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When we looked across teams switching away from Braintrust, almost none of them were abandoning evals. They were hitting a wall once agents went live in production, where evals alone don't tell you what's failing right now. So we compared the leading alternatives on the criteria that actually matter for production agent teams: session-level tracing, eval and classifier coverage, real-time alerting, log coverage policy, and OpenTelemetry compatibility.
Engineers Don't Leave Braintrust Because It's Broken
Structured eval workflows, dataset management, a solid prompt playground, and a strong pre-release testing story, for teams whose primary workflow is "run evals before shipping," it holds up well. If you're here and you like Braintrust, you're probably hitting something in production that it wasn't designed for, not because the tool is broken.
The friction shows up when agents hit production at scale. Braintrust is eval-first and pre-release-first. Teams running production agents tend to need real-time alerting, continuous session tracing across multi-step tool calls, and post-deployment debugging that routes them to the exact failing span. As Confident AI notes, AI products fail silently: chatbots hallucinate, RAG pipelines return HTTP 200 with wrong answers, agents pass malformed parameters, and traditional APM misses all of it. Most tools trace what happened without evaluating whether it was correct.
The gap is what we call the production loop: trace what happened, evaluate whether it was correct, get alerted when it isn't, and debug to the exact code line. From our own data across 12 million logs, roughly 22% of issues were explicit tool call failures where the agent stopped. The remaining 78% were silent regressions, hallucinations ranked first, user frustration second, agent forgetfulness third. None of those trigger a crash alert. That's the gap we're using as the lens throughout this comparison.
The Five Things That Actually Differentiate These Tools
Before getting into specific platforms, it's worth naming the axes that matter in production.
Session-level tracing depth. Single LLM call logging is table stakes. What matters is whether the platform captures the full execution graph: prompts, tool invocations, retrieval steps, memory accesses, retries, and state transitions, connected causally. As Vellum notes, teams need full trace capture of prompts, tool invocations, latency, cost, and user feedback in a single trace, not a stream of disconnected logs.
Eval and classifier coverage. Does the platform ship built-in classifiers for hallucinations, bad tool calls, and goal abandonment? Can you define custom failure modes without writing a full eval use? The difference between "wire your own LLM judge" and "deploy a fine-tuned classifier from three labeled examples" is significant at production volume.
Real-time alerting. Not infrastructure alerts. Behavioral alerts: hallucination rate spikes, error cascades, user frustration patterns, goal abandonment trends. What triggers the alert, what artifact attaches (trace ID, span, cost), and where it routes (Slack, PagerDuty) all matter. LangChain's observability guide points out that production trace volume makes manual log sifting unsustainable, and the fastest teams build feedback loops where past decisions inform future behavior.
Full log coverage vs. sampled. This dimension almost never appears in competing comparison tables. If your eval pipeline only classifies a sampled subset of production traffic, you will miss failures in the unsampled majority. Missing even one intermediate tool-call span can break debugging and make eval results statistically unreliable. Arize's observability guide reinforces this: whether traces become durable assets or vanish directly impacts your ability to improve agents over time.
OpenTelemetry and OpenInference compatibility. Teams already emitting OTLP spans can drop in a new backend without re-instrumenting. Platforms that require proprietary SDKs create migration lock-in.
Deployment model. SaaS vs. self-host vs. open source determines whether data-residency-sensitive teams can use the platform at all.
Guardy: Best for Full Production Observability Loop
Best for: Engineering teams who need traces, evals, and incident response in one platform, especially teams whose agents fail silently rather than crash.
Guardy was built for the production loop gap described above. Every user interaction becomes a structured execution graph: sessions contain traces, traces contain spans, and spans model every LLM call, tool invocation, retrieval step, memory access, retry, and workflow transition as a connected causal tree rather than a stream of disconnected logs. Trace replay gives engineers a visual execution graph showing exact state transitions at every step, replacing chronological log streams with structurally connected execution trees that expose reasoning pathways and failure propagation patterns.
