Top 10 Braintrust Alternatives for Production Evals (2026)

An agent answers a billing question with last quarter's refund policy. Every span is green, latency is normal, and the trace looks identical to the thousand that came before it. Nothing in the request path flags it, because being wrong is not an error state.

The only thing that catches it is a score on the output itself, run against the traffic users actually sent rather than a test set written in advance. Most platforms in this category can do that. What separates them is what it costs to leave it on.

Braintrust prices tracing and scoring on separate meters, so watching production and evaluating it climb independently as traffic grows. Release-gated evals against curated datasets barely touch the score meter. Scoring live traffic runs straight into it.

This guide covers what Braintrust does, how its pricing works, and ten Braintrust alternatives compared on what it takes to score live requests.

What Is Braintrust?

Braintrust is an evaluation platform for LLM applications, with observability attached. The core unit is an eval: a dataset, a task, and a set of scorers, run as an experiment so a prompt or model change produces a comparable number. Experiment review is where the product concentrates, with persistent baselines, row-level regression sorting, and repeated trials for non-deterministic outputs. A GitHub Action runs the same evals on pull requests and blocks a merge when scores fall below a threshold you set.

Instrumentation runs through native SDKs or OTLP, and there is a gateway that routes across major providers with response caching, included on every plan. Tracing exists to supply eval inputs rather than to answer what happened during an incident, which is a coherent design goal and a different one from tracing built for production debugging.

Braintrust Pricing

Braintrust publishes three tiers, and users, projects, playgrounds, experiments, and datasets are unlimited on all of them. What separates the plans is throughput and governance rather than seat count.

• Starter - Free. $10 in model credits then token rates, 1 GB of processed data then $4/GB, 10,000 scores then $2.50 per thousand, and 14-day retention. Human review scores are capped at one per project.
• Pro - $249/month flat. $100 in credits, 5 GB then $3/GB, 50,000 scores then $1.50 per thousand, and 30-day retention extendable to 180 days at $0.50/GB/month. Adds custom charts, environments, and basic role-based access control.
• Enterprise - Custom. Adds custom retention policies, automated S3 export, SAML single sign-on, a business associate agreement, an uptime SLA, and on-premise or hosted deployment. Self-hosting sits here and the price is not published.

Processed data counts gigabytes across inputs, outputs, prompts, metadata, spans, datasets, and attachments, so cost follows the shape of your traffic rather than its volume. A multi-step agent carrying retrieved context on every turn can clear an allowance faster than the request count suggests.

The score meter runs independently of that, and it decides how much of production you can afford to check. Braintrust counts a score as any output run through an LLM judge, an autoeval, or a custom code scorer, so one judge pointed at every response writes one score per request, and a second evaluator doubles it. Because the meter tracks the test suite as well as traffic, a team can move up a tier without gaining a user. Retention past the included window bills per gigabyte monthly to a 180-day ceiling, which puts a year of history out of reach on Pro at any price.

Why Look for a Braintrust Alternative?

The pricing structure above has consequences past the invoice, and a few of them decide whether the platform fits the job rather than what it costs. Here are some reasons people might look for a Braintrust alternative:

• Scoring live traffic bills separately from tracing it - Processed data and scores are two meters moving in the same direction, so raising evaluation coverage raises the bill independently of traffic growth. On platforms that count a score the same as any other event, coverage is a configuration choice instead.

• There is no tier between free and $249 - A team that outgrows 1 GB moves straight to Pro whether it needed 1.2 GB or 4 GB, and the same jump lands on teams still deciding whether the platform fits.

• Retention is short and capped - 14 days on Starter and 30 on Pro, extendable at $0.50 per gigabyte per month to a 180-day ceiling. Regression work that compares this quarter against last quarter needs Enterprise.

• Getting data out is an Enterprise feature - Automated S3 export sits on the top tier and there is no direct query access to the underlying store, so portability planning starts as a procurement question.

• The packaging is evaluation-first - Tracing is built to feed the eval workflow, which is coherent design and also means teams whose actual problem is debugging a production run are paying for an experiment workbench they use as a logging system.

Whether any of that is disqualifying depends on which half of the loop you spend your time in. The ten platforms below are compared on that basis: what each one does when the traffic is live.

10 Best Braintrust Alternatives for Production Evals

The list runs from platforms where scoring live traffic is included in the core meter toward ones where it sits behind a seat count or an Enterprise contract. Here's our list of the 10 best Braintrust alternatives:

1. Respan

Respan runs the gateway, tracing, evaluations, and prompt management on one data plane, which means a score in production already carries the routing decision that served the request and the prompt version that produced it. Route, observe, and evaluate every LLM call.

The loop closes inside the product. A bad score becomes a dataset, an experiment, a new prompt version, and a proven fix without exporting anything or opening a second tool. That matters most during an incident, when the alternative is holding a score in one system and the record of which model served the request in another.

