6 Best Braintrust Alternatives in 2026

Your agent hallucinates in production. Braintrust shows you the trace after users complain, but it can't block the bad output before it ships. Braintrust delivers solid eval workflows and framework support across 9+ AI development tools. But it lacks runtime intervention, relies on generic LLM-as-judge patterns instead of purpose-built eval models, and creates operational overhead for enterprise deployments. As you scale from prototype to production, observation-only tools leave you flying blind in real time. 

Multi-agent systems compound this problem. Every additional tool call, retrieval step, and branching decision increases the surface area for failures that require real-time governance, not post-mortem analysis. You need platforms that can intervene at the speed your agents operate, not just log what went wrong after the damage is done. When most generative AI pilots fail to reach production, you need platforms that close the loop between seeing issues and preventing them. This guide covers six alternatives that address these gaps.

TLDR:

• Braintrust lacks runtime intervention and purpose-built eval models, but it does offer hybrid enterprise deployment options that provide some deployment flexibility.

• Six alternatives compared, including Galileo, LangSmith, Arize AI, and Langfuse.

• Galileo is the top pick for combined observability, eval, and runtime intervention.

• Tools evaluated on agent-native features, deployment options, and cost efficiency.

Why Teams Look for Braintrust Alternatives

Braintrust provides useful eval and tracing capabilities, but teams consistently hit the same walls as they move from prototyping to production. Three core limitations drive the search for alternatives.

No Runtime Intervention or Production Guardrails

Braintrust's observability is retrospective. The platform enables teams to trace agent decisions, monitor latency, and review logs after the fact. But it lacks intervention capabilities for complex multi-step workflows. There are documented bugs where data is not displayed in Thread view for production logs and traces. 

For teams running autonomous agents, this observation-only approach means debugging requires reactive investigation rather than proactive prevention. CIO.com's analysis identified this exact gap: platforms without a trust layer cannot provide governance controls required by SOC 2, HIPAA, or GDPR environments. When an autonomous agent produces a harmful output, your team discovers it through a customer complaint or a compliance alert, and there are no options to block it before it reaches the end user.

No Proprietary Evaluation Models

Braintrust's eval capabilities rely on LLM-as-judge patterns and its Loop AI assistant for custom scoring. According to Braintrust's own documentation, custom eval relies on the Loop AI assistant with no explicit mention of fully user-defined eval code or plugins. For regulated industries requiring auditable, deterministic eval logic, this dependency introduces non-determinism that may be unacceptable for compliance purposes. LLM-as-judge patterns also carry a high cost at production scale. 

Every evaluation requires a full inference call, meaning you're paying GPT-4-level pricing just to score your outputs. At high throughput, eval costs can rival your primary model spend. The non-deterministic nature of these evaluations creates further challenges for regulated industries. Auditors expect reproducible, consistent scoring logic that they can trace and verify.

Limited Enterprise Deployment Flexibility

Braintrust operates primarily as a cloud platform. While self-hosting options exist, Braintrust's documentation acknowledges that self-hosting requires "technical expertise and management" that "might be a barrier for some enterprises." The hybrid model requires organizations to maintain their own data infrastructure. 

The documentation explicitly acknowledges this creates "operational overhead and complexity for some users." Your compliance team shouldn't need to architect around platform limitations. Splitting responsibility between your infrastructure and Braintrust's control plane complicates incident response timelines and audit documentation. Enterprise teams need turnkey deployment options that their infrastructure teams can operate without dedicating ongoing engineering cycles to maintenance.

What to Look for in a Braintrust Alternative

The gaps above point directly to the criteria that matter most:

• Runtime intervention that acts on outputs before users see them

• Proprietary eval models purpose-built for speed and cost efficiency, including specialized hallucination detection

• Self-service metric creation enabling business teams to build custom evaluators without engineering bottlenecks

• Framework-agnostic integration through OpenTelemetry support

• Enterprise deployment options including on-premises, hybrid, and data residency controls

• Agent-native eval and observability features designed for multi-step, tool-using workflows

• Cost efficiency at scale with sustainable economics for monitoring production traffic

Comparison Table

1. Galileo, Best Overall Braintrust Alternative

Galileo is the AI observability and eval engineering platform where offline evals become production guardrails. Where Braintrust stops at observation, Galileo closes the loop with runtime intervention that blocks, transforms, or routes outputs in real time. The platform's Luna-2 small language models replace expensive LLM-as-judge patterns with purpose-built evaluators. Trusted by enterprises including HP, Twilio, and Reddit, with SOC 2 compliance and full on-premises deployment options.

