AI-driven research is powerful—but for enterprises, it’s often slow, expensive, and riddled with security concerns. SambaNova and CrewAI are tackling these challenges head-on with a newly launched open-source Deep Research framework, designed to run AI-powered research at enterprise scale with unprecedented speed, security, and cost-efficiency.
Key Points:
- The open-source framework lets enterprises deploy AI research agents securely on-premise, solving privacy concerns.
- SambaNova’s RDU-powered AI chips outperform GPUs, enabling real-time research with open-source models like Llama 3.3 70B.
- By switching from closed models like GPT-4o to open alternatives, enterprises can save over $2 million per year.
- Built-in agentic routing optimizes research workflows by directing queries to specialized AI agents.
AI research agents—capable of generating detailed reports and insights—have been a major trend this year, with companies like OpenAI, Google, Perplexity, and xAI all introducing their own versions. However, existing solutions can fall short for enterprises that require strict data security, faster turnaround times, and cost-effective implementation. SambaNova's new framework offers an open-source alternative, allowing enterprises to build and deploy AI research agents directly within their secure environments, ensuring sensitive data remains on-premise.
SambaNova, known for its high-performance AI computing solutions, builds custom AI chips (RDU processors) designed to outperform traditional GPUs in enterprise AI workloads. CrewAI, on the other hand, specializes in multi-agent AI frameworks, allowing different AI systems to collaborate on complex tasks. Their newly launched open-source Deep Research framework combines the best of both worlds:
- AI research agents that enterprises can securely deploy on-premise—ensuring data privacy.
- AI inference that runs 3X faster than GPUs using SambaNova’s RDU-powered AI computing.
- Massive cost savings by using open-source models like Llama 3.3 70B instead of expensive proprietary models.
Agentic Routing: Smarter, More Efficient Research Workflows
Beyond just speed and security, the Deep Research framework also introduces agentic routing, which automatically directs queries to the best AI agent for the task. This ensures research is performed faster and more efficiently, with different AI models specializing in specific tasks.
For example:
- A financial analyst might start with a market summary using a general AI search agent.
- For a deeper dive, the AI routes the query to a specialized financial research agent.
- To generate a final investment report, the Deep Research agent compiles and cites findings from various sources.
With this system, even complex research tasks can be completed in seconds rather than minutes—helping businesses analyze market trends, competitive intelligence, and research data in real time.
Minding the Bottom Line
Cost is a major concern for enterprises running AI-powered research. Many AI research systems rely on closed-source models like OpenAI’s GPT-4o, which charge premium rates per token. Since deep research tasks consume 10X to 100X more tokens than simple chatbot queries, expenses can skyrocket quickly.
SambaNova’s framework enables enterprises to switch to open-source AI models like Llama 3.3 70B, which perform comparably while costing significantly less. For enterprises running large-scale AI research, this could mean savings of over $2 million per year.
With this release, SambaNova and CrewAI are pushing forward the enterprise adoption of open-source AI. Companies can give it a try using the SambaNova web app (bring your own API keys) or get the code from GitHub.