Braintrust Alternatives: 7 Tools for AI Evals and Logging

Searching for a Braintrust alternative usually means one of two things: you're already running into its limits, or you're comparing every serious option before picking an AI eval and logging tool at all.

Either way, the reason is rarely that Braintrust doesn't work. It does. It's a well-funded platform, backed by an $80 million Series B in early 2026, and it covers tracing, evaluations, datasets, and a built-in gateway in one dashboard. The real question is whether it fits your team, your stack, and your budget, especially once self-hosting, framework compatibility, or cost at scale enters the picture.

That's what this guide breaks down. Below are seven real Braintrust alternatives, each one covered on what it does well, where it falls short, and exactly what it costs, verified against the vendor's own pricing page rather than a secondhand estimate. By the end, you'll know which one actually fits your workflow instead of guessing from a feature list.

Key Factors to Consider When Comparing These Tools

Not every Braintrust alternative offers the same evaluation and logging capabilities. Before choosing one, consider these six factors.

1. Evaluation Capabilities: Look for dataset-based testing, custom scorers, LLM-as-a-judge evaluations, human review, and regression testing. Evaluate whether the tool supports both pre-release and production evaluations.

2. Tracing and Logging: A useful tool should help you inspect model responses, tool calls, errors, latency, token usage, and costs. Complete agent traces make it easier to identify where an AI workflow fails.

3. Production Monitoring: Logging alone isn't enough. Check for real-time scoring, failure detection, alerts, and dashboards that help teams identify issues in live AI applications.

4. Deployment and Data Control: Consider whether the platform supports cloud hosting, self-hosting, or private deployment. Data retention, redaction, and residency controls matter when logs contain sensitive information.

5. Integrations and OpenTelemetry: Choose a tool that works with your existing frameworks, model providers, and monitoring systems. OpenTelemetry support can make tracing across different services easier.

6. Pricing and Scalability: Look beyond the free tier. Compare charges for traces, spans, storage, seats, and evaluation calls. A tool that's affordable during testing may become expensive as production usage grows.

At a Glance

1. LangSmith: the native choice if you're already building with LangChain or LangGraph.

2. Langfuse: open-source, done right, with a free self-hosted version that has nothing held back.

3. Arize Phoenix and Arize AX: the sharpest tool here for catching retrieval and embedding drift.

4. Helicone: change one line of code and get instant cost and latency visibility.

5. Weights & Biases Weave: LLM evals without leaving a workflow your ML team already lives in.

6. Comet Opik: a free tier generous enough to run in production, not just kick the tires.

7. Confident AI: built on open-source DeepEval, with genuine end-to-end application testing.

Best Braintrust Alternatives for AI Evals and Logging

Here's the full rundown, tool by tool: what each one actually does well, where it falls short, and pricing checked against the vendor's own page as of October 2026.

1. LangSmith

LangSmith is LangChain's own observability and evaluation platform, and it's the natural choice if your application is already built with LangChain or LangGraph. It covers tracing, evaluation, prompt management, dataset testing, and agent deployment inside one dashboard, with tracing that shows up automatically once you're using the framework. It also works with non-LangChain applications, just with less of that automatic convenience.

Features:

• Automatic tracing for LangChain and LangGraph applications, plus manual instrumentation for anything else

• Online and offline evaluations, with an annotation queue for human feedback

• Prompt Hub and a built-in playground for iterating on prompts

• LangSmith Deployment for shipping and scaling agents, including a free small serverless deployment on the Plus plan

• An LLM gateway for cost controls, rate limiting, and model fallbacks

Advantages:

• Close to zero setup if you already use LangChain or LangGraph

• One platform covers tracing, evals, and deployment instead of stitching tools together

• Active product development, with new features like autonomous issue detection (Engine) shipping through 2026

Disadvantages:

• No open-source version, and self-hosted or hybrid deployment is only available on the Enterprise plan

• Usage-based pricing on top of seats (billed in LangChain Standard Units) can be hard to predict until you've run it for a month

Pricing:

• Developer: $0 per seat/month, 1 seat, up to 5,000 base traces/month included, then pay-as-you-go

• Plus: $39 per seat/month, unlimited seats, up to 10,000 base traces/month included, includes access to Deployment and Engine

• Enterprise: custom pricing, with self-hosted and hybrid deployment, custom SSO/RBAC, and a support SLA

• A discounted Startup plan with credits is available for early-stage, VC-backed companies

Best for: teams already building on LangChain or LangGraph who want evals and tracing without adding a second framework.

