Helicone alternative: Why Braintrust is the best pick

When choosing an LLM observability platform, the architectural approach matters as much as the features. As teams evaluate Helicone alternatives, understanding how different tools handle observability becomes critical to long-term success.

About Braintrust

Braintrust is the AI observability platform. By connecting evals and observability in one workflow, Braintrust gives builders the visibility to understand how AI behaves in production and the tools to improve it.

Braintrust approaches evals from a product engineering perspective, not a research perspective. The platform treats evaluation as a continuous, integrated part of the development workflow rather than a separate manual step. Production traces become test cases with one click. Evals run on every pull request automatically. Product managers and engineers work in the same environment without handoffs. This approach has helped companies like Notion achieve 10x productivity gains, Zapier improve from sub-50% to 90%+ accuracy, and Coursera maintain 90% satisfaction at 45x scale.

The platform provides comprehensive observability through SDK-based tracing that captures your entire application flow, not just LLM calls. An optional AI proxy offers caching and unified access to multiple model providers when you need it. Evaluation infrastructure, CI/CD integration, and collaborative workspaces complete the system. Everything works together to help teams ship quality improvements faster.

About Helicone

Helicone is an open-source LLM observability platform that provides visibility into your LLM calls through a proxy-based architecture. The platform logs requests and responses as they pass through Helicone's infrastructure, giving you basic insights into API usage, costs, and latency. Helicone works well for teams in early experimentation who need simple visibility into their LLM calls without complex instrumentation.

Key architectural differences

Both platforms offer proxies, but the fundamental architecture differs in how observability works.

Helicone: Proxy-required architecture

Helicone's proxy sits between your application and model providers. To get observability, all LLM requests must flow through Helicone's infrastructure. This creates several architectural dependencies:

Request path coupling: The proxy handles every LLM call. If Helicone experiences downtime or network issues, your LLM calls fail even if OpenAI or Anthropic remain operational. Your application's reliability depends on Helicone's infrastructure.

Added latency: Network round-trip to the proxy adds milliseconds or more to every LLM request. For latency-sensitive applications serving real-time user interactions, this compounds across multiple calls and degrades user experience.

Limited visibility scope: You only see what passes through the proxy. Retrieval steps, tool calls, business logic, and application context remain invisible. For RAG or agentic applications that involve multi-step workflows, this captures only a fraction of what matters for debugging and optimization.

Volume-based constraints: Free tier covers 10,000 requests per month. Scaling costs grow linearly with request volume rather than value delivered.

Vendor lock-in risk: Switching away from Helicone means re-architecting how observability works in your application since the proxy sits in your critical request path.

Braintrust: SDK-based observability with optional proxy

Braintrust separates observability from request routing. Trace data is logged asynchronously within your application and sent outside the request path.

Independent observability: SDK integration captures trace data asynchronously with zero impact on request latency. Your application continues serving users even if Braintrust were temporarily unavailable. Reliability stays in your control.

Comprehensive visibility: Instrument your entire application stack including LLM calls, vector searches, tool invocations, retrieval pipelines, and business logic. See exactly what context went into each LLM call, which tools the agent selected, and how your application processed results.

Optional AI proxy: Braintrust provides an AI proxy for caching and unified access to multiple model providers (OpenAI, Anthropic, Google, etc.). The proxy serves a different purpose than observability. Use it when you want caching benefits or simplified credential management. Observability works independently through the SDK whether you use the proxy or not.

Flexible deployment: Use SDK tracing alone for comprehensive observability. Use the proxy alone for model access features. Combine both to get request caching while SDK tracing captures full application context. The architecture adapts to your needs rather than forcing a single approach.

Zero performance impact: Asynchronous logging means your request latency stays unchanged. Traces are batched and sent outside the critical path. Users never experience slowdowns from observability.

Volume-independent pricing: Free tier includes 1 million spans per month. A span represents any traced operation in your application. For context, a single RAG query might generate 5-10 spans (embed query, search vectors, rerank, format context, LLM call, etc.). The free tier accommodates 100,000-200,000 actual user interactions.

