Report: Helicone vs Braintrust.ai

Executive summary

Helicone and Braintrust.ai address overlapping needs in the LLM tooling stack but from different angles. Helicone is an observability + gateway platform optimized for operational monitoring, cost control, and security for LLMs. Braintrust.ai focuses more on evaluation, tracing, and SDK-driven integrations for model evaluation and testing. Choosing between them comes down to whether you need an inline, low-friction observability and routing proxy (Helicone) or a more evaluation-focused, SDK-integrated workflow (Braintrust.ai).

A conversation between two perspectives

Proponents (Helicone):

"Helicone provides a unified view of performance, cost, and user interaction metrics across various LLM providers, empowering developers to make their LLM deployments more efficient, reliable, and cost-effective." https://docs.helicone.ai/gateway/overview?utm_source=openai
"Tusk (YC W24): Reduced latency by 37% by identifying slow model chains via session tracing." https://www.helicone.ai/blog/implementing-llm-observability-with-helicone?utm_source=openai
"Enterprise Chatbot: Cut monthly costs by 62% using response caching and rate limiting." https://docs.helicone.ai/guides/cookbooks/cost-tracking?utm_source=openai

Critics (limitations and edge cases):

"Helicone operates as a proxy between applications and model providers, meaning all LLM requests must pass through Helicone's infrastructure... Request Path Coupling." https://docs.helicone.ai/references/availability?utm_source=openai
"Helicone's platform lacks comprehensive enterprise-grade features such as audit trails, advanced role-based access controls, and sophisticated policy enforcement mechanisms." https://docs.helicone.ai/references/data-autonomy?utm_source=openai

The opposing voice (Braintrust.ai benefits and concerns)

Proponents (Braintrust.ai):

Braintrust provides robust evaluation and tracing workflows for model testing, with SDKs and guides for adding custom providers and building evaluation pipelines. https://www.braintrust.dev/docs/guides?utm_source=openai
Braintrust supports multiple providers and custom endpoints to run evaluations across OpenAI, Anthropic, Google Vertex, Amazon Bedrock, and others. https://www.braintrust.dev/docs/integrations/ai-providers/custom?utm_source=openai

Critics (integration risk and complexity):

"SDK integration risks include data privacy and compliance issues, as improper management of sensitive data can lead to violations of regulations like GDPR, HIPAA, and CCPA." https://www.braintrust.dev/docs/security?utm_source=openai
The SDK-first approach raises attack surface concerns in the Model Context Protocol era (MCP-related vulnerabilities and possible code execution paths). For examples of similar risk research see: https://arxiv.org/abs/2504.03767

Where they overlap and where they diverge

Observability vs. Evaluation: Helicone emphasizes request-level observability, real-time dashboards, cost analytics, session grouping, prompt experimentation, and run-time security (prompt injection detection). https://docs.helicone.ai/features/advanced-usage/llm-security?utm_source=openai Braintrust focuses on evaluation, test suites, and traceability for model outputs—more of a model QA and evaluation tooling layer than a request-proxy observability layer. https://www.braintrust.dev/articles/top-10-llm-observability-tools-2025?utm_source=openai
Integration style: Helicone offers proxy and async logging integrations that are low-friction to adopt and can be used as a pass-through gateway. https://docs.helicone.ai/ai-gateway/concepts/loadbalancing?utm_source=openai Braintrust uses SDKs and explicit SDK integration points, which are powerful but increase implementation complexity and risk. https://www.braintrust.dev/docs/guides/logs?utm_source=openai
Cost and routing: Helicone has cost-based routing, caching, and smart fallbacks that have demonstrable savings in production stories. https://docs.helicone.ai/guides/cookbooks/cost-tracking?utm_source=openai Braintrust's primary value is in model evaluation quality and traceability rather than runtime cost optimization.
Security & compliance: Helicone provides configurable data residency, SOC2 practices, and LLM-specific threat detection (e.g., Meta’s Llama Guard integration) but its proxy model introduces architectural coupling. https://us.helicone.ai/privacy?utm_source=openai Braintrust emphasizes security practices for its SDKs but SDKs themselves widen the attack surface and require careful engineering controls. https://www.braintrust.dev/docs/security?utm_source=openai

Excerpts that matter (direct quotes and sources)

Helicone on observability and ROI: "Helicone provides a unified view of performance, cost, and user interaction metrics across various LLM providers..." https://docs.helicone.ai/gateway/overview?utm_source=openai
Helicone on cost savings: "Cut monthly costs by 62% using response caching and rate limiting." https://docs.helicone.ai/guides/cookbooks/cost-tracking?utm_source=openai
Helicone on architecture coupling: "Helicone operates as a proxy between applications and model providers, meaning all LLM requests must pass through Helicone's infrastructure." https://docs.helicone.ai/references/availability?utm_source=openai
Braintrust on integrations: "Users can add any AI model or endpoint to their evaluation and tracing workflows by configuring a custom provider with the required parameters." https://www.braintrust.dev/docs/integrations/ai-providers/custom?utm_source=openai
Braintrust on security considerations: "Unauthorized use or misuse of AI by our employees or others may result in disclosure of confidential company and customer data..." https://www.braintrust.dev/docs/security?utm_source=openai

Trade-offs and practical guidance

If you need runtime observability, cost controls, and a low-friction gateway that sits in the request path and provides A/B prompt testing and prompt improvement features, Helicone is the sharper tool.
If your primary goal is rigorous model evaluation, building test suites, traceability of outputs across multiple model candidates, and embedding evaluation into CI/CD and SDK-driven workflows, Braintrust.ai is more aligned with that goal.
For regulated, enterprise deployments with strict audit and RBAC needs, neither product is a silver bullet today: Helicone may need additional enterprise controls, and Braintrust's SDK approach adds integration risk. Consider combining tools: use Helicone for runtime observability and cost governance and a dedicated evaluation platform (Braintrust, Arize, or internal tooling) for in-depth model QA.

Actionable next steps

Pilot Helicone in a staging environment using the async-logging or gateway mode to measure latency overhead and cost savings (use their session tracing and cost cookbooks). https://docs.helicone.ai/guides/cookbooks/cost-tracking?utm_source=openai
Run a small evaluation suite in Braintrust to validate model selection, run comparison traces, and store artifacts in CI to prevent regressions. https://www.braintrust.dev/docs/guides?utm_source=openai
Perform a security review: examine SDK risk, MCP exposure, and proxy coupling in your architecture. See Braintrust security docs and Helicone's privacy references. https://www.braintrust.dev/docs/security?utm_source=openai https://us.helicone.ai/privacy?utm_source=openai

Inline related deep-research questions

Conclusion

Both Helicone and Braintrust.ai are valuable but they solve different parts of the LLM lifecycle. Helicone is strong where runtime observability, cost control, and prompt ops matter. Braintrust.ai is strong where structured model evaluation, traceability, and SDK-driven evaluation pipelines are required. Many teams will benefit from using both in complementary roles: Helicone for production observability and routing; Braintrust.ai for pre-production evaluation and model QA.

Sources

(Selected sources embedded inline above; full source list used during research available on request.)

Explore Further