| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | $50/mo | — |
| Capabilities | 16 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Performs real-time web search using neural embeddings to understand query intent and semantic meaning rather than keyword matching. Returns ranked results with full page content (not snippets) and relevance highlights. Supports three latency profiles: Instant (<180ms), Auto (~1s), and Deep Search (up to 60s) for varying use cases. Integrates directly with AI agent frameworks via tool-calling APIs for Claude, GPT, and other LLMs.
Unique: Uses neural embeddings for semantic understanding instead of keyword matching, combined with full-page content retrieval (not snippets) and three configurable latency tiers. Direct integration with Claude/GPT tool-calling APIs eliminates need for wrapper layers. Instant mode achieves <180ms latency for agent loops.
vs alternatives: Faster than traditional web search APIs (Google, Bing) for agent use cases due to <180ms Instant mode and native tool-calling support; returns full page content instead of snippets, reducing downstream API calls for RAG systems.
Performs complex multi-step web research with structured output extraction and reasoning. Accepts complex queries and returns organized, citation-backed results with extracted structured data. Latency up to 60 seconds allows for iterative search refinement and content synthesis. Designed for research tasks requiring more than simple keyword matching, such as comparative analysis, fact-checking, or data aggregation across multiple sources.
Unique: Combines web search with multi-step reasoning and structured output extraction in a single API call. Returns citation-backed results with extracted structured data, eliminating need for separate LLM calls to parse and organize search results. Latency up to 60 seconds allows for iterative refinement within the search process.
vs alternatives: More cost-effective than chaining standard search + separate LLM calls for research tasks; provides structured outputs with citations built-in, whereas competitors require post-processing with additional LLM calls.
Supports filtering search results by domain inclusion/exclusion lists and source restrictions. Allows developers to limit searches to specific domains (e.g., only news sites, only GitHub) or exclude domains (e.g., exclude social media). Filtering is applied server-side, reducing irrelevant results and improving result quality for domain-specific queries.
Unique: Server-side domain filtering eliminates irrelevant results before returning to client, reducing token usage and improving result quality. Supports both include and exclude lists for flexible source control.
vs alternatives: More efficient than client-side filtering because irrelevant results are eliminated server-side; reduces bandwidth and token usage compared to filtering results locally.
Extracts structured data from search results and web pages with citations linking each extracted field back to source URLs. Enables building applications that return organized, verified data instead of raw search results. Works in conjunction with Deep Search for complex extraction tasks. Supports custom schema definition for domain-specific data extraction.
Unique: Combines web search with structured data extraction and automatic citation generation. Citations are built-in and link each extracted field to source URLs, enabling verification without additional processing.
vs alternatives: More efficient than search + separate LLM extraction because extraction and citation are done in single API call; citations are automatically generated instead of requiring post-processing.
Supports retrieving and processing content from multiple URLs or search results in batch operations. Enables efficient processing of large numbers of pages without individual API calls per page. Batch operations are optimized for throughput and cost efficiency, making them suitable for large-scale content processing pipelines.
Unique: Batch operations optimize throughput and cost for large-scale content retrieval. Eliminates per-page API call overhead, making it cost-effective for processing hundreds/thousands of pages.
vs alternatives: More cost-effective than individual API calls for bulk content retrieval; batch processing reduces API overhead and enables higher throughput.
Provides enterprise-grade features including Zero Data Retention (ZDR) option for privacy-sensitive applications and tailored content moderation policies. ZDR ensures no query or result data is retained by Exa after request completion. Custom moderation allows enterprises to define content policies specific to their use case. SOC 2 Type II certified for security and compliance.
Unique: Offers Zero Data Retention option ensuring no query or result data is retained after request completion. Custom moderation policies enable enterprises to define content filtering specific to their use case. SOC 2 Type II certified for security compliance.
vs alternatives: More privacy-protective than standard search APIs due to ZDR option; custom moderation provides more control than one-size-fits-all content policies.
Provides enterprise-grade security features including SSO (Single Sign-On) for authentication, Zero Data Retention (ZDR) for privacy-sensitive deployments, and SOC 2 Type II compliance certification. Enables enterprise customers to meet security and compliance requirements without custom integration or data handling agreements.
Unique: Provides enterprise security features (SSO, ZDR, SOC 2 Type II) as built-in capabilities rather than requiring custom implementation. Most search APIs lack native enterprise security features.
vs alternatives: Offers built-in SSO, ZDR, and SOC 2 compliance vs. competitors requiring custom security implementation or third-party compliance services.
Provides interactive API dashboard at dashboard.exa.ai with guided onboarding that generates stack-specific integration code based on user's technology choices. Dashboard handles API key generation, SDK installation, and provides code examples for selected framework/language combination. Reduces setup time from hours to minutes.
Unique: Provides interactive dashboard with stack-specific code generation, reducing setup time and friction for new users. Most APIs require manual documentation reading and code writing.
vs alternatives: Offers guided onboarding with generated code vs. competitors requiring manual documentation reading and custom integration code.
+8 more capabilities
Store and automatically index high-dimensional vector embeddings in a managed, scalable database without manual infrastructure provisioning. The system handles index optimization and partitioning transparently.
Query stored vectors to find semantically similar items by computing distance metrics between query embeddings and indexed vectors. Returns ranked results based on relevance in sub-100ms latency.
Serve as the retrieval component in Retrieval-Augmented Generation pipelines, providing relevant context documents to language models for grounded responses.
Provide a generous free tier (1 pod, 100K vectors) enabling teams to build and test real applications before committing to paid plans.
Execute searches using both dense vector embeddings and sparse keyword-based vectors simultaneously, combining results to improve relevance by capturing both semantic and lexical similarity.
Filter vector search results based on metadata attributes (tags, categories, timestamps, custom fields) before or during similarity search, enabling faceted and conditional retrieval.
Insert, update, or replace multiple vectors and their metadata in a single batch operation, optimizing throughput for bulk data ingestion without individual API calls.
Pinecone scores higher at 71/100 vs Exa API at 56/100. Exa API leads on adoption, while Pinecone is stronger on quality.
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Partition vector data within a single index using namespaces, enabling logical separation of data (by user, tenant, or dataset) without creating separate indexes.
+4 more capabilities