From observation to systematic improvement

Understanding the difference between these Helicone alternatives requires looking beyond observability features to how they help teams improve AI quality.

Where Helicone stops at showing you what happened, Braintrust closes the feedback loop from production traces to evaluation to deployment. This approach represents a fundamental shift in how teams develop AI products.

Production traces as test cases

When you spot a failure in production, click to add it to a dataset. That trace becomes a test case in your next eval run. Fixed the issue? The eval proves it. Introduced a regression? The eval catches it before deployment. This direct connection from production to evaluation eliminates manual data wrangling and ensures your test cases reflect real user scenarios.

Traditional approaches require engineers to manually export traces, transform data formats, write evaluation scripts, and cobble together testing infrastructure. Braintrust makes this instant.

Integrated evaluation infrastructure

Braintrust provides complete evaluation capabilities built into the platform:

Code-based scorers: Write custom evaluation logic in Python or TypeScript that runs at scale. Test exact matching, semantic similarity, business rule compliance, or any custom metrics your application requires.

LLM-as-a-judge: Use AI models to evaluate subjective criteria like helpfulness, relevance, or tone. Braintrust provides battle-tested prompts and handles model coordination.

Human annotation: Built-in workflows for expert review when automated scoring isn't sufficient. Collect labeled data to improve your evals over time.

Agent simulation: Evaluate complete multi-step agent workflows end-to-end. Test how agents handle tool selection, error recovery, and goal completion across complex scenarios.

Dataset management: Version control for evaluation datasets. Track which test cases came from production versus synthetic generation. Organize cases by failure type, user segment, or feature area.

Helicone provides custom scores via API but no evaluation infrastructure. You build everything yourself.

CI/CD integration that actually works

Braintrust provides a turnkey GitHub Action that runs evals on every pull request. Results appear as comments showing exactly which test cases improved, which regressed, and aggregate score changes. Set quality gates that block merges if performance drops below thresholds.

Configure once in a few lines of YAML:

- uses: braintrustdata/eval-github-action@v1
  with:
    api-key: ${{ secrets.BRAINTRUST_API_KEY }}

Every code change thereafter includes eval results. Engineers see quality impact before requesting review. Reviewers see objective metrics alongside code changes. Product quality improves because regressions get caught automatically rather than discovered by users.

No custom infrastructure to build or maintain. No scripts to write. No deployment orchestration. Just quality gates that work.

Unified workspace for cross-functional teams

Product managers iterate in the Playground with live eval results. Compare prompt variations side-by-side against real test cases. Test different models, temperatures, or system instructions. See evaluation scores update in real-time as you adjust parameters.

When a PM finds a winning prompt configuration, engineers pull that exact setup into code through the SDK. No translation between tools. No handoff delays. No requirements documents that drift from implementation.

Engineers and PMs work in the same environment, see the same data, and discuss the same metrics. This eliminates the translation layer that slows down most AI teams and creates the disconnect between what PMs want and what engineers build.

Production-scale performance

Braintrust handles millions of traces with sub-second query performance. The platform uses a custom time-series database optimized for trace data. Filter, aggregate, and analyze production traffic without waiting. Debug issues while they matter rather than after users have moved on.

Large teams at scale need this performance. When you're processing thousands of requests per second, slow queries make observability unusable for real-time debugging.

Proven results at leading companies

Notion went from 3 issues fixed per day to 30 through a 10x productivity improvement by building evals into their workflow with Braintrust. Engineers catch regressions before code review instead of discovering issues in production.

Zapier improved AI features from sub-50% accuracy to 90%+ within 2-3 months using Braintrust's complete feedback loop. The team iterates faster because evals provide objective quality metrics on every change.

Coursera maintains a 90% learner satisfaction rating with their AI Coach while processing 45x more interactions than manual processes could handle. Systematic evaluation ensures quality scales with volume.

These results come from connecting observability to improvement through integrated evals, CI/CD, and collaborative workflows. Observability platforms that stop at data collection don't deliver these outcomes.

Detailed feature comparison

What you give up with Helicone

When evaluating alternatives to Helicone, understanding the trade-offs helps determine if its approach fits your use case.