• Score live traffic, not just test sets - LLM judges, deterministic code checks, and human reviewers compose into one evaluator, deployed offline before shipping and online against sampled production spans, filtered by status, customer, or thread.
• See what your agents actually did - Every LLM call, tool run, retrieval, and agent turn becomes a span in one nested trace, each with its own input, output, latency, and cost.
• Route across 1,000+ models - One OpenAI-compatible endpoint with ordered fallback chains, automatic retries, and load balancing, at roughly 10ms added P95.
• Know where every dollar goes - Spend broken down by model, request, and end customer, with soft caps that warn and hard caps that stop spend at the ceiling.
• Catch problems before customers do - Monitors on cost, errors, latency, tokens, or eval scores alert Slack, email, or a webhook the moment they breach.
• Ship prompts without shipping code - Versioned templates deploy instantly, so v3 serves production while v4 is in progress.
• Attack your own agent first - Red Team runs adversarial campaigns against the OWASP Top 10 LLM risks and reports confirmed findings by severity.

That consolidation holds at volume. Retell AI runs more than a million LLM calls an hour through Respan without sampling, and scaled from 5M to 500M+ monthly API calls on it.

Online evals: Judges, code checks, and human review run on sampled live spans, with the score attached to the trace and the model that served it.

Meter: Logs and scores, counted separately from seats. Additional logs run $8 per 100,000 and additional scores $1 per 1,000.

Pricing: Free covers the full platform at 100,000 logs, 1,000 scores, 5 datasets, 2 evaluators, and 5 prompts with unlimited seats and 7-day retention. Team is $199 per month billed yearly, lifting scores to 10,000, making datasets, evaluators, and prompts unlimited. Enterprise adds custom retention, SAML, a 99.99% uptime SLA, and a HIPAA BAA.

Because the meter is logs rather than people, the bill tracks what the application does instead of how many colleagues want to look at it. Best fit is teams running agents in production who would rather debug one system than correlate four.

2. Langfuse

Langfuse is an open-source LLM engineering platform covering tracing, prompt management, datasets, evaluations, and metrics, with an MIT-licensed core that runs the full product on your own infrastructure.

For this comparison the meter is the interesting part. A unit is one trace, one observation, or one score, which means a score costs the same as any other event rather than sitting on a meter of its own. Turning up evaluation coverage raises the unit count the way adding a retrieval step would, so the decision about how much of production to check is an ordinary capacity question.

LLM-as-a-judge and deterministic code evaluators both run online against the same observations table production writes to, with rules controlling targeting and sampling. Because the evaluation and the trace share one data model, moving from a failing production output to a dataset row is a click rather than an export.

Running it yourself means operating Postgres, ClickHouse, Redis, and object storage as separate services, which is the architecture that lets it scale and also the reason it is more to run than a single-binary tool. If ClickHouse is outside what your team can operate, that constraint puts you on Cloud.

Online evals: Judges and code evaluators run on live observations, scored into the same table as the traces.

Meter: Units, where a trace, an observation, and a score each count as one. Overage is $8 per 100,000 on paid tiers.

Pricing: Hobby is free with 50,000 units, 30-day access, and 2 users. Core is $29 for 100,000 units, 90-day access, and unlimited users. Pro is $199 with three-year access and SOC 2 and ISO 27001 reports, and Enterprise is $2,499. Enterprise SSO and fine-grained RBAC sit in a $300 monthly Teams add-on on top of Pro, so a governed deployment is nearer $499 than $199.

The best fit for tools like Langfuse are teams with data-residency requirements and the appetite to run the infrastructure.

3. MLflow

MLflow is an Apache 2.0 project governed by the Linux Foundation, and its ownership story is materially different from every venture-backed platform on this list. Experiment tracking, a model registry, and deployment tooling serve classical ML, while OpenTelemetry-based tracing, evaluation, prompt management, and an AI Gateway serve LLM applications.

Evaluation goes deeper than the license suggests. Built-in judges cover common quality dimensions, judges can be versioned, and alignment optimizers refine judge instructions against human feedback so the scorer improves rather than staying fixed at whatever the first prompt produced.

Its gateway handles routing, traffic splitting, fallback chains, rate limiting, budget alerts, and credential management, so MLflow covers the request path and the record in a way most open-source options do not. Instrumentation is a single autolog() call across a wide set of frameworks, and language support extends past Python and TypeScript to Java and R.

There is no per-score meter because there is no meter, which changes the evaluation question entirely. What you pay instead is operational: the tracking server, the backing database, and the object storage are yours to deploy, upgrade, and keep running.

Online evals: Scorers run against traced production data on infrastructure you operate.

Meter: None. Cost is infrastructure and engineering time.

Pricing: Free under Apache 2.0 with no tiers, no per-trace fees, and no feature gating between the open-source release and what you can run. Managed offerings exist through Databricks and other cloud platforms for teams that would rather not operate it.

Without an existing MLflow footprint it is heavier to adopt than a purpose-built LLM tool, and its center of gravity remains the ML lifecycle. Best fit is organizations running both classical ML and LLM workloads that want one system across them.

4. LangWatch

LangWatch is an Apache 2.0 platform built around testing agents before they ship and monitoring them after. Scenario testing generates realistic multi-turn conversations with a simulated user and a judge that can pass or fail a run at any turn, and the same suites run locally or in CI.