2. Langfuse

Langfuse is the open-source name that comes up most often in this space, and for good reason. The core product, covering tracing, prompt management, datasets, and LLM-as-a-judge evaluation, is MIT licensed, and you can self-host the whole thing for free with no feature gate separating the paid version from the free one. It's built on OpenTelemetry, so it plugs into your existing observability setup instead of asking you to replace it. Langfuse was acquired by ClickHouse in January 2026, and the project has stayed actively developed since.

Features:

• Full tracing and session tracking for LLM calls, agent graphs, and multi-turn chats

• LLM-as-a-judge evaluation, prompt experiments, and a dataset manager

• Prompt versioning, a playground, and human annotation queues

• OpenTelemetry support alongside native Python, JavaScript, and proxy-based (LiteLLM) integrations

• A usage calculator on the pricing page so you can estimate your actual monthly bill before committing

Advantages:

• Genuinely free, full-featured self-hosting, not a crippled trial that pushes you toward the paid cloud

• No per-seat pricing on the paid cloud tiers, so adding teammates doesn't change your bill

• Framework-agnostic, so it works whether your stack is LangChain, LlamaIndex, or something custom

Disadvantages:

• Self-hosting still means you're running and maintaining the infrastructure yourself

• Cloud pricing is usage-based, which takes more work to forecast than a flat monthly fee

Pricing:

• Open Source (self-hosted): free, MIT license, all core platform features and APIs

• Hobby (Cloud): free, no credit card required, 50,000 units/month included, 2 users, 30-day data access

• Core: $29/month, 100,000 units/month included then roughly $8 per additional 100k, unlimited users, 90-day data access

• Pro: $199/month, unlimited data history, high rate limits, SOC2 and ISO27001 reports available

• Enterprise: custom pricing (reported around $2,499/month as a starting point), SSO enforcement, dedicated support, billing via AWS Marketplace

Best for: teams that want real self-hosting, not a limited free trial, and don't mind a bit more setup than a fully managed tool.

3. Arize Phoenix and Arize AX

Phoenix is Arize AI's open-source observability and evaluation tool, and it's particularly strong if retrieval quality is what keeps breaking your RAG pipeline. It covers embedding drift, retrieval precision and recall, and context-relevance scoring, which general-purpose logging tools don't address in depth. Arize AX is the managed, enterprise-facing layer built on the same foundation, aimed at teams that want a hosted version with higher limits and compliance features.

Features:

• Embedding drift detection and retrieval-quality metrics (precision, recall, relevance)

• LLM-as-a-judge evaluation alongside traditional ML monitoring, since Arize also supports classic model observability

• OpenTelemetry-based tracing that integrates with an existing observability stack

• An in-app AI assistant (Alyx) for exploring traces and evaluations

• Dashboards and alerting on the managed AX tiers

Advantages:

• Phoenix itself is free to self-host with no feature cap

• Strongest option on this list specifically for diagnosing RAG and retrieval problems

• AX Free gives you a real managed tier before you need to talk to sales

Disadvantages:

• Phoenix's setup leans more technical than a drop-in proxy tool like Helicone

• AX Enterprise pricing is custom and aimed more at larger teams than solo builders

Pricing:

• Phoenix (open source, self-hosted): free, Elastic License 2.0, no usage caps

• AX Free: $0/month, limited monthly trace spans and shorter data retention

• AX Pro: $50/month, higher span and ingestion limits with longer retention; startup pricing available on request

• AX Enterprise: custom pricing, with dedicated support, security compliance, and self-hosted or VPC deployment options

Best for: teams whose main AI headache is retrieval quality rather than general prompt behavior.

4. Helicone

Helicone takes a different approach than most tools on this list. Instead of adding an SDK to your code, you change your API base URL to point at Helicone's proxy, and it logs every request and response automatically, including cost, latency, and all. It's open source, and the one-line integration makes it the fastest tool here to get running. It's strongest on cost and usage visibility, rather than on deep evaluation workflows.