Limited visibility into application context

Proxy-based logging only sees what flows through the proxy. Modern AI applications involve more than LLM calls. RAG pipelines retrieve documents. Agents select tools. Business logic constructs prompts. Helicone sees the final LLM request. You don't see the retrieval that produced bad context, the tool that returned wrong data, or the logic that formatted the prompt incorrectly.

This becomes painful when debugging production issues. You know the LLM gave a bad answer but can't trace back through the steps that created the bad input. With SDK-based tracing, you instrument everything and see the complete picture.

No path from observability to improvement

Helicone provides custom scores via API but no evaluation infrastructure. You write code to push scores, build your own infrastructure to run evaluations, and create your own CI/CD integration. You get no built-in LLM-as-a-judge scorers, no human annotation workflows, no GitHub Action, and no quality gates.

Observability exists in isolation from improvement. When you find issues in production, there's no systematic way to ensure you fix them and prevent regressions. Teams waste weeks building evaluation infrastructure that Braintrust provides out of the box.

Manual workflow for quality improvements

Engineers must manually extract logs, write evaluation scripts, and deploy changes without confidence in quality impact. PMs have no workspace to test ideas against real data. They prototype in isolation, then hand requirements to engineering who rebuild from scratch.

The feedback loop from observation to improvement remains manual. Changes that should take minutes require days of coordination. Quality improvements slow to a crawl because the tools don't support rapid iteration.

Infrastructure dependency in your critical path

Every LLM call depends on the proxy staying available. Network issues, DNS problems, or outages on Helicone's side affect your users even when model providers are operational. This differs from SDK-based tracing, which logs asynchronously outside your request path. Your application continues working if the observability platform has issues.

As a Helicone competitor, Braintrust addresses these architectural limitations through a fundamentally different approach. The SDK architecture eliminates infrastructure dependencies while providing deeper visibility.

Scaling costs tied to volume, not value

Helicone's pricing scales with LLM request volume. As your application grows, costs increase regardless of the value you're getting from observability. Free tier covers only 10,000 requests per month, which many production applications exceed quickly.

Teams end up paying for basic logging rather than investing in capabilities that improve AI quality. When you're spending budget on observability infrastructure, you want features that drive systematic improvements, not just data storage.

Open source benefits with closed development limitations

While Helicone's open source model provides transparency, it also means the product development roadmap depends on community contributions rather than dedicated product investment. Features like evaluation infrastructure, CI/CD integration, and collaborative workflows require significant engineering effort that open source projects often struggle to deliver.

Teams that choose open source Helicone alternatives typically do so for transparency and self-hosting, but then face the trade-off of building critical evaluation capabilities themselves.

Braintrust: Pros and Cons

Pros

Complete feedback loop: Production traces become eval cases instantly. Evals run on every PR automatically. Quality improvements ship with confidence because you have objective metrics proving changes work.

Zero infrastructure impact: SDK-based tracing logs asynchronously with zero request latency. Your application's performance stays unchanged. Reliability remains in your control since observability lives outside the critical path.

Comprehensive visibility: See your entire application flow including LLM calls, vector searches, retrieval pipelines, tool calls, and business logic. Debug issues by tracing back through the complete context that led to each LLM request.

Turnkey CI/CD integration: GitHub Action works out of the box with a few lines of YAML. Eval results appear on every pull request showing quality impact. Set gates that block regressions from merging.

Unified PM/engineer workspace: Product managers iterate in the Playground with live evaluation results. Engineers pull winning prompts directly into code. No handoffs or translation between tools. Faster iteration cycles and better collaboration.

Production-ready evaluation: Code-based scorers, LLM-as-a-judge, human annotation, and agent simulation built into the platform. No custom infrastructure to build. Start evaluating immediately rather than spending weeks on tooling.

AI proxy for multi-model access: Optional proxy provides caching and unified access to OpenAI, Anthropic, Google, and other providers through one OpenAI-compatible API. Switch models without rewriting integration code.

Performance at scale: Sub-second queries on millions of traces. Debug production issues in real-time rather than waiting for slow queries. Custom time-series database optimized for trace data.

