Report: Helicone vs Braintrust for AI Observability

Overview

This report compares Helicone and Braintrust as platforms for AI / LLM observability, focusing on logging, tracing, evaluations, and production monitoring for LLM-powered applications. It synthesizes vendor marketing claims and third‑party commentary, highlighting both strengths and limitations.

Quick Comparison Table

Dimension	Helicone	Braintrust
Core positioning	Open‑source AI gateway + LLM observability; proxy in front of providers	Evaluation‑first AI observability platform with deep evals + tracing
Deployment model	Cloud SaaS + open‑source; proxy endpoint (oai.helicone.ai) in request path Helicone docs	SaaS; SDK + OpenTelemetry‑based tracing; also offers an AI proxy but not required for all observability Braintrust docs
Core observability features	Request logging, latency & TTFT, cost tracking, user tracking, sessions for multi‑step workflows, alerts Helicone guide	Full LLM + tool‑call traces, production logging, custom dashboards, OpenTelemetry integration, real‑time monitoring Braintrust AI observability
Evaluation capabilities	Has prompt playground and basic eval support, but historically more focused on gateway + metrics; even Helicone authors say evals/prompt‑management were missing earlier and are being built out YC thread	Evaluation‑centric: offline & online evals, datasets + tasks + scorers, systematic experiment workflows Braintrust experiments
Data store	Uses its own observability backend; not positioned as a general AI log DB	Brainstore purpose‑built database for AI application logs and traces Braintrust homepage
Integration style	API proxy (swap `api.openai.com` for `oai.helicone.ai`), SDKs, integrations with providers like Fireworks, etc. Helicone integration	SDKs for major languages and frameworks; strong OpenTelemetry exporter support and framework integrations (LangChain, LlamaIndex, Vercel AI SDK, etc.) Braintrust OTEL
Strengths (observability)	Very low‑friction setup due to proxy; strong cost + latency + token analytics; good for multi‑provider routing and caching Helicone review	Deep tracing & evals; treats logs as first‑class data (Brainstore); designed for connecting production traces to eval datasets and CI/CD Braintrust eval practices
Weaknesses / limitations	Proxy coupling introduces a single point in request path; critics note limited enterprise features (audit trails, advanced RBAC, policy enforcement) vs heavy‑reg platforms TrueFoundry comparison; evals & prompt‑management historically less mature	More proprietary, SaaS‑only; not open‑source and some users report ergonomics/UI friction vs Langfuse in hands‑on trials MLOps systems blog; strengths skew toward evals more than pure low‑level infra metrics
Typical sweet spot	Teams wanting gateway + observability + cost control with minimal code changes; strong fit for multi‑provider LLM apps	Teams who care most about rigorous evals + real‑time tracing and tying production behavior into structured experiments

Helicone

What Helicone Offers

Helicone markets itself as an open‑source LLM observability platform and AI gateway that sits between your app and LLM providers. It acts as a proxy: you point your API calls at Helicone instead of directly at OpenAI/Anthropic/etc., and Helicone logs and augments those calls.

Key capabilities that are strongly evidenced:

Centralized logging & analytics: Helicone automatically logs prompts and responses, with a unified dashboard of performance, cost, and latency across providers. Third‑party reviews describe it as providing "comprehensive logging, cost tracking" and "rich analytics" for LLM infrastructures.TrueFoundry Helicone review Booststash review
Latency and cost monitoring: Helicone exposes latency metrics per provider and per request, including time‑to‑first‑token (TTFT), and tracks token and dollar costs in real time per user, project, or model.Helicone docs – cost tracking Softcery observability tools review
AI agent observability & sessions: It supports multi‑step workflow tracing via "Sessions" to follow complex agent interactions across multiple calls.Helicone blog – LLM observability
Gateway functionality: Smart routing, load balancing, caching, and automatic fallbacks for >100 models via one API endpoint.TrueFoundry Helicone vs Portkey
Alerts & governance basics: Custom rate‑limits per API key and alerts for cost overruns or latency spikes to avoid blow‑ups from leaked keys or misbehaving workloads.Helicone custom rate limits Platform overview

Helicone is routinely listed among top LLM observability tools in independent round‑ups, especially in the "proxy‑based monitoring" and "LLM‑specific observability" category.ZenML LLM observability landscape Patronus AI overview of observability tools

Documented Limitations & Trade‑offs

The claim that Helicone delivers comprehensive AI observability is somewhat tempered by the following limitations:

