Report: Helicone vs Braintrust

Executive summary

Helicone and Braintrust serve very different but occasionally overlapping needs in the AI product lifecycle. Helicone is an observability- and gateway-focused platform built to monitor, optimize, and secure production LLM traffic. Braintrust is an evaluation-, talent- and workflow-centric platform aimed at hiring, validating, and operating human + AI evaluation at scale for enterprises. Choosing between them depends on whether your priority is LLM production observability, cost and routing (Helicone) or evaluation, talent sourcing, and recruitment/annotation workflows (Braintrust).

The debate — two voices

Proponent (Helicone): "Helicone is an open-source observability platform designed for developers building production-ready Large Language Model (LLM) applications. It focuses on monitoring, debugging, and optimizing LLMs throughout their lifecycle." (https://www.helicone.ai/blog/llm-observability) This voice emphasizes Helicone's one-line proxy integration, unified dashboards, response caching, cost analytics, and an AI Gateway that provides intelligent routing across providers. For example: "The AI Gateway provides a unified API compatible with OpenAI's specifications, enabling developers to seamlessly switch between multiple LLM providers..." (https://docs.helicone.ai/gateway/overview?utm_source=openai)

Proponent (Braintrust): "Braintrust is a decentralized talent network that connects enterprises with a global pool of freelancers. It aims to reduce transaction fees and enhance trust and transparency by eliminating intermediaries." (https://www.usebraintrust.com/clients) From the Braintrust side, the highlight is efficient, AI-driven candidate matching and evaluation tooling: "Braintrust's AI-driven matching engine scans its global talent network to identify top candidates..." (https://www.usebraintrust.com/candidate-matching) and enterprise features like SOC2 compliance and hybrid deployment.

Where they overlap

Tracing & evaluation workflows: Both platforms provide trace/logging and evaluation features for LLM interactions, but for different ends. Helicone focuses on runtime observability (requests/responses, tokens, latency, cost). Braintrust focuses on evaluation pipelines (scorers, human annotation, LLM-as-judge). "Helicone provides detailed logging of all LLM requests, capturing timestamps, input prompts, model responses, and relevant metadata." (https://docs.helicone.ai/getting-started/integration-method/custom?utm_source=openai) vs "Braintrust allows integration of any AI model or endpoint into your evaluation and tracing workflows." (https://www.braintrust.dev/docs/integrations/ai-providers/custom?utm_source=openai)
Integration with provider ecosystem: Helicone's AI Gateway offers OpenAI-compatible unified access to many models (OpenAI, Anthropic, Google Gemini) and intelligent model routing. (https://docs.helicone.ai/integrations/overview?utm_source=openai) Braintrust supports plugging in custom models into evaluation pipelines but is not an LLM gateway.

Key strengths — Helicone

Production observability and cost management: Helicone offers unified dashboards, token tracking, detailed cost breakdowns and caching to reduce API spend. "Helicone offers detailed cost analytics and optimization tools to help manage AI budgets effectively... including Cost Breakdown by User, Project, or Model; Automatic Model Selection; Caching for Cost Reduction." (https://docs.helicone.ai/guides/cookbooks/cost-tracking?utm_source=openai)
AI Gateway & routing: Intelligent routing, latency-aware load balancing, health checks and failovers are core features. "The gateway implements latency-based P2C with PeakEWMA and automatic health checks every 5 seconds..." (https://docs.helicone.ai/ai-gateway/concepts/loadbalancing?utm_source=openai)
Developer ergonomics and rapid setup: Proxy or manual logger options, plus an open-source self-hosting path. "Helicone provides a simple integration via proxy or asynchronous logging..." (https://docs.helicone.ai/getting-started/integration-method/custom?utm_source=openai)

Key strengths — Braintrust

Evaluation and human-in-the-loop workflows: Braintrust excels at scoring, human annotation, agent simulation and dataset management — essential for model evaluation and alignment. "Braintrust integrates evaluation infrastructure directly into the development workflow, offering code-based scorers, human annotation, agent simulation, and dataset management." (https://www.braintrust.dev/articles/helicone-vs-braintrust?utm_source=openai)
Talent sourcing & recruitment: Enterprise hiring, vetted talent, and operational tools for scaling human reviewers and annotators. "Braintrust's network comprises over one million vetted and verified talent members... Only 2% of applicants are admitted." (https://www.usebraintrust.com/candidate-matching)
Enterprise compliance & reliability: SOC 2 Type II compliance, DPAs and hybrid deployment support. "Braintrust is SOC 2 Type II compliant." (https://www.braintrust.dev/blog/soc2?utm_source=openai)