Proven results: Companies like Notion, Zapier, and Coursera achieve measurable quality gains through systematic evaluation workflows. 10x productivity improvements and accuracy gains from sub-50% to 90%+ in months.

Volume-independent pricing: Free tier includes 1 million spans per month, accommodating 100,000-200,000 user interactions for typical applications. Growth costs scale with value rather than raw request volume.

Dataset management: Version control for evaluation datasets. Track test case origin (production vs. synthetic). Organize by failure type or feature area. Maintain test quality as your application evolves.

Flexible architecture: Use SDK alone, proxy alone, or both together. Architecture adapts to your needs rather than forcing a single approach. Start with observability and add evaluation when ready.

Expert support: Responsive team that helps with evaluation strategy, not just technical support. Founded by engineers who built large-scale ML systems and understand production AI challenges.

Cons

Proprietary platform: Not open source, which matters for teams that require code transparency or have strict open source requirements for vendor tools.

Self-hosting limited to enterprise: Hybrid deployment available only for enterprise customers. Teams that need full self-hosting without enterprise contracts should consider alternatives.

SDK integration required: Getting full value requires instrumenting your application with Braintrust SDKs. This takes more setup than proxy-based approaches, though the benefits of comprehensive visibility and zero latency impact justify the effort.

Higher price point: Pro tier at $249/month costs more than some alternatives. The price includes evaluation infrastructure, CI/CD integration, and collaborative features that you'd otherwise build yourself, but teams only needing basic observability may find it expensive.

Helicone: Pros and Cons

Pros

Quick setup: Routing traffic through the proxy requires minimal code changes. Add Helicone's endpoint and API key, and logging starts immediately.

Open source: MIT license provides code transparency and ability to self-host with full control over infrastructure.

Simple visibility: Basic LLM call logging works well for early experimentation when you need to see requests, responses, and costs without complex instrumentation.

Cons

Proxy required for all observability: Cannot get any visibility without routing traffic through Helicone's infrastructure. Observability and request routing are coupled rather than independent.

Infrastructure dependency: Helicone downtime or network issues directly impact your application. Users experience failures even when model providers remain operational.

Added request latency: Network round-trip to proxy adds milliseconds or more to every LLM call. Compounds across multiple requests in latency-sensitive applications.

Limited visibility scope: Only sees LLM requests and responses. Misses retrieval steps, tool calls, business logic, and application context that matter for debugging complex workflows.

No evaluation infrastructure: Provides API for custom scores but no built-in evaluation capabilities. Build scorers, infrastructure, CI/CD integration, and quality gates yourself.

Manual improvement workflow: Finding production issues requires manual export, custom evaluation scripts, and separate tooling. No systematic path from observation to quality improvement.

Volume-based free tier: 10,000 requests per month free tier gets exceeded quickly in production applications. Costs scale with request volume rather than value delivered.

Session grouping only: Cannot trace multi-step agent workflows or RAG pipelines end-to-end. Visibility limited to individual LLM calls without application context.

No PM/engineer collaboration: Developer-focused platform provides no workspace for product managers to iterate on prompts with evaluation data. Cross-functional collaboration requires separate tools and handoffs.

No CI/CD integration: Build your own GitHub Action, evaluation orchestration, and quality gates. No turnkey solution for catching regressions before code merges.

Vendor lock-in risk: Switching away requires re-architecting observability since proxy sits in critical request path. Migration path more complex than with SDK-based approaches.

Open source development pace: Product roadmap depends on community contributions rather than dedicated investment. Advanced features like agent evaluation and collaborative workflows unlikely to appear.

No agent simulation: Cannot evaluate multi-step agent workflows end-to-end. Testing agent behavior requires custom tooling beyond what Helicone provides.

Limited prompt management: Basic prompt versioning without production-ready deployment features or collaboration workflows that larger teams need.

Performance at scale not documented: Unclear how the platform handles millions of traces. No published benchmarks for query performance on large datasets.