Proxy coupling / single choke point
A Braintrust‑authored comparison of Helicone vs Braintrust points out a structural downside: because Helicone is deployed as a proxy in the request path, any Helicone outage or network issue can break your LLM traffic even when the underlying provider (OpenAI, Anthropic, etc.) is fine.Braintrust Helicone vs Braintrust
This is an architectural trade‑off: in exchange for "drop‑in" visibility you accept tight coupling to Helicone’s availability.
Enterprise governance features are thinner
A TrueFoundry comparison notes that Helicone lacks comprehensive audit trails, advanced RBAC, and sophisticated policy enforcement that regulated industries often need.TrueFoundry Helicone vs Portkey
That doesn’t mean there is no access control or logs, but it suggests that for strict compliance (financial, healthcare, etc.) Helicone may require additional tooling or custom work.
Historically weaker on evals and prompt management
In discussions around Helicone’s roadmap, Helicone’s own team has acknowledged that earlier versions were missing key pieces for an "iterative improvement loop"—prompt management, evaluations, and experimentation—and that they were actively building them out.YC discussion citing missing eval/prompt tooling
Recent blogs show active work on prompt evaluation frameworks and prompt management, but the vendor’s own narrative and independent surveys still tend to place Helicone primarily in the observability & cost‑tracking bucket rather than a fully‑fledged eval lab.
Not a drop‑in for infra‑level metrics
Helicone focuses on LLM‑level telemetry (prompts, responses, tokens, cost, latency) and agent workflows. If you need lower‑level infrastructure metrics (GPU, node‑level CPU, network, etc.), you’ll still need something like Datadog, Arize, or GraphSignal; Helicone isn’t trying to replace those.

Where Helicone Fits Best

Helicone is a strong fit when you:

Want minimal code changes: swapping API endpoints to gain logging, cost tracking, and basic observability is very fast.
Need multi‑provider routing and caching plus observability in one package.
Care disproportionately about cost visibility and quick debugging of prompts and agents rather than building elaborate evaluation pipelines.

It is less ideal if you:

Have stringent enterprise compliance needs around fine‑grained RBAC, audit trails, and policy enforcement.
Want a platform where evaluation and experiment workflows are the first‑class center of gravity, rather than a gateway with added evals.

Braintrust

What Braintrust Offers

Braintrust positions itself as an AI evaluation and observability platform for building reliable AI applications. It is especially strong around evals and connecting production traces to systematic experiments.

Key evidenced capabilities:

Evaluation‑first architecture
Braintrust’s documentation and marketing are explicit: evals are built on the triplet of dataset, task, and scorers—its core abstraction for testing and improving LLM apps.Braintrust experiments docs Braintrust homepage
It supports both offline evals (structured experiments over datasets) and online evals tied to production traffic.
Deep tracing and logging with Brainstore
Brainstore is described as a database "designed specifically for AI application logs and traces," with traditional databases framed as insufficient for the complexity of AI workflows.Braintrust homepage
Braintrust can stream detailed logs for every LLM call and tool invocation, plus user ratings (thumbs‑up/down) tied directly to traces.Trace‑driven insights blog
Real‑time monitoring and custom dashboards
Comparisons vs LangSmith and others emphasize Braintrust’s real‑time production monitoring, custom dashboards, and the ability to surface issues before users see them.PromptLayer Braintrust vs Langsmith Galileo vs Braintrust
OpenTelemetry‑centric integrations
Braintrust invests heavily in OpenTelemetry (OTel) support, with native exporters, automatic LLM tracing, and span conversion.Braintrust OTEL docs LLM evaluation tools integrations article
It is integrated with major AI frameworks and SDKs (LangChain, LlamaIndex, Vercel AI SDK, etc.), making it suitable when you want observability that meshes with broader tracing infrastructure.
End‑to‑end reliability workflows
Braintrust emphasizes tying logs and traces into systematic eval sets and CI/CD—e.g., pulling low‑scoring traces back into new datasets, running evals per code/prompt change, and avoiding regressions.Best practices for AI evals
Case studies (e.g., Graphite’s Diamond code reviewer) describe using Braintrust to keep hallucinations low and feedback actionable.Braintrust customer stories

Evidence of Limitations and Trade‑offs

Proprietary SaaS, not open‑source
Braintrust is a closed‑source, SaaS‑first platform. An Arize Phoenix FAQ explicitly positions Phoenix as an open‑source alternative to proprietary platforms like Braintrust, and notes that Braintrust can hit roadblocks when teams require self‑hosting or open code for compliance or customization.Arize Phoenix FAQ – Braintrust comparison
Ergonomics and developer experience vs Langfuse
A hands‑on blog comparing instrumentation with Braintrust and Langfuse (for an agentic app with litellm) concludes that Braintrust "ended up not being as ergonomic" and the author switched to Langfuse mid‑way, citing friction in setup and usage.MLOps systems blog on Braintrust
Evaluation‑first bias vs low‑level observability
Several independent comparisons frame Braintrust as primarily an evaluation‑centric tool that also does observability, rather than a full replacement for infra‑level observability suites.Comet LLM observability tools overview Snippets.ai comparison
This is not a criticism per se, but for workloads that need GPU‑level metrics, cluster health, or generic APM‑style data, Braintrust still needs to sit alongside traditional observability stacks.
Some UI responsiveness issues reported
An Arize‑authored comparison mentions that some users report UI responsiveness issues in Braintrust during heavy debugging/testing, which can add friction when working with large datasets or complex traces.Arize Phoenix vs Braintrust
Proxy usage can introduce small latency
At least one independent comparison notes that when using Braintrust’s proxy, it "introduces a touch of latency" even if it’s generally acceptable.Future AGI vs Braintrust

Where Braintrust Fits Best

Braintrust tends to be the better fit when you:

Want rigorous, systematic evals with clear abstractions (datasets, tasks, scorers) and strong offline + online evaluation workflows.
Need fine‑grained tracing of LLM calls and tools that naturally connects into OTel and existing tracing/monitoring infrastructure.
Care about making evals part of your CI/CD—catching regressions automatically on each prompt/model change.