Features:

• Proxy-based logging that requires only a base URL change, no SDK rewrite

• Cost, latency, and token tracking across OpenAI, Anthropic, and any OpenAI-compatible provider

• Built-in caching, retries, and rate limiting as part of the gateway layer

• Prompt experiments and a key vault for managing provider API keys

• Self-hostable under an open-source license if you want to run it yourself

Advantages:

• Fastest setup on this list; a config change rather than a code change

• Strong, purpose-built cost and spend visibility across providers

• Genuinely open source, not just a free trial

Disadvantages:

• Routing every request through a proxy adds some latency, and it's a dependency sitting between your app and the model provider

• Evaluation and tracing depth are lighter than dedicated platforms like Langfuse or LangSmith, so some teams pair it with a second tool

Pricing:

• Self-hosted: free, Apache 2.0 license

• Hobby (Cloud): free, 10,000 requests/month, 1GB storage, 1 seat, 1 organization

• Pro: around $79/month, higher request volume and seats

• Team: around $799/month, features aimed at regulated teams (SOC2/HIPAA-track controls, multiple organizations)

• Enterprise: custom pricing

Best for: teams whose first problem is visibility into spend and latency, not evaluation depth.

5. Weights & Biases Weave

Weave is Weights & Biases' tracing and evaluation product for LLM applications, built on top of the ML experiment-tracking platform many data science teams already use. If your organization already lives in W&B for model training, Weave lets you keep that continuity instead of adopting a second, unrelated tool for the LLM side of the work.

Features:

• LLM call tracing and evaluation inside the same workspace as W&B's experiment tracking

• Scoring and comparison tools for prompts, models, and application versions

• Integrates with LangChain, LlamaIndex, and common model providers

• Shared dashboards that sit alongside existing W&B training runs and model registry data

• Enterprise options including single-tenant hosting and a HIPAA-compliant configuration

Advantages:

• Real continuity for teams already running training and experimentation in W&B

• A genuinely free Personal tier for solo, non-commercial use

• Backed by a long-established MLOps platform rather than a newer single-purpose startup

Disadvantages:

• Weave itself is newer and less polished than W&B's core experiment-tracking product

• Not a tool you'd adopt cold if you're not already a W&B customer; it's built for continuity, not as a standalone first choice

Pricing:

• Personal (self-hosted): $0/month, 1 seat, non-commercial use only

• Pro (cloud-hosted): from $60/month, for teams under 50 employees

• Enterprise: custom pricing, available cloud-hosted or self-hosted, with SSO, HIPAA, and audit logs

• Weave costs aren't broken out separately; confirm ingestion limits directly with W&B

Best for: ML teams already using Weights & Biases who want LLM evaluation without leaving that ecosystem.

6. Comet Opik

Opik is Comet's open-source entry into this space, built by the team behind Comet's longer-standing ML experiment-tracking platform. It ships with a notably generous free tier, including agent evaluation and guardrail features that some competitors hold back for paid plans, and the open-source core is Apache 2.0 licensed.

Features:

• Tracing and evaluation for LLM calls and multi-step agents

• Automated agent optimization and built-in guardrails, even on the free tier

• Code-based and LLM-as-a-judge evaluation metrics

• Integrations with LangChain, LlamaIndex, and major model providers through a Python SDK

• Cost-intelligence tooling, including tracking spend on coding agents like Claude Code

Advantages:

• One of the more usable free tiers in this category, with real production features rather than a crippled trial

• Fully open source (Apache 2.0), so self-hosting is a genuine option, not a marketing line

• Backed by Comet's existing ML tooling, so it has more history than a brand-new entrant

Disadvantages:

• Enterprise and some paid-tier pricing aren't published, so budgeting requires a sales conversation

• Smaller ecosystem and community than Langfuse or LangSmith at this point

Pricing:

• Open Source (self-hosted): free, Apache 2.0

• Free Cloud: $0/month, hosted with usage limits

• Pro Cloud: reported at roughly $19/month, billed per account rather than per seat

• Enterprise: custom pricing, not publicly disclosed

Best for: smaller teams or solo builders who want production-grade tracing and evals without a credit card.

7. Confident AI

Confident AI is built on DeepEval, an open-source evaluation framework with more than 50 research-backed metrics covering hallucination, tool-use correctness, bias, and conversational quality. The open-source layer runs locally at no platform cost, with only your LLM API calls to pay for. Its standout feature is testing your actual application end-to-end over HTTP, rather than evaluating prompts in isolation, which matters once your AI feature involves more than a single model call.

