Context.dev vs Okara: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Context.dev and Okara — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Context.dev
Context.dev
A single REST API that turns any URL into LLM-ready markdown, crawls whole sites, and returns brand, logo, and structured company data.
Key features
- Web Scraping API: Converts any URL into markdown, HTML, raw bytes, sitemaps, screenshots, or images, with JS rendering, anti-bot bypass, and premium proxies included at one credit per page.
- Site Crawling: Crawls an entire domain page by page so teams can build knowledge bases or ground RAG pipelines in fresh content instead of a model's training cutoff.
- Schema-Based Extraction: The Extract endpoint crawls a site and returns structured data shaped to a JSON Schema you supply, removing hand-written parsers.
- Answers Endpoint: Takes a research task plus the JSON shape you want back, researches the web, and returns a structured answer in one API call, with a cheaper fast mode.
- Brand Intelligence: Retrieves logos, colors, fonts, styleguides, descriptions, socials, and addresses for a domain, powering programmatic theming and automated brand kits.
- Logo Link CDN: Serves any company's logo through a direct image URL on a separate quota that does not consume API credits.
- Entity Enrichment and Classification: Extracts products, enriches people from an email or profile URL, searches company news, and returns NAICS/SIC codes or transaction identification.
- SDKs, MCP Server, and CLI: Official TypeScript, Python, Ruby, Go, and PHP SDKs plus an MCP server and CLI let agents and applications integrate without custom HTTP plumbing.
Best for
- Grounding AI Agents: Give an LLM agent live web access so answers reflect the current web rather than the model's training cutoff.
- RAG Knowledge Bases: Crawl documentation sites, academic journals, or PDFs at scale to build and refresh a retrieval corpus.
- Support Chatbot Ingestion: Turn a customer's whole website into the knowledge base behind an AI support bot, as SiteGPT does after migrating from a competing scraper.
- Automated Brand Kits and Theming: Pull a company's logo, colors, and fonts from its domain to theme an app or generate on-brand assets programmatically.
- Onboarding Autofill: Enrich a new signup's company profile from their email domain so onboarding forms prefill instead of asking users to type.
- Website Change Monitoring: Run concurrent monitors against competitor or supplier pages and react when content changes.
- Structured Research Pipelines: Use the Answers endpoint to run repeatable web research tasks that return machine-readable JSON for downstream automation.
Okara
Okara
Encrypted private AI chat with 20–30+ open-source and proprietary models, persistent shared memory, and secure workspaces for professional use.
Key features
- Multi-Model Support: Access 20–30+ open-source and proprietary models (examples include Llama, Qwen, DeepSeek, Kimi, OpenAI, Claude, Gemini) and choose the best model per task without managing model infrastructure.
- Encrypted Shared Memory: Persistent, encrypted conversation memory that preserves context across sessions while protecting user data and reducing the need to re-provide context.
- Hosted, No-Infra Setup: Managed platform removes the requirement to self-host or provision complex model infrastructure, letting teams use open-source models out of the box.
- Secure Workspaces: Team and workspace features designed for sensitive workflows, enabling controlled sharing, collaboration, and auditability for regulated environments.
- Vertical Solutions: Prebuilt configurations and compliance-focused tooling tailored for finance, government, and scientific research use cases handling confidential data.
- Tiered Model Access: Upgradeable access controls that allow organizations to unlock additional or premium models and manage which models are available to users.
- Encrypted, privacy-first chat interface for interacting with language models
- Support for 20–30+ open-source models (examples: Llama, Qwen, DeepSeek, Kimi)
- Persistent memory and context retention across sessions
- Prebuilt solutions and workflows for finance, government, and scientific teams
- Accessible without requiring users to manage model infrastructure
- High-performance workspace optimized for sensitive datasets and experiments
- Model selection/upgrade options to access additional models
- Open-source-powered backend components
Best for
- Private Financial Analysis: Analysts and accountants use encrypted chats with model selection to analyze sensitive financial data, generate reports, and run scenario planning without exposing client information.
- Government Decision Support: Public sector teams leverage secure workspaces and encrypted memory to draft policy notes, review documents, and collaborate on sensitive workflows while maintaining compliance.
- Research Collaboration: Scientists and labs store experiment context in encrypted shared memory, run literature synthesis and data summarization with preferred open-source models, and collaborate securely across teams.
- Secure Knowledge Management: Organizations retain private chat histories and context to build internal knowledge assistants that answer questions from proprietary documents without leaking data.
- Model Evaluation and Selection: Teams compare outputs across multiple open-source and proprietary models on the same prompts to select the best-performing model for specific tasks without infrastructure overhead.
- Financial analysts and accountants querying sensitive financial records with privacy guarantees
- Government officials and agencies needing encrypted, auditable AI-assisted workflows
- Research scientists managing datasets, experiments, and papers in a private workspace
- Teams that want multi-model experimentation without operating model infrastructure
- Professionals requiring persistent conversational context for complex tasks