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COMPARE/AI INFRASTRUCTURE/EXA · VOYAGE AI
AI INFRASTRUCTURE · HEAD TO HEAD● YOU PICK THE WINNER
ExavsVoyage AI
Two tools. Verified data. Side by side. Everything on this page is vendor-sourced and dated. We don't pick a winner. That's your call. Use the Lens to apply your own criteria.
Exa
Starts at$5/1k requests
Free planYes
Plans8 tiers
Features9 features
Integrations32 native
PlatformsWeb · API
MCP serverYes
Voyage AI
Starts atN/A
Free planYes
Plans6 tiers
Features9 features
Integrations20 native
PlatformsWeb · API
MCP serverNo
● THE LENS · YOUR COMPARISON · YOUR WINNER
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Your criteria. Your weighting. Your verdict. Pick your axes and watch the data respond. You decide what's best for you, not us.
PRESETS
Based on your selections, the data leans this way, scroll down to read more.
AXES · 8 · PICK ANY
ALL TOOLS UPDATED 2026.04.30
CAPABILITIES
Where they overlap, where they don't
Functional roles from both tools, split by coverage. The corners are where the two tools become different products.
Only ExaRoles covered exclusively by this tool.
3- Web Researcher
- Research Assistant
- Data Extractor
Both1 of 5 roles in the verified union.
1- Api Caller
Only Voyage AIRoles covered exclusively by this tool.
1- Embedding Model
ROLE COVERAGE···
PLAN LADDER
Every plan, mapped to one ladder
Both tools laid out by ascending monthly cost. Expand any plan to see what's included.
AT A GLANCE
More comparable fields
Every data point from both tools, side by side. Nothing hidden, nothing weighted.
FACT
Category
AI Infrastructure
AI Infrastructure
Starting Price
$5/1k requests
N/A
Pricing Model
Freemium
Freemium
Plans
8 tiers
6 tiers
Free Plan
Yes
Yes
Free Trial
No
No
Platforms
Web · API
Web · API
Top Features
9 features
9 features
Integrations
32 native
20 native
Public API
Yes
Yes
Webhooks (In)
No
No
INTEGRATIONS · 49 TOOLS
Integrations from both tools, side by side
Integrations sourced from each tool's own documentation. Where both tools connect to the same app, you see it once. The rest shows where their ecosystems diverge.
BOTH3
29
17UNION49
INTEGRATION
NOTES
Haystack
✓
✓
Both. Use Exa search in Haystack RAG pipelines. Framework integration.
LangChain
✓
✓
Both. Use Exa as a retriever or tool in LangChain/LangGraph apps. Framework integration.
LlamaIndex
✓
✓
Both. Use Exa as a search tool in LlamaIndex pipelines. Framework integration.
DEEP READ
What it does
Features and capabilities sourced from each tool's own documentation, structured for comparison.
Exa
TOP FEATURES · 9
F01
Neural Web Search APIsearchExa's search endpoint uses an embeddings-based ranking model to return semantically relevant results for natural-language queries. Six search types cover the latency-quality range from auto and fast (under 200ms) to deep-lite, deep, and deep-reasoning (up to 60s), with each type tuned for a different speed-quality tradeoff. Each result includes title, URL, published date, author, and optional page content.
F02
Token-Efficient Content HighlightsaiThe Contents API retrieves full page text, AI-generated summaries, or highlights. Highlights are ML-extracted excerpts of the most relevant tokens from a page, reducing LLM token consumption by up to 90% compared to passing full page text. Results are served from Exa's cache with automatic live-crawl fallback for uncached pages.
F03
LLM Answer Endpoint with CitationstextThe /answer endpoint runs an Exa search and uses an LLM to generate a direct factual answer or a detailed summary with grounded source citations. It supports streaming via SSE, structured JSON output via the outputSchema parameter, and is compatible with OpenAI's chat completions interface for drop-in integration into existing pipelines.
