Helicone vs Braintrust: A Practical Comparison for Engineering Teams in 2026

You shipped an LLM application, and production behavior is now harder to see. Helicone and Braintrust both address the visibility gap, though they do so differently. Helicone lets you quickly set up request logging, while Braintrust helps measure and improve output quality run by run.

Pick the wrong platform and the cost compounds. A team choosing Braintrust for basic request logging may end up with more instrumentation than needed. A team choosing Helicone when prompt regressions are the real problem may hit a ceiling quickly.

The decision also changed in early 2026. Helicone was acquired and moved into maintenance mode. Braintrust raised fresh funding to expand. This Helicone vs Braintrust comparison reviews architecture, pricing, evaluation depth, and enterprise fit while staying honest about where production AI governance still begins.

Helicone vs Braintrust: What Each Platform Is Built For

The primary design intent of each platform is the most useful input to this decision. It shapes the architecture, pricing, setup, deployment, and operating tradeoffs that follow.

Helicone is built for observability. It gives teams logging, tracing, analytics, and cost tracking across AI applications with a fast simple URL change. Braintrust is built for LLM evaluation, where teams measure LLM output quality, run evals, and improve behavior over time.

So the question is not which tool is better in the abstract. The better question is which problem blocks your team today. Are you looking for quick production observability, or do you need structured prompt testing and regression checks?

Track record matters too. Helicone served more than 16,000 organizations and processed more than 14.2 trillion tokens across three years, according to its materials. It also built a Rust-based AI gateway alongside its observability product.

Braintrust counts Notion, Stripe, Vercel, Ramp, and Dropbox among its users. It raised an $80 million Series B in February 2026, led by ICONIQ. That funding supports Braintrust’s push into AI observability, evaluation workflows, and go-to-market expansion.

One detail changes the calculation for multi-year adoption. Mintlify acquired Helicone on March 3, 2026. Helicone’s services remain live in maintenance mode, with security patches, bug fixes, performance fixes, and new model support continuing.

Braintrust is moving in the opposite direction. It is funding engineering expansion and deeper product growth. If your team is choosing a long-term dependency, that difference belongs inside the evaluation, not in a footnote.

Helicone Logs Requests and Braintrust Evaluates Them, TrueFoundry Governs Both

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Helicone vs Braintrust: Architectural Differences

The design intent appears first in architecture. Architecture determines where trace data resides, how requests flow, and whether code changes affect the critical path. These details shape the tradeoffs teams live with after launch.

Helicone: Proxy-Based Architecture With One-Line Setup

Helicone is one of the fastest ways to start logging LLM observability data. Change one line of code, point your API base URL at the Helicone proxy, and traces begin flowing quickly. That proxy approach is the reason adoption feels simple.

The model works with any provider that accepts HTTP requests. Teams do not need a new SDK, deep refactoring, or a custom exporter. The tradeoff is structural because every request now flows through Helicone’s infrastructure.

If Helicone experiences downtime or a network issue, calls may fail even when OpenAI or Anthropic is healthy. Self-hosting reduces that exposure, although it moves operations to your team. Helicone reports sub-millisecond proxy overhead in self-hosted mode.

The proxy is now in maintenance mode too. A request-path proxy is harder to own when active feature development has stopped. Helicone’s tracing remains easy to read, although deep agent observability can look flatter across multi-step flows. 

Braintrust: SDK-Based Architecture With Deeper Tracing

Braintrust takes the opposite route. Teams instrument the application with its SDK, available for Python, TypeScript, and Ruby. This architecture shifts more work off the critical path and reduces the risk of user-facing latency.

Logging runs in a background thread. Traces are batched and flushed asynchronously. If the SDK cannot reach Braintrust due to a network issue, the application continues to run.

The payoff is depth. A Braintrust trace is a directed acyclic graph of typed spans, including LLM, tool, score, task, and review. That makes a single AI agent execution easier to inspect as a decision path.

Depth requires more upfront effort. Enterprise teams need more instrumentation knowledge before data becomes useful. The deeper the evaluation workflow, the more structured implementation work teams need to see value.

Helicone vs Braintrust: Feature Comparison

The two products overlap less than their category labels suggest. Helicone leans toward an LLM observability platform, while Braintrust leans toward evaluation. A side-by-side read shows where Helicone and Braintrust actually focus.

The pattern becomes clear once listed. Helicone optimizes for broad LLM observability at the lowest setup cost. It also includes comprehensive features such as caching, failover, and rate limiting that pure observability tools often lack. For teams evaluating routing, caching, failover, and provider abstraction together, this also overlaps with how an LLM gateway works in production. 

Braintrust optimizes for evaluation depth. It supports scorers, dataset workflows, CI checks, and structured experimentation around prompt quality. Governance remains thin on both platforms, which is the gap the later sections address.

Helicone vs Braintrust: What Each Platform Actually Costs

Pricing diverges as sharply as architecture. The two models reward different usage patterns, team needs, and levels of governance. This matters because the cheapest option today may no longer be the cheapest once traffic grows.

Helicone Pricing

Helicone’s Hobby plan is a free tier that includes 10,000 requests per month, 1 seat, and 7-day data retention. The Pro plan runs $79 per month and adds unlimited seats, reports, alerts, the HQL query language, and one-month retention.