The classifier system is the feature we see teams switch for most. We ship built-in classifiers for hallucinations, bad tool calls, agent forgetfulness, and jailbreaking. Beyond that, teams can define any failure mode, label three or four example logs, and deploy a fine-tuned classifier in under a minute. That classifier immediately wires to alerts and evaluations. One finance company we work with built a mismatched GL codes classifier this way because even the end states of their agent could take a hundred different variations: wrong vendors selected, GL codes not in the system, outputs that a traditional string check would never catch. That kind of custom signal detection isn't something you can do with an LLM-as-a-judge approach at production volume, and it's why we classify every interaction, not a sample.
We also support prompt A/B testing with statistical rigor in production (covered in depth in our Prompt A/B Testing guide) and wire observability directly into the remediation loop through GitHub-aware debugging workflows where production failures and execution context become inputs for diff generation, patch suggestions, and pull request creation. Setup is self-serve: five lines of instrumentation code using OpenTelemetry, LangChain, LangGraph, or custom Python agents. We automatically instrument LangChain, CrewAI, AutoGen, Claude Code, Vercel AI SDK, and Mastra. One Fortune 1000 customer running custom Python and LangChain agents for supply chain, HR, and marketing workflows brought their error rate from 20% down to under 10% in a single week.
Cons: We're a newer entrant than Langfuse or LangSmith, so community tutorials and third-party integration guides are still growing. Pricing is usage-based; confirm current tiers directly at tryguardy.com.
Langfuse: Open Source and Self-Hosted, But You're Wiring Your Own Evals
Best for: Data-residency-sensitive teams who want a mature OSS tracing and prompt management platform they can self-host.
Langfuse is one of the most established names in LLM observability for good reason. It's a mature open-source project with strong OpenTelemetry support, an active community, and a self-host option that makes it the default choice when data residency is a hard requirement. The platform covers online and offline evals, prompt management, LangChain and LangGraph integration, and provides a backbone for attaching scores to traces. As Confident AI's comparison notes, Langfuse is a solid option for teams wanting LLM tracing and prompt management with self-hosting.
The limitation is that Langfuse's eval story requires teams to wire their own judges. Hallucination and faithfulness metrics aren't provided out of the box, and native quality alerting is limited compared with evaluation-first platforms. For teams running agentic workflows, the interface shows input, LLM decision, and output without the deeper tool-call-level structure agents require. Teams who moved to Guardy from Langfuse often describe it as a starting point they outgrew once agents introduced multi-step tool calls, it's excellent for catching agent drift by sampling through logs, but it doesn't provide a semantic analysis layer on top. For a detailed look at Langfuse's pricing behavior, see our Langfuse pricing article. For a direct head-to-head, see Guardy vs Langfuse.
Pricing: Free OSS self-host tier; cloud plans available. Check langfuse.com for current cloud pricing.
Cons: Alerting isn't native in the way production incident response teams expect; custom classifier depth requires external tooling; the cloud tier uses sampled tracing at higher volumes.
LangSmith: A Great Fit If You're Already All-In on LangChain
Best for: Teams who built on LangChain or LangGraph and want an eval workflow that fits that stack without adding another integration layer.
LangSmith is the natural fit if your stack is already LangChain-heavy. It offers tight native integration, dataset management, human annotation workflows, a prompt hub, and a familiar UI for teams already operating in that ecosystem. LangSmith's platform page describes it as a framework-agnostic agent engineering platform for observing, evaluating, and deploying agents with dashboards covering cost, latency, errors, and qualitative metrics.
Alerting does exist in LangSmith, LangChain's alerts post covers support for error rate, run latency, and feedback score thresholds with webhook and PagerDuty integration. The important nuance is that these alerts are infrastructure-metric-focused rather than quality-evaluation-focused. A hallucination rate spike or goal abandonment pattern won't trigger an alert out of the box. For evaluation-first workflows and production dataset curation, teams often end up adding specialized layers alongside LangSmith to cover that gap.