Features:

• Open-source DeepEval library with 50-plus metrics, usable in a Pytest-style testing workflow

• End-to-end HTTP evaluation of a live application, not just isolated prompt testing

• CI/CD regression gates to catch quality drops before a release ships

• Dataset curation, annotation, and drift-aware alerting on the hosted cloud layer

• AI red teaming (DeepTeam) and production guardrails on higher tiers

Advantages:

• A genuinely open evaluation framework at the core, not just a free trial wrapped around a closed product

• Clear upgrade path from local open-source testing to a managed cloud layer when you need collaboration features

• Starter and Team are flat, per-organization prices with unlimited seats included, so adding teammates doesn't raise the bill the way per-seat pricing would

Disadvantages:

• The open-source layer alone lacks team features like shared dashboards and collaborative annotation, which live only in the paid cloud product

• Pricing has shifted more than once in 2026, so the numbers below are more of a current snapshot than a fixed reference

Pricing:

• Open source DeepEval (self-hosted): free; you only pay for your own LLM API usage

• Free (Cloud): $0/month forever, 2 user seats, 1 project, 5 test runs per week, 1 GB/month of trace spans

• Starter: $200/month per organization, unlimited seats, up to 5 projects, 5 GB-months of trace spans included, then $1/GB-month

• Team: $2,000/month per organization, unlimited seats and projects, 75 GB-months of trace spans included, then $1/GB-month, adds custom RBAC, SOC 2, and SSO

• Enterprise: custom pricing, unlimited usage, adds on-prem deployment, HIPAA, custom data residency, and dedicated AI red teaming and governance modules

Best for: teams that want a genuinely open evaluation framework first, with the option to add a managed layer later.

Side-by-Side Comparison

Braintrust's own plans, verified on braintrust.dev, set the baseline: Starter is free with usage-based overage, Pro is $249/month, and self-hosting is restricted to the custom-priced Enterprise tier. The table below measures the seven alternatives against that baseline across the dimensions that actually drive a buying decision.

Finding the Right Alternative to Braintrust

The right Braintrust alternative depends on whether your team prioritizes LLM evaluations, production monitoring, debugging, cost tracking, or enterprise-level AI quality and safety.

• LangChain and LangGraph applications → LangSmith. Best for teams that want integrated tracing, evaluations, and agent debugging within the LangChain ecosystem.

• Open-source and self-hosted observability → Langfuse. Ideal for teams that need tracing, prompt management, and evaluations with the flexibility of self-hosting.

• RAG application monitoring → Arize Phoenix. A good fit for teams that need to assess retrieval quality, embedding drift, and context relevance. Consider Arize AX for managed deployment.

• LLM usage and cost tracking → Helicone. Best for developers who want to monitor token costs, latency, and usage without complex instrumentation.

• LLM evaluations within an existing ML workflow →W&B Weave. It's a natural choice for teams already using Weights & Biases who want to keep their work in the same environment.

• Open-source tracing for smaller teams → Comet Opik. A practical option for solo developers and small teams looking for tracing, agent evaluations, and a generous free tier.

• AI testing and regression detection → Confident AI. Best for teams that need application testing, hallucination detection, and CI/CD regression testing with DeepEval.

Braintrust remains worth considering if your team focuses on evaluation-driven development, prompt experiments, regression testing, and quality checks before deployment.

Ultimately, choose the platform that fits your existing tech stack, evaluation needs, deployment preferences, and budget. Compare how each tool handles your actual LLM workflows before committing, rather than choosing based on feature count alone.

Conclusion

All seven tools here are either equal to or worse than Braintrust. What actually decides the choice is narrower than a feature list: whether you need to self-host for compliance, whether your stack already runs on LangChain or Weights & Biases, whether evaluation depth matters more to you than cost visibility, and what you're willing to pay once usage climbs past the free tier. Answer those honestly, and the shortlist narrows itself; no scorecard required.

Before you commit to any of them, do one thing: sign up for the free tier; most of these have a real one, and run your own evaluation set through it for a few days instead of judging it off a demo or a comparison table. Pricing pages change, companies get acquired mid-year, and no amount of reading will tell you how a tool handles your actual prompts and your actual traffic the way using it will. Pick the gap you're trying to close, test the tool built for it, and let your data make the final call.