About Boosted.ai
Boosted.ai runs a high-volume agentic AI environment for institutional investors, wealth managers, and large investing platforms. The same engine runs every research workflow, so both professionals and retail investors get the identical capability: automated thesis checks, filing analysis, and multi-step AI research processes that deliver fast, consistent, decision-ready outputs for any portfolio.
Each request may trigger large branching workflows across distributed services. At small scale this is trivial, but at enterprise scale, with tens of thousands of concurrent users and billions of tokens processed daily, the system must keep latency tight, sustain throughput, and remain invisible to the end user.
Enterprise clients cannot tolerate slowdown, variance, or instability when it comes to tools to assist in decision-making. Reliability is a product requirement.
The challenge: Observability across thousands of concurrent AI workflows
Boosted.ai’s architecture spans FastAPI services, DAG-like agent workflows with high trace depth, and distributed Python and Go microservices communicating over gRPC. Legacy tooling made cross-service correlation slow and incomplete. gRPC instrumentation required custom plumbing. Profiling bottlenecks in deeply nested agent workflows was time-consuming, and identifying a single degrading dependency took too long relative to the impact on client workloads. The gap was clear: without full-stack observability engineered for generative AI pipelines and high-volume multitenant environments, performance risk accumulates. For institutional finance clients, performance risk becomes business risk.
The Solution: Pydantic Logfire + OpenTelemetry integrated into Boosted.ai’s platform
Key capabilities now supporting Boosted.ai:
FastAPI performance instrumentation
Automatic tracing keeps core APIs responsive during peak market events, sustaining consistent latency for all client research actions.
AI workflow tracing across large DAG structures
Agentic workflows fan out into hundreds or thousands of steps. Tracing exposes slow or failing nodes instantly, preventing incomplete or unreliable responses.
Custom spans for controlled observability overhead
Boosted.ai instruments only what produces value, keeping infrastructure overhead low while supporting increasing client volume.
Cross-service tracing for Python ↔ Go gRPC
Manual gRPC instrumentation now rolls into a unified trace. Engineers see complete multi-language call paths, making root-cause identification deterministic instead of trial-and-error.
Scalable observability for LLM agents
Pydantic Logfire now supports:
- 50,000+ agent workflows
- 10,000+ monthly client research queries
- billions of tokens processed daily
These metrics matter because they reflect real client usage at enterprise scale. The observability layer must match that scale without adding latency or instability.
Operational impact for Boosted.ai clients
Unified tracing
Full visibility across every service powering AI research workflows. Issues surface before they affect end-user performance.
Performance insights at the point of degradation
Engineers pinpoint bottlenecks in minutes. Slowdowns are isolated immediately, maintaining confidence for institutional users who depend on deterministic system behavior.
Reliability across the entire environment
Efficient span management keeps observability cost-neutral while workloads grow. High-traffic investing platforms can onboard without compromising stability.
Architecture built for continuous validation
Support for capabilities like Pydantic Evals enables ongoing regression testing of model output quality as new features ship, ensuring consistency and accuracy across client-facing research tools.
The result: A platform that meets enterprise-grade expectations
For Boosted.ai’s clients — institutional investment teams, wealth managers, and investing platforms with huge user bases — latency variance, partial outputs, or downtime are unacceptable. These users operate in high-stakes environments where system reliability influences trust, brand value, and, ultimately, economic outcomes.
By embedding AI-native observability across all workflows, Boosted.ai sustains a research experience that remains fast, stable, and predictable under peak load.
"Before Logfire, understanding what went wrong meant digging through huge text logs. Now we can trace an agent's reasoning step by step, fix issues in minutes, and keep the platform fast even when tens of thousands of users are active."
— Zach Silver, Staff Software Engineer, Boosted.ai