Pricing: Free tier available; paid plans scale with usage. Check smith.langchain.com for current pricing.
Cons: Significantly less useful if you're not on LangChain; alerting is limited to infrastructure metrics; pricing scales with usage in ways that can surprise teams at volume.
Arize Phoenix: The Right Bridge If You're Coming from ML Observability
Best for: ML platform teams adding LLM observability to an existing monitoring stack who want OpenTelemetry-native instrumentation.
Arize Phoenix is built on OpenTelemetry and powered by OpenInference instrumentation, a set of custom instrumentation SDKs maintained by Arize that provides framework-native tracing for 40+ integrations. For teams with existing ML observability workflows who are adding LLM or agent components, Phoenix offers a natural bridge: OTLP-compatible spans that any OTel backend can consume, combined with LLM-based evaluators and code-based quality checks.
The distinction between Phoenix (open-source, local) and Arize AX (commercial cloud platform with managed infrastructure and alerts) matters for the OSS/self-host decision. Teams with data residency requirements can run Phoenix locally; teams needing managed scale can move to Arize AX. For a deeper head-to-head comparison with Braintrust specifically, see our Arize vs Braintrust article.
Pricing: Phoenix is open source and free to self-host. Arize AX is a commercial platform; check arize.com for current pricing.
Cons: Eval workflow depth for agentic multi-step traces is less mature than purpose-built eval tools; production alerting capability varies between the open-source and commercial tiers and should be verified directly.
Helicone: Good for Cost Tracking, Not a Full Observability Story
Best for: Early-stage teams or simple LLM API use cases where cost tracking and basic logging are the priority.
Helicone's proxy-based architecture is its defining feature: route your LLM API calls through Helicone's gateway and you get token and cost tracking, rate limiting, caching, and basic logging with minimal integration effort. Helicone's documentation describes its Sessions feature as a way to group related requests to show the true cost of user interactions, with intelligent routing and caching for cost optimization.
The scope limitation is worth naming clearly. As ZenML's monitoring roundup notes, Helicone operates at the proxy level and only sees what's in the API call, not the internal state or logic in the application. Complex chains and agents may still need separate logging inside the application to be fully debuggable. For most teams building multi-step agents, Helicone is a useful first step for cost visibility, not a complete observability story.
Pricing: Free tier available; paid plans for higher volume. Helicone is also open source and can be self-hosted. Check helicone.ai for current tiers.
Cons: Not a full eval or agent debugging platform; proxy-level visibility misses internal agent state; no native behavioral alerting or classifier support.
Confident AI: The Deepest Eval Coverage, But Production Monitoring Isn't the Focus
Best for: Teams whose primary need is rigorous, metric-rich regression eval suites with CI/CD integration, comfortable wiring production traces into a separate monitoring layer.
DeepEval is the open-source LLM evaluation framework that underpins Confident AI's platform. DeepEval's documentation lists 50+ ready-to-use metrics including LLM-as-a-judge, agent and tool-use metrics, conversational metrics, safety metrics, and RAG metrics. When teams need shared dashboards, regression tracking, observability, or production monitoring, DeepEval integrates natively with the Confident AI platform.
This is the closest alternative to Braintrust's core value proposition in this list: structured evaluation pipelines, regression testing, and metric-based quality gates. If your team's primary pain is "we need more eval metric depth and better regression tracking," Confident AI is a credible answer. As Confident AI's own evaluation tools comparison notes, it's strongest pre-release or in CI pipelines rather than for continuous production session monitoring.
Pricing: DeepEval is open source and free. Confident AI cloud has paid plans; check confident-ai.com for current pricing.
Cons: Production monitoring and real-time alerting aren't the primary focus; DeepEval itself has no UI or collaboration layer; best paired with a separate production monitoring tool.
Laminar: A Clean Starting Point for Smaller Teams
Best for: Smaller teams or early-stage projects wanting clean agent trace visualization and prompt management without committing to a heavier platform.