Where the comparison breaks down (critical differences)

Different primary use cases: Helicone is primarily an LLM observability/gateway product. Braintrust is primarily an evaluation, workforce and hiring platform that also supports evaluation and tracing. They are complementary more than direct substitutes.
Architecture & operational risk: Helicone's proxy architecture routes LLM calls through its infrastructure, which brings features (unified routing, caching) but creates risk: "Because all traffic routes through Helicone's infrastructure, any downtime or network issues on their end directly impact your application." (https://docs.helicone.ai/references/availability?utm_source=openai) Braintrust's SDK-style integration is designed so that if Braintrust is unavailable your application can continue to operate with only trace loss: "If Braintrust becomes unavailable, the AI application continues to serve users normally, with only temporary loss of trace visibility..." (https://www.braintrust.dev/articles/helicone-vs-braintrust?utm_source=openai)
Observability depth vs evaluation depth: Helicone captures request/response and metadata but cannot see document retrieval or upstream business logic that assembled a prompt when used as a proxy: "Proxy-based logging only captures the request sent to the LLM and the response received. You can't see the document retrieval that produced the context..." (https://www.helicone.ai/blog/llm-observability?utm_source=openai) Braintrust, being evaluation-focused, provides richer tooling for human scoring and dataset-driven evaluation.

Limitations and cautions

Helicone: Potential single point of failure through proxy; added latency for low-latency apps; unclear public benchmarks for sustained extreme scale without asynchronous logging; caching helps but may not help dynamic prompts. "This added latency can be particularly detrimental in applications requiring real-time processing or low-latency responses." (https://docs.helicone.ai/ai-gateway/concepts/cache?utm_source=openai)
Braintrust: Not a gateway — it won't reduce API costs via caching/routing; its focus is on evaluation and talent. Integrations may need SDK work and enterprise onboarding. "Braintrust's SDK-based architecture demands significant technical expertise for integration." (https://www.braintrust.dev/docs/integrations/ai-providers/custom?utm_source=openai)

Direct quotes (selected)

"Helicone is an open-source observability platform designed for developers building production-ready Large Language Model (LLM) applications." (https://www.helicone.ai/blog/llm-observability?utm_source=openai)
"Braintrust is a decentralized talent network that connects enterprises with a global pool of freelancers..." (https://www.usebraintrust.com/clients)
"Helicone offers detailed cost analytics and optimization tools to help manage AI budgets effectively..." (https://docs.helicone.ai/guides/cookbooks/cost-tracking?utm_source=openai)
"Braintrust is SOC 2 Type II compliant." (https://www.braintrust.dev/blog/soc2?utm_source=openai)

Recommendations — how to choose

Choose Helicone if: you need runtime observability, cost reduction (caching + model selection), multi-provider routing, real-time dashboards and an LLM gateway that centralizes control over requests/responses. See Helicone's AI Gateway and routing docs for details: Helicone AI Gateway and routing basics.
Choose Braintrust if: your priority is evaluation pipelines, human-in-the-loop annotation, hiring/outsourcing reviewers, or enterprise compliance and governance for evaluation workflows. See Braintrust's evaluation and enterprise features: Braintrust evaluation and annotation workflows and Braintrust enterprise compliance and deployment.
Use both if: you run LLM-powered products that need production observability + structured human evaluation. A common architecture is: keep Helicone’s observability/gateway in front of LLM traffic for routing/cost control while integrating Braintrust’s evaluation pipelines (human + automated scoring) to continuously assess and label outputs for model improvement.

Related threads you might want next

Bottom line

Helicone and Braintrust address different corners of AI product engineering. Helicone is the operations- and cost-focused gateway and observability choice for production LLM apps; Braintrust is the evaluation, talent, and workforce platform for improving model quality and scaling human review. For many teams the correct answer is not "Helicone or Braintrust" but "Helicone plus Braintrust"—Helicone to run and monitor the models, Braintrust to evaluate and improve them.

Sources

URLs are embedded inline with quotes and references throughout the report. If you want, I can produce a side-by-side feature matrix, an integration checklist for using both together, or a short recommendation tailored to your product and traffic profile.

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