Raindrop monitors AI agent performance at scale using Gemini 2.5 Flash

AI agents present unique monitoring challenges compared to traditional software. Failures in AI systems are often "silent," meaning they may not produce standard exceptions or errors, which makes issue detection more difficult for engineering teams. Traditional debugging methods, such as sifting through logs or relying on pre-production evaluations, may fail to capture real-world performance issues.

Raindrop provides a monitoring platform specifically designed for AI agents in production. It helps engineering teams identify complex issues like tool call failures and user frustration by processing massive streams of user interactions. To power its monitoring pipeline efficiently, Raindrop uses Gemini 2.5 Flash for categorization, summarization, and search re-ranking.

Raindrop's platform processes tens of millions of events daily. A primary challenge for Raindrop is enabling engineering teams to query and classify issues across these vast datasets in near real-time. When a user defines a new issue to monitor, Raindrop’s system must rapidly interpret the user's intent and analyze event streams to find matches.

This high-throughput processing requires models that offer extremely low latency and high cost-efficiency. Raindrop needed a solution to power its core "semantic monitoring" pipeline and new features like Deep Search—a tool for researching production AI data—without incurring prohibitive costs or slow response times that would diminish the user experience.

"We needed a model that could quickly process these initial events at a reasonable cost," said Ben Hylak, Co-Founder and CTO of Raindrop. "Gemini 2.5 Flash’s low latency and intelligence enables our Deep Search product which would otherwise be unusable—too slow and too expensive with other models."

Raindrop integrated Gemini 2.5 Flash to manage categorization and query re-writing. The implementation was streamlined using the Vercel AI SDK, allowing Raindrop to integrate the models quickly.

Raindrop leverages Gemini 2.5 Flash for several key functions:

• Query expansion and re-writing: In the Deep Search pipeline, Gemini 2.5 Flash is leveraged to rewrite user queries to optimize results, improving search relevance across millions of events.
• Structured outputs: Raindrop utilizes tool calling and structured outputs to ensure more accurate results from model interactions. This reliability is critical for debugging and providing accurate reasoning traces to users.

Before adopting Gemini 2.5 Flash, Raindrop evaluated other small models but found the cost-to-performance ratio unfavorable. "Other models were either too expensive, too slow, not intelligent enough, or did not produce reliable structured outputs.," Hylak noted. "The intelligence-to-cost ratio only made sense with Gemini 2.5 Flash."

By switching to the Gemini 2.5 Flash model, Raindrop achieved significant performance and efficiency gains.

Key results include:

• Search times reduced from hours to often under a minute
• Costs cut by more than 90%
• Increased reliability across both evaluations and production monitoring

Raindrop uses the Gemini API’s support for structured outputs and tool calls within their Deep Search pipeline. This allows them to get accurate results and view reasoning traces for debugging, which is critical for maintaining a reliable system. The initial integration was completed in minutes using the Vercel AI SDK.

Raindrop is continuing to build out its agent-native monitoring platform with features like complete tracing and the automatic detection of tool-call issues. They believe that as AI models become faster and more reliable, agents will be able to handle increasingly complex tasks.

"Developers should take advantage of Gemini 2.5 Flash’s reliable structured outputs and pricing model to enable use cases that they may have previously thought were prohibitively expensive," Hylak advised. "Gemini 2.5 Flash can likely change the course of your product development by letting you give intelligent experiences to your users that actually work with your pricing model."

To start building your own applications, explore the capabilities of Gemini models in our API documentation.