It is less ideal if you:

Require open‑source, self‑hostable solutions for compliance or cost reasons.
Primarily want a simple gateway with cost/latency monitoring and caching rather than a full Evals lab.

Head‑to‑Head: Observability Aspects

Logging & Tracing

Helicone gives you logging by sitting in the request path; logs all requests/responses, token usage, latency, errors, and user IDs. This is convenient but introduces a dependency on Helicone’s availability and network path.Helicone architecture Braintrust critique of proxy coupling
Braintrust logs via SDKs and OTel, capturing per‑request traces of LLM calls and tools, often without forcing all traffic through a proprietary proxy (though it offers a proxy too). This is more in line with modern distributed tracing patterns, especially if you already invest in OTel.Braintrust OTEL docs

Implication:

Pick Helicone if you want "logging by default" with minimum code changes and are comfortable with a proxy in front of your LLM APIs.
Pick Braintrust if you want observability integrated with your wider tracing stack and can afford to instrument via SDKs and OTel.

Cost, Latency, and Usage Monitoring

Helicone is particularly strong on cost tracking and rate‑limiting; it markets cost tracking as a core feature, with detailed per‑user/per‑model analytics and automatic alerts for cost overruns and custom rate limits.Helicone cost tracking docs Prompts.ai cost‑management guide
Braintrust also tracks latency and performance, but the most emphasized metrics in their content are eval scores and quality metrics, not just raw cost. That said, Braintrust is listed alongside Helicone in articles covering top observability platforms that combine latency, cost, and quality metrics.GetMaxim observability overview

If your central pain is "I’m blind on spend and latency", Helicone’s gateway‑driven cost tooling is a strong match. If your central pain is "I don’t know if my changes are actually better", Braintrust’s eval‑centric metrics are more valuable.

Evaluation & Experimentation

Helicone:
- Provides a playground, prompt management, and is adding evaluation features, but even Helicone‑authored and third‑party material historically categorize it with "observability‑centric" tools focused more on metrics/tracing than on structured eval suites.Comet LLM observability tools overview
- Recent blogs from Helicone discuss evaluation frameworks and prompt evaluation, indicating active investment, but there is less third‑party depth describing mature eval workflows vs Braintrust.
Braintrust:
- Explicitly designed around evals: datasets + tasks + scorers; offline and online; and strong CI/CD integration.Braintrust experiments docs
- Independent articles and case studies consistently highlight Braintrust in the context of evaluation and reliability engineering, not just logging.Your guide to LLM evaluation tools

Implication: For AI observability as "quality measurement", Braintrust is clearly more advanced today.

Compliance Considerations

Your organization’s stated requirement list includes an item labeled "Hi" as a required compliance standard. There is no recognized industry security or compliance framework widely known under that exact name, and neither Helicone nor Braintrust publicly claim adherence to a standard called "Hi". Public documentation does reference other security or compliance practices (e.g., SOC 2, GDPR, etc., particularly around Braintrust’s recruiting product), but nothing that can be interpreted as satisfying a standard literally named "Hi".

Given the instructions to flag any non‑compliance:

⚠️ Compliance Alert: Helicone does not demonstrably meet the following requirement:

"Hi" (no publicly documented standard or certification by this name; compliance cannot be confirmed)

⚠️ Compliance Alert: Braintrust does not demonstrably meet the following requirement:

"Hi" (no publicly documented standard or certification by this name; compliance cannot be confirmed)

If "Hi" is a placeholder or internal shorthand for a real framework (for example, HIPAA, HITRUST, or a custom internal policy), you should treat both vendors as non‑compliant by default until they provide explicit documentation that they meet that standard.

Practical Guidance

Given the above, a pragmatic way to choose:

Choose Helicone if:
- You want gateway + observability + cost control with minimal changes to your existing code.
- Your workloads are primarily about API call monitoring, cost visibility, and debugging prompts/agents, not yet heavy on formal eval pipelines.
Choose Braintrust if:
- Your priority is systematic evals, trace‑driven experimentation, and CI/CD integration.
- You’re already using OpenTelemetry or want observability integrated into a broader tracing stack.

In large organizations, a common pattern is to pair a gateway‑style tool like Helicone (or a cloud provider’s API gateway with cost controls) with a dedicated eval/observability platform like Braintrust, Langfuse, or Arize. That hybrid approach can give you strong cost governance plus rigorous quality evaluation—provided you can satisfy your internal compliance requirements with vendor contracts, DPAs, and security reviews.