Key Features

• Agent Graph visualization renders every branch, decision, and tool call across multi-agent workflows

• Signals automatically surfaces failure patterns across production traces

• Luna-2 SLMs deliver sub-200ms eval latency with 97% cost reduction compared to GPT-4 alternatives

• Runtime Protection intercepts prompt injections, PII leaks, and hallucinations before they reach users

• Eval and feedback loops enable custom evaluators with accuracy improvement through iterative refinement

• OpenTelemetry-native tracing integrating with LangChain, CrewAI, OpenAI Agents SDK, and other frameworks

Strengths and Considerations

Strengths:

• Only platform natively combining observability, eval, and runtime intervention in a single product

• Luna-2 delivers significant cost savings vs. GPT-4-based eval with comparable accuracy

• Full on-premises, cloud, and hybrid deployment with SOC 2 Type II and ISO 27001 compliance

• Agent-native features that can support50K+ simultaneous agents and 20M+ traces per day

• Recognized by Gartner, IDC, and leading analyst coverage

• Self-service metric creation enables business stakeholders to build custom evaluators without engineering dependencies

Considerations:

• Platform depth is optimized for teams running production agents at scale

Best For

Enterprise AI teams that need to move beyond observation-only platforms. If you're running autonomous agents in regulated industries or managing multi-agent orchestrations at scale, Galileo addresses the specific gaps that drive teams away from Braintrust. Purpose-built for platform teams who need governance, reduced incident response times, and business stakeholder participation in eval workflows. 

Particularly strong for healthcare organizations navigating HIPAA requirements, fintech teams managing compliance-critical customer interactions, and enterprises deploying autonomous customer service agents where a single hallucination carries reputational and regulatory risk.

2. LangSmith

LangSmith is LangChain's enterprise LLM observability and eval platform for production AI agent workflows. It offers distributed tracing, real-time monitoring, automated evals, and human-in-the-loop testing. Available as managed SaaS and self-hosted deployments.

Key Features

• Distributed tracing optimized for LLM execution paths with nested agent reasoning and tool calls

• Real-time monitoring dashboards tracking usage, latency, and cost across LLM providers

• Multi-turn eval framework with LLM-as-judge patterns, custom scorers, and expert annotation queues

• Golden dataset management for regression prevention with version-controlled test sets

• LangGraph Studio IDE for visual agent workflow development and debugging

Strengths and Weaknesses

Strengths:

• Seamless native integration for LangChain and LangGraph applications

• Collaborative annotation workflows bridging engineering and domain expert teams

• Insights Agent provides automated pattern discovery for performance anomalies

Weaknesses:

• Optimized for LangChain workflows, creating setup friction for teams using other frameworks

• Observability is retrospective with no runtime intervention or proprietary eval models

Best For

Teams building with LangChain and LangGraph that need integrated observability. Organizations prioritizing collaborative workflows between engineers and domain experts. Best suited for mid-to-large engineering teams already invested in the LangChain ecosystem who want to minimize instrumentation overhead and leverage native framework integration.

3. Arize AI

Arize AI is an enterprise ML observability platform with $131M in funding and deep roots in traditional ML monitoring. It extends its trace-span-session architecture to LLM workflows with LibreEval hallucination detection and on-premises deployment through its NVIDIA partnership.

Key Features

• Three-tiered observability architecture (traces, spans, sessions) for multi-agent workflows

• LibreEval open-source hallucination detection with factual consistency checking

• Production monitoring with custom threshold-based and automated anomaly alerting

• LLM-as-a-Judge evaluators with human-in-the-loop annotation integration

• On-premises deployment for data sovereignty in regulated industries

Strengths and Weaknesses

Strengths:

• Proven enterprise adoption across travel, recruiting, and financial services verticals

• Kubernetes-native on-premises architecture for regulated industry compliance

• Granular span-level visibility into individual LLM operations

Weaknesses:

• Traditional ML monitoring heritage means agent-native capabilities require additional development effort

• Engineering-focused interface creates accessibility challenges for non-technical stakeholders

Best For

Enterprise teams with existing ML observability needs extending coverage to LLM applications. Strong for organizations with data sovereignty mandates in regulated industries. Particularly well-suited for large platform engineering teams managing both traditional ML models and LLM workloads who want consolidated monitoring across their full AI stack.

4. Langfuse

Langfuse is an open-source LLM engineering platform combining observability, prompt management, eval, and analytics. With 10K+ GitHub stars and an API-first architecture, it provides self-hosting for teams needing complete data control.

Key Features

• Comprehensive tracing with prompt capture, token usage, cost tracking, and latency measurement

• Version-controlled prompt management with interactive testing playground

• Dataset management and experimentation framework for benchmarking

• Self-hosting deployment with complete data ownership

• API-first extensibility for custom workflow integration

Strengths and Weaknesses

Strengths:

• Open-source design with plans to open-source all product features

• Self-hosting with complete data ownership, eliminating vendor dependency

• Framework-agnostic integration through OpenTelemetry and multiple SDKs

Weaknesses:

• Self-hosting requires dedicated engineering resources for infrastructure management

• Lacks runtime intervention and proprietary eval models, requiring external tools for production guardrailing and specialized scoring

Best For

Engineering-first teams prioritizing open-source transparency and full self-hosting control over their observability stack. Ideal for small-to-mid-size teams with strong DevOps capabilities who want to avoid vendor lock-in and are comfortable investing engineering cycles in infrastructure management and custom integrations.

5. MLflow

MLflow is the leading open-source MLOps platform managing the complete ML lifecycle from experimentation to deployment. Its dedicated GenAI eval system adds LLM-as-a-Judge scorers, agent eval capabilities, and production monitoring.