Laminar is an open-source observability platform purpose-built for AI agents, offering OpenTelemetry-native tracing, an SDK for running evals locally or in CI/CD, natural language agent monitoring via Signals, and SQL access to all trace data for custom dashboards, as described on Laminar's GitHub. Their developer-ergonomic entry point and transcript view for long-running agent traces are real differentiators for teams who need to read what an agent said across a 2,000-span trace without parsing every span manually. Laminar's own observability ranking describes their Agent Rollout debugger and Signals for natural-language outcome tracking as purpose-built for agents that fail four tool calls deep.
Pricing: Open source and free to self-host; cloud options available. Check laminar.sh for current plans.
Cons: Less mature than Langfuse or LangSmith for large-scale production eval workflows; alerting and custom classifier depth are limited compared to full-stack platforms. Pricing and self-host options should be verified on their site.
How These Tools Actually Compare
Gartner predicts that 40% of agentic AI projects will be canceled by end of 2027 due to reliability concerns. That's a striking number, and it's exactly why the alerting and full log coverage columns in the table below matter more than most comparison tables suggest. For pricing, we use "free tier / paid plans" where exact published numbers weren't available at time of writing rather than fabricating figures.
| Tool | Session-Level Tracing | Eval / Classifiers | Real-Time Behavioral Alerting | Full Log Coverage (vs Sampled) | OpenTelemetry Native | Deployment | Starting Price |
|---|---|---|---|---|---|---|---|
| Guardy | Full execution graph with replay and fork | Built-in + custom classifiers (deploy in ~1 min) | Yes, Slack alerts on behavioral anomalies and error spikes | Full coverage, every interaction | Yes (via OTel) | SaaS | Usage-based; confirm at tryguardy.com |
| Langfuse | Trace and span-level | Bring-your-own judge; no built-in LLM quality metrics | Limited; no native behavioral alerting | Sampled on cloud tier | Yes | OSS self-host / SaaS cloud | Free OSS; paid cloud plans |
| LangSmith | Trace and run-level | Dataset-based evals; human annotation | Infrastructure metrics only (error rate, latency) | Sampled at volume | Partial | SaaS | Free tier; usage-based paid |
| Arize Phoenix | Span-level, OTel-native | LLM-based evaluators + code checks | Via Arize AX commercial tier | Varies by tier | Yes (OTel + OpenInference) | OSS (Phoenix) / SaaS (Arize AX) | Free OSS; paid Arize AX |
| Helicone | Proxy-level only | None | None | API-call level only | Partial | OSS self-host / SaaS | Free tier; paid plans |
| Confident AI | Integration-dependent | 50+ DeepEval metrics; regression suites | Not primary focus | CI/CD-oriented | Partial | OSS (DeepEval) / SaaS | Free OSS; paid cloud |
| Laminar | Agent-native span-level | Local/CI evals; Signals for NL monitoring | Limited | OpenTelemetry-based | Yes | OSS self-host / SaaS | Free OSS; paid cloud |
| Braintrust | Basic trace logging | Strong pre-release eval and dataset management | Limited for production behavioral signals | Eval-run-oriented | Partial | SaaS | Free tier; usage-based paid |
The alerting and full log coverage columns are the ones most competitors leave out of their comparison tables. For teams running agents in production, those two columns often make the decision.
Switching from Braintrust Is More of a Workflow Change Than a Data Migration
Switching from Braintrust is generally more of a workflow change than a data migration challenge, but there are a few practical dimensions worth planning for.
Data export. Braintrust allows export of datasets and eval results. Prompt version history and dataset records are the most portable assets. Eval scores tied to specific Braintrust experiment runs are the least portable, since each platform has its own data model for what constitutes a "run" or "experiment." Plan to rebuild eval baselines in the new platform rather than importing historical scores.
