search
• 500K+ clinical trials - Study protocols and outcomes
• 2.5M+ ChEMBL molecules - Drug discovery data
• 8M+ patents - IP and prior art search

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2. Better for Specialized Domains

from valyu import Valyu

valyu = Valyu()

# Semantic search across proprietary biomedical data
response = valyu.search(
    "CRISPR delivery mechanisms for therapeutic gene editing",
    included_sources=["valyu/valyu-pubmed", "valyu/valyu-clinical-trials"],
    max_num_results=20
)

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3. Structured Data Support

# Get structured financial data
response = valyu.search(
    "NVIDIA quarterly revenue 2024",
    included_sources=["valyu/valyu-earnings-US"]
)

# Returns structured JSON with revenue figures
print(response.results[0].content)

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When to Choose Valyu Over Exa

• Domain-specific data - Healthcare, finance, academic, patents
• Proprietary sources - Data behind paywalls and APIs
• Structured data - Financial metrics, clinical trial data, drug properties
• Full-text academic search - Beyond abstracts into paper content
• Deep research - Multi-step research with comprehensive reports

• Neural search over publicly available web content

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Code Comparison

from valyu import Valyu

valyu = Valyu()

# Search web + proprietary sources
response = valyu.search(
    "transformer attention mechanisms",
    search_type="all",
    max_num_results=10
)

for result in response.results:
    print(f"Title: {result.title}")
    print(f"Source: {result.source}")  # Shows web vs proprietary
    print(f"Content: {result.content[:200]}...")

# Exa only searches web content
from exa_py import Exa

exa = Exa(api_key="...")

response = exa.search(
    "transformer attention mechanisms",
    num_results=10
)

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Benchmark Performance

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