Pricing comparison

Helicone pricing

Free tier: 10,000 LLM requests per month Paid tier: Custom pricing based on volume

The free tier works for early experimentation but production applications often exceed 10,000 requests quickly. Paid pricing scales with request volume. For a RAG application making multiple LLM calls per user interaction, costs accumulate rapidly.

Braintrust pricing

Free tier: 1 million spans per month Pro tier: $249/month

A span represents any traced operation in your application (LLM call, vector search, tool execution, custom logic, etc.). For context, a single RAG query might generate 5-10 spans (embed query, search vectors, rerank, format context, LLM call, etc.). The free tier accommodates approximately 100,000-200,000 actual user interactions depending on application complexity.

Pro tier includes:

• Comprehensive observability with unlimited spans
• Full evaluation infrastructure (code-based scorers, LLM-as-a-judge, human annotation)
• Turnkey GitHub Action for CI/CD integration
• AI proxy with caching and multi-model access
• Collaborative workspace for PM/engineer iteration
• Agent simulation and workflow testing
• Dataset management and versioning

When evaluating cost, factor in engineering time. With Helicone, you build evaluation infrastructure, CI/CD integration, and collaborative workflows yourself. Most teams spend weeks on capabilities that Braintrust includes. The Pro tier price reflects a complete platform rather than just observability logs.

Making the decision

The choice between these Helicone alternatives depends on where you are in your AI journey and what you need from your observability platform.

Choose Helicone if:

You're experimenting with LLMs and need basic visibility into API calls. Your application is simple enough that LLM requests tell the full story. You're comfortable accepting proxy latency and infrastructure dependency. Open source access matters more than evaluation capabilities. Your volume stays under 10,000 requests per month. You have engineering resources to build evaluation infrastructure and CI/CD integration yourself.

Choose Braintrust if:

You're building production AI products where quality matters. Your application involves RAG pipelines, agents, or multi-step workflows requiring comprehensive tracing. You need to prove changes improve quality before they reach users. PMs and engineers need to collaborate without tool handoffs. You want to avoid infrastructure dependencies in your critical path. You need evaluation capabilities built into the platform rather than building them yourself. Sub-second performance on millions of traces is important. You want to ship quality improvements in minutes rather than days.

Most teams shipping AI to real users need Braintrust. The platform eliminates the architectural trade-offs of proxy-dependent observability while connecting observation to systematic improvement through evals, CI/CD, and collaborative workflows.

For teams evaluating Helicone alternatives, the decision comes down to whether you need just observability or a complete platform for improving AI quality. If you're serious about shipping reliable AI products, observability alone isn't enough.

Frequently asked questions

What happens if Helicone's proxy goes down?

Your LLM calls fail. Because all traffic routes through Helicone's infrastructure, any downtime or network issues on their end directly impact your application. With Braintrust's SDK architecture, observability lives outside your request path. If Braintrust were unavailable, your AI application continues serving users normally. You just temporarily lose trace visibility until service restores. Your users never experience degraded performance.

Can I see what happened before an LLM call with Helicone?

No. 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, the tool execution that generated input data, or the business logic that constructed the prompt. Helicone provides LLM-level visibility without application context.

Braintrust's SDK tracing instruments your entire application. Capture every step from user input through retrieval, processing, LLM calls, and final output. When debugging issues, trace back through the complete context that led to each LLM request. See exactly which documents were retrieved, how they were ranked, and how prompts were constructed.

How do I run evals with Helicone?

Build it yourself. Helicone provides an API to push custom scores, but no evaluation infrastructure. You write evaluation code, create infrastructure to run it, integrate with CI/CD, and build quality gates. Most teams spend 2-3 weeks on initial setup, then ongoing maintenance.

Braintrust provides complete evaluation infrastructure: define test cases, write scorers (code-based or LLM-as-a-judge), run evals via CLI or CI/CD, and view results in the UI. The GitHub Action handles CI/CD integration with a few lines of YAML. Start evaluating in minutes rather than weeks.

Does Braintrust have a proxy like Helicone?

Yes, but it serves a different purpose and observability doesn't depend on it. Braintrust's AI proxy provides caching and unified access to multiple model providers (OpenAI, Anthropic, Google, etc.) through one OpenAI-compatible API. The proxy helps with operational concerns like caching, rate limiting, and simplified credentials.