The Team plan is $799 per month. It supports multiple organizations and includes SOC 2 and HIPAA compliance. Enterprise pricing is custom and covers on-prem deployment, SAML SSO, and larger commercial needs.

Helicone’s pricing scales with request volume. The model is transparent, which engineers appreciate. Yet it also means your observability bill grows with traffic, even when the value you extract remains stable.

A busy agent pipeline can burn through a tier quickly. Teams should model API costs, retention, and traffic growth before choosing Helicone for the entire organization. Cost visibility is useful, although it does not replace budget enforcement.

Braintrust

Braintrust takes a volume-independent posture at the entry point. The Starter plan includes 1 GB of processed data, 10,000 scores, 14-day retention, and unlimited users, projects, playgrounds, and experiments.

Pro costs $249 per month. It raises the limits to 5 GB of processed data, 50,000 scores, and 30-day retention. Usage beyond those caps is billed based on processed data and score overages rather than an immediate cutoff.

Enterprise is custom and adds custom RBAC, retention, export, BAA, and hybrid or on-prem deployment. Braintrust costs more than Helicone at comparable team sizes. The premium makes sense when prompt regressions create meaningful product risk.

The right choice depends on the actual bottleneck. Helicone is often the lower-cost path for request visibility. Braintrust is the best fit when prompt management, evals, and quality control drive engineering priorities. Teams should also review gateway cost planning when comparing observability spend with production governance costs. 

Helicone vs Braintrust: Which Platform Should You Choose

With architecture and pricing on the table, the choice usually comes down to two questions. What is blocking your team today, and how large is the team that must operate the tool?

Choose Helicone if you need fast setup, cost tracking, and basic visibility into requests. For a team of one to three engineers without systematic evaluation pipelines, Helicone meets the need at lower cost and lower integration effort.

The caveat carries weight in 2026. The product is in maintenance mode, so treat it as a solution for today’s visibility needs. If you adopt it, self-hosting may reduce request-path dependency.

Choose Braintrust if evaluation quality is the bottleneck and your team can invest in deeper instrumentation. When prompt regressions reach production, the eval framework, span-level traces, and CI gating can earn their cost.

Pair Braintrust with separate production monitoring for broad real-time request analytics. Braintrust focuses on evaluation first, not broad request monitoring. Teams comparing Braintrust vs Helicone should separate LLM observability from evaluation maturity before deciding.

What Neither Platform Covers for Enterprise Teams

The Helicone vs Braintrust comparison highlights a shared gap separate from the choice between them. Both are observability platforms and evaluation tools. Neither is an inference governance platform.

For teams with strict compliance needs, this distinction matters. Fine-grained RBAC, audit trails, policy enforcement, and budget gates must operate before the call reaches a model. Observability after inference cannot block an unauthorized request.

Specifically, neither platform provides:

• Access controls before inference: Both platforms observe what a model call produces after it happens. Neither sits in the request path to stop an unauthorized request before inference starts.
• VPC-native governance on every paid tier: Helicone offers self-hosting via its open-source Apache 2.0 codebase, which is now in maintenance mode. Braintrust requires Enterprise hybrid deployment for VPC data-plane needs.
• Hard budget enforcement: Both tools surface cost data after the fact. Neither prevents a runaway AI agent or team workflow from exceeding spend before the bill appears.
• MCP tool connection governance: Neither platform governs each tool call that agents open through MCP servers. That is where a growing share of agent risk now resides.

These gaps do not make either product weak. They define where each product stops. Teams needing request-path controls should evaluate a governed AI gateway alongside their observability or evaluation tool.

Helicone and Braintrust Both Observe AI, TrueFoundry Governs It Before It Runs

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Where TrueFoundry Fits Alongside or Instead of Helicone and Braintrust

TrueFoundry operates at a different layer than either tool. Helicone and Braintrust help teams understand model behavior after or around inference. TrueFoundry governs access, spend, routing, and compliance before inference executes.

That layering means teams can run TrueFoundry with whichever platform they choose. Helicone can support broad request visibility. Braintrust can support deeper evaluation. TrueFoundry can own policy enforcement on the request path.

This matters when AI traffic becomes production traffic. Teams need to decide who can call which model, which data can move, and which budget applies. They also need logs tied to real users and workloads.

TrueFoundry is most useful when teams need:

• Pre-inference governance: Enforce access and policy before a request reaches the model.
• Hard budget controls: Stop teams or agents before spend exceeds approved limits.
• Private deployment: Keep prompts, outputs, logs, and metadata inside controlled environments.
• Audit-ready records: Tie model calls to user identity, cost, model, and policy outcome.
• Agent workflow control: Govern multi-step agents before loops or tools create risk.

For teams running agentic workloads, TrueFoundry’s agent governance layer adds runtime controls, workflow limits, and audit trails. This helps stop a runaway loop before it becomes a cost or security incident.

TrueFoundry can also serve as a standalone layer for teams whose main need is inference governance. Built-in tracing captures request-level logs with user identity, model attribution, and cost metadata. Those records can remain inside the customer’s own cloud boundary.

If your team needs visibility only for requests, Helicone may fit. If systematic evaluation is the bottleneck, Braintrust may fit. If governance must happen before inference, TrueFoundry covers the layer both tools leave open.

Book a demo to see TrueFoundry govern inference, budgets, access, and audit logs securely.