F04
Structured Output ExtractiondataAny search or answer endpoint call accepts an outputSchema JSON Schema parameter, which causes the API to synthesize and return structured data matching that schema from web results. This enables complex enrichment workflows such as extracting company names, founding years, and funding stages across a list of URLs at scale, without additional parsing or downstream LLM calls.
F05
Find Similar LinkssearchThe /findSimilar endpoint accepts a source URL and returns a ranked list of semantically similar web pages. Developers apply optional domain inclusion and exclusion filters, crawl and publish date ranges, and can request full page content, highlights, or summaries for each result within the same API call.
F06
Websets — AI-Powered List BuilderautomationWebsets converts a natural-language description of any target list into a curated, AI-verified dataset. Users describe a target population in plain language and Websets returns matching companies, people, research papers, or articles, with AI agents cross-referencing each result across multiple data sources, scoring relevance, and adding enrichment columns defined by custom AI prompts.
Voyage AI
TOP FEATURES · 9
F01
Text Embedding ModelsaiSpectrum of high-accuracy models — general-purpose (voyage-4-large, voyage-4, voyage-4-lite), domain-specific (voyage-code-3, voyage-finance-2, voyage-law-2), and multilingual variants. All support up to 32,000-token context windows. Accessible via Python SDK, TypeScript library, and REST API.
F02
Contextualized Chunk EmbeddingsdataThe voyage-context-3 model encodes document chunks with awareness of surrounding context from the same document, producing context-aware vectors that improve retrieval accuracy for long-document RAG. Resolves the common problem of context loss during chunking.
F03
Multimodal EmbeddingsimageVoyage multimodal models (voyage-multimodal-3.5) vectorize interleaved text and visual content — including screenshots of PDFs, slide decks, tables, figures, and video — into a unified vector space. No separate ETL pipelines or text extraction required.
F04
RerankerssearchVoyage rerankers (rerank-2.5, rerank-2.5-lite) are cross-encoder models that jointly score query-document pairs to refine ranked results from initial retrieval passes. Latest models support instruction-following, multilingual input, 32,000-token context, and up to 1,000 candidate documents per call.
F05
Flexible Dimensions and QuantizationsettingsModels support Matryoshka learning for output at 256, 512, 1024, or 2048 dimensions in a single call. Quantized output formats (int8, uint8, binary, ubinary) reduce vector storage costs by up to 32x with minimal accuracy loss.
F06
Batch Inference APIapiBatch API accepts large embedding and reranking jobs up to 100,000 inputs per batch via JSONL file with 12-hour completion, automatic retries, and 33% cost discount. Results returned as downloadable JSONL matched to custom IDs.
WHO IT'S FOR
Who each tool is built for
Use cases and fit tags from each tool's own positioning. Match these to your context, not their marketing.
developerDevelopers building AI agents, chatbots, or voice assistants integrate Exa to give models real-time web access. The auto and instant search types return current facts, news, and citations in under 200ms, allowing agents to answer time-sensitive questions accurately without hallucinating. Cursor uses Exa to power coding agents with low-latency web search, and OpenRouter uses it to ground 400-plus LLM models in current data.
businessGo-to-market and revenue teams use Websets to build precise lead lists from a natural-language ICP description. AI agents verify each result against multiple sources and add enrichment columns for CEO name, funding stage, tech stack, and contact details, then export to CSV or push directly into Clay or CRM tools. Flatfile replaced hundreds of thousands of dollars in data assembly costs using this approach.
researchResearch scientists and product teams use Exa's research paper category (100M-plus papers indexed) combined with deep-reasoning search to synthesize multi-source answers to complex queries. The /answer endpoint returns grounded summaries with citations from arXiv, Semantic Scholar, and research blogs. Anara uses this workflow to help scientists surface relevant papers inside AI-powered research tools.
enterpriseFinancial and legal services use voyage-finance-2 and voyage-law-2 paired with vector databases and rerankers to build RAG systems over large document corpora. Harvey AI used custom Voyage legal embeddings fine-tuned on proprietary datasets to reduce irrelevant document retrieval by 25% and cut vector database costs by 3x compared to general-purpose models.
KEEP COMPARING
Related comparisons
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