The key difference: observability works independently through SDK integration regardless of whether you use the proxy. You get comprehensive tracing by instrumenting your application. Use the SDK alone for full observability without any proxy. Use the proxy when you want caching or simplified model access. Use both together to get request caching while SDK tracing captures full application context. With Helicone, routing through the proxy is mandatory for any observability. The proxy serves both purposes and you cannot separate them.

How does the free tier compare?

Helicone offers 10,000 LLM requests per month. Braintrust offers 1 million spans per month.

A span includes LLM calls plus all other traced operations (retrievals, tool calls, custom logic). For a RAG application, a single user query might generate 5-10 spans (embed query, search vectors, rerank, format context, LLM call, post-process results, etc.). Braintrust's free tier accommodates approximately 100,000-200,000 actual user interactions depending on application complexity.

Helicone's free tier covers 10,000 LLM calls, which might represent 1,000-2,000 user interactions in a multi-step application if each interaction involves multiple LLM calls. For simple applications with one LLM call per user interaction, it covers 10,000 interactions.

The difference reflects architectural approach. Braintrust's SDK traces your entire application flow. Helicone's proxy only logs LLM requests. Braintrust provides much more visibility per interaction.

Can I self-host Braintrust?

Hybrid deployment is available for enterprise customers, which keeps data in your infrastructure while using Braintrust's control plane. Helicone provides documented self-hosting for all users with control over ClickHouse, Redis, and S3 infrastructure.

If self-hosting is a hard requirement and you're not at enterprise scale, Helicone offers more accessibility. However, self-hosting Helicone means maintaining your own observability infrastructure, which requires DevOps resources.

Which tool is better for CI/CD integration?

Braintrust by a significant margin. It provides a turnkey GitHub Action that runs evals and displays results on every pull request. Configure once in a few lines of YAML. Every code change includes eval results showing which test cases improved, which regressed, and aggregate quality metrics. Set thresholds that block merges if performance degrades.

Helicone requires building this integration yourself. Write scripts to run evaluations, connect them to your CI/CD pipeline, format results for PR comments, and configure quality gates. This typically takes weeks of engineering work for initial setup plus ongoing maintenance.

For teams serious about preventing quality regressions before code ships, this capability saves significant engineering time while improving product quality.

Is Braintrust or Helicone better for product managers?

Braintrust provides a complete workspace for PMs. Iterate on prompts in the Playground with live evaluation results. Compare variations side-by-side against real test cases. Test different models, temperatures, system instructions. See evaluation scores update in real-time. When PMs find a winning configuration, engineers pull that exact code into production. No handoffs or translation required.

Helicone is developer-focused. PMs have no workspace to prototype against real data. They draft requirements based on intuition, then engineering rebuilds the solution in code and runs tests separately. Cross-functional collaboration requires more manual coordination and handoffs between tools.

What if I need open source for compliance?

Helicone may be a better fit. The open source model provides code transparency and self-hosting options that some organizations require for vendor tools. Braintrust is proprietary software, though enterprise contracts can include security reviews, compliance documentation, and hybrid deployment options.

Consider whether open source is a means to an end (transparency, self-hosting, avoiding vendor lock-in) or a hard requirement. If the goal is transparency, enterprise contracts can provide that. If the goal is self-hosting, hybrid deployment may suffice. If open source is a non-negotiable requirement, Helicone's license model fits better.

How do these platforms handle millions of traces?

Braintrust uses a custom time-series database optimized for trace data. Query performance stays sub-second even on datasets with millions of traces. Filter, aggregate, and analyze production traffic without waiting. The platform was built specifically to handle production scale for teams shipping real AI products.

Helicone's performance at scale is not well documented publicly. The architecture uses standard databases that may experience performance degradation as trace volume grows. For teams expecting high production volume, validate performance characteristics against your expected load.

Get started

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Explore what makes Braintrust the leading alternative to Helicone for teams building production AI. See how companies like Notion, Zapier, and Coursera use systematic evaluation to ship quality improvements faster.