Key Features

• GenAI eval framework (mlflow.genai.evaluate()) with LLM-as-a-Judge scorers and human feedback tracking

• Prompt Registry with version-controlled template management and performance comparison

• Model Registry providing centralized governance with staging, production, and archival transitions

• REST-based serving across cloud, on-premises, and edge environments

• CI/CD integration with GitHub Actions, Jenkins, and GitLab CI

Strengths and Weaknesses

Strengths:

• Unified platform for teams managing both traditional ML and LLM workloads

• Open-source flexibility with self-hosting for data sovereignty

• Mature model governance with complete lineage tracking and approval workflows

Weaknesses:

• LLM observability depth is less specialized than purpose-built tools, particularly for complex agent debugging

• Developer-focused interface is less optimized for cross-functional collaboration with business stakeholders

Best For

Teams already invested in MLflow for traditional ML who want to extend coverage to LLM eval using a unified platform. Best for organizations with established MLOps practices and data science teams who need a single governance layer across both classical ML and generative AI workloads without introducing additional toolchain complexity.

6. Databricks

Databricks Mosaic AI provides integrated LLM observability within its unified lakehouse platform. Named a Leader in the IDC MarketScape 2025-2026, it offers end-to-end tracing through Managed MLflow and Unity Catalog governance across AWS, Azure, and GCP.

Key Features

• Managed MLflow tracing capturing inputs, outputs, prompts, retrievals, and tool calls

• Custom eval with LLM-as-a-Judge scorers for agent output quality measurement

• Unity Catalog providing fine-grained access controls, lineage, and auditing

• AI Gateway with usage tracking, payload logging, and security controls

• Multi-cloud deployment across AWS, Azure, and GCP

Strengths and Weaknesses

Strengths:

• Unified architecture eliminates data silos by combining storage, processing, and AI workflows

• Open standards and multi-cloud portability prevent vendor lock-in

• Strongest governance and lineage tracking for organizations with strict compliance needs

Weaknesses:

• Requires adoption of the broader Databricks lakehouse, adding overhead for teams only needing LLM observability

• Eval capabilities are less granular compared to dedicated AI observability platforms

Best For

Enterprise teams already on Databricks or requiring unified data-to-AI governance through a single platform. Especially valuable for large organizations with centralized data platform teams who build LLM applications on proprietary enterprise data and need end-to-end lineage from raw data through model serving without introducing additional vendor relationships.

Choosing the Right Braintrust Alternative

Braintrust delivers solid eval workflows and a strong developer experience. But production AI teams need more than retrospective observability. The gaps in runtime intervention, eval model constraints, and enterprise deployment complexity become costly as agent complexity scales.

Use the criteria from this guide as your decision framework. Can the platform act on outputs in real time? Does it offer cost-efficient eval at production scale? Can your compliance team deploy it within existing infrastructure?

Galileo addresses each of these needs in a single platform:

• Runtime Protection: Blocks hallucinations, PII leaks, and prompt injections in under 200ms

• Luna-2 SLMs: Purpose-built eval at 97% lower cost than GPT-4-based scoring

• Signals: Automatic failure pattern detection across production traces

• Agent Graph: Full visualization of multi-agent decision paths and tool calls

• Enterprise deployment: On-premises, cloud, and hybrid with SOC 2 Type II compliance

Book a demo to see how Galileo closes the loop from observability to intervention for your production AI systems.

FAQs

What Is Braintrust Used For?

Braintrust is an LLM eval and observability platform that helps AI teams instrument, trace, and test production AI applications. It captures reasoning chains, supports LLM-as-judge and human eval workflows, and monitors latency, cost, and quality metrics. The platform integrates with 9+ major AI frameworks and counts Notion, Stripe, and Dropbox among its customers.

How Does Galileo Compare to Braintrust?

Galileo extends beyond Braintrust's observation-and-eval scope by adding runtime intervention that blocks unsafe outputs before users see them. Luna-2 models achieve significant cost savings while enabling low-latency production monitoring. The platform also offers full on-premises deployment for regulated industries, addressing Braintrust's operational overhead for enterprise self-hosting.

Is It Easy to Switch from Braintrust to an Alternative?

Migration complexity varies by platform and integration depth. Most alternatives support OpenTelemetry-based instrumentation, making trace collection portable. The primary effort involves reconfiguring eval workflows, custom scorers, and monitoring dashboards. Teams typically run both platforms in parallel during transition before fully decommissioning.

What Should I Look for in a Braintrust Alternative?

Prioritize runtime intervention that acts on outputs before users see them. Look for proprietary eval models that reduce cost and latency versus LLM-as-judge approaches. Self-service metric creation, enterprise deployment flexibility, and agent-native architecture are equally critical for production-scale AI operations.

Why Do Teams Choose Galileo over Braintrust?

Teams choose Galileo for its integrated approach to preventing quality issues before agents reach production. The platform's Luna-2 eval models deliver significant cost advantages over traditional LLM judges. Runtime monitoring operates at low latency, enabling full production traffic evaluations. The platform supports on-premises deployment for regulated industry requirements.