Integration effort. If you're already emitting OpenTelemetry spans, you have the easiest migration path: swap the OTLP endpoint and most of your instrumentation carries over. Guardy, Langfuse, Arize Phoenix, and Laminar all support OTel-native ingestion. If you're on LangChain or LangGraph, most platforms in this list have native SDK support, though depth varies. We support LangChain, LangGraph, CrewAI, AutoGen, Claude Code, Vercel AI SDK, Mastra, and custom Python agents, and our setup is five lines of instrumentation code, self-serve. A complex agent with tool calls, retrieval, reranking, and multi-step reasoning can generate 10 to 30 observations per user request, which affects both migration testing scope and ongoing pricing at volume.
Workflow change. This is the dimension teams most underestimate. Eval-first teams moving to a tracing-first platform often spend the first two weeks trying to replicate their existing eval runner before they start using the tracing and alerting capabilities that drove the switch. The reverse is also true: teams moving from a tracing tool to an eval-first platform often discover they've lost their production monitoring coverage during the transition. We recommend running both platforms in parallel for two to four weeks before fully cutting over, using the overlap period to validate that the new platform is surfacing the same failure types you were catching before.
Data residency. If data residency is a hard requirement, self-host options narrow the list to Langfuse, Arize Phoenix, Laminar, and Helicone. SaaS-only platforms require confirming regional deployment and data processing agreements before migration.
For a deeper look at how agentic tracing architectures work across platforms, see our AI Agent Tracing Explained guide.
Frequently Asked Questions
What are the best Braintrust alternatives?
The best alternative depends on what Braintrust isn't covering for your team. For teams that need the full production loop, tracing, evals, alerting, and debugging in one platform, Guardy is the most complete option. For open-source self-hosted tracing with prompt management, Langfuse is the most mature choice. For teams already on LangChain, LangSmith fits naturally. For rigorous pre-release regression eval suites, Confident AI and DeepEval offer the deepest metric coverage. For cost tracking on simple LLM API workflows, Helicone is the fastest entry point.
Which Braintrust alternative is best for OpenTelemetry-native observability?
Arize Phoenix is the most explicitly OTel-native option, built on OpenTelemetry and OpenInference from the ground up with 40+ framework integrations. Langfuse and Laminar also offer strong OTel support. Guardy uses OpenTelemetry for initial log ingestion and runs behavioral analysis on top of the trace data. Teams already emitting OTLP spans will find all four integrate without re-instrumentation.
Which Braintrust alternative supports prompt A/B testing with evaluation?
Guardy includes prompt A/B testing with statistical rigor in production as a native feature, wired directly to the eval and alerting layer. For a detailed breakdown of how to run production prompt experiments without silent regressions, see our Prompt A/B Testing guide. LangSmith supports prompt versioning and dataset-based comparison, though its A/B testing is more pre-release oriented. Most other platforms in this list treat prompt management separately from production experimentation.
Which Braintrust alternative works best with LangChain and LangGraph?
LangSmith is the tightest native integration since it's built by the same team. Langfuse has deep LangChain and LangGraph support and is a common choice for teams who want self-hosting alongside the LangChain stack. Guardy natively instruments both LangChain and LangGraph, and extends to CrewAI, AutoGen, Claude Code, Vercel AI SDK, and Mastra, which makes it the stronger choice if your stack includes multiple agent frameworks alongside LangChain.
Is Braintrust still worth using?
Yes, for specific use cases. Braintrust is a strong platform for teams whose primary workflow is pre-release evaluation: building eval datasets, running structured quality gates before shipping, managing prompt versions, and using the playground for iterative testing. If that's 80% of your current pain, Braintrust addresses it well. It becomes a poor fit when production monitoring, real-time behavioral alerting, multi-step agent debugging, or full log coverage are priorities. Those workflows require a platform built around the production loop rather than the pre-release loop, which is the core architectural difference between Braintrust and tools like Guardy, Langfuse, or Arize Phoenix. For a direct feature comparison, see our Braintrust vs Guardy article.
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