On the production side, Monitors run continuous evaluations against live traffic with alerting when scores drop, evaluation can score a whole thread rather than a single message, and built-in evaluators cover hallucination, toxicity, PII, and LLM-as-a-judge scoring. Guardrails run the same evaluators inline in application code.

LangWatch's meter is worth reading carefully, because they count an evaluation as a billable event alongside every LLM call, tool call, and retrieval. That is a different structure from Braintrust's separate score meter, though it lands in similar territory: scoring more of production consumes the allowance faster. The €5 per 100,000 rate past the included volume keeps the slope shallow.

Self-hosting runs the full platform through Docker Compose on your own ClickHouse, and the AI Governance layer with virtual keys, budgets, and routing policies sits on Enterprise.

Online evals: Monitors evaluate live traffic continuously, with thread-level scoring and alerting on drops.

Meter: Events, where an evaluation step counts the same as an LLM call, plus a per-seat charge.

Pricing: Developer is free with 50,000 events a month, 14-day data access, 2 users, and 3 custom evals. Growth is €29 per core-seat per month with 200,000 events included then €5 per 100,000, 30-day retention extendable at €3 per GB, unlimited lite-users, and unlimited simulations, evals, and prompts. Enterprise adds self-hosting, custom retention, SSO, RBAC, and audit logs.

Best fit is teams testing multi-turn or voice agents who want the simulation suite and the production monitors in one place.

5. Confident AI

Confident AI is an evaluation-first platform built on DeepEval, the open-source evaluation framework from the same team. The metric library is the draw: single-turn and multi-turn coverage, custom G-Eval metrics defined in natural language, and deterministic code-based metrics, all open source and runnable locally before you touch the cloud product.

Confident AI is shaped around cross-functional workflows more than most tools here, with no-code evaluation runs, annotation queues, custom annotation forms, and dataset editing in the browser. Production traces feed back into datasets for the next test cycle, and multi-turn simulation generates conversation test data rather than requiring someone to write it by hand.

Tracing exists and integrates with OpenTelemetry, though like Braintrust it serves the eval workflow rather than functioning as a general debugging surface. Red teaming and AI governance are separate modules on top of Enterprise rather than part of the base product.

The free plan's constraint is a rate limit rather than a volume one, which is unusual and easy to trip over. Five test runs per week is roughly one a day, so a team iterating on a prompt through a morning is locked out by the afternoon regardless of how little traffic it has.

Online evals: Production traces are scored automatically against the metric library, with alerting on score drops.

Meter: GB-months of trace spans, priced at $1 per GB-month past the allowance. Scoring is not separately metered.

Pricing: Free covers 2 seats, 1 project, 1 GB-month of spans, and 5 test runs per week. Starter is $200 per month for unlimited seats, 5 projects, and 5 GB-months. Team is $2,000 and adds SOC 2, SSO, custom RBAC, and Git-based prompt workflows. Enterprise covers on-premise deployment, HIPAA, and the red teaming and governance modules.

Best fit is teams where QA or product people need to run evaluation cycles without going through engineering for each one.

6. Opik by Comet

Opik is Comet's Apache 2.0 platform for AI observability and evaluation, covering tracing, agent execution graphs, test suites with pass and fail assertions, datasets, experiments, prompt management, and online evaluation on production traces. The open-source build runs the same codebase as the hosted product, which is a meaningful distinction in a category where open source sometimes means a stripped core with the useful parts held back.

Built-in metrics cover answer relevance, context precision, task completion, and hallucination, with custom scoring logic available when the built-in checks miss. An agent optimizer runs prompt optimization algorithms against your own evaluation results, which is closer to MLflow's approach than to a playground.

Scoring runs on the span meter rather than on a meter of its own, and at $5 per 100,000 additional spans the marginal cost of checking more of production is low. Retention on the free tier is 60 days, which is longer than most of this roster and long enough for real regression work.

Opik's framing assumes an ML lifecycle you may not have, since it sits inside Comet's broader platform alongside experiment tracking and a model registry. Guardrails are available on the self-hosted open-source build rather than the cloud tiers, so wanting both guardrails and managed hosting means picking one.

Online evals: Evaluation rules run on production traces, with results stored alongside the spans.

Meter: Spans. Additional spans are $5 per 100,000, and retention beyond 60 days costs $29 per 100,000 spans.

Pricing: Free Cloud covers 10 team members, 25,000 spans a month, and 60-day retention. Pro is $19 a month for up to 50 members and 100,000 spans. Enterprise brings unlimited members, flexible deployment, SSO, RBAC, and compliance certifications.

Best fit is mid-sized teams that want a genuinely open core and cheap span volume, with the 25,000-span ceiling the thing that ends the free tier.

7. Weights & Biases Weave

Weave is the LLM layer of Weights & Biases, and its trace model treats sessions, turns, steps, tools, and sub-agents as first-class concepts rather than flattening an agent run into a list of spans. Pre-built scorers cover hallucination, toxicity, bias, PII, context relevance, and coherence, results group into leaderboards for comparison across runs, and Monitors track quality signals on live traces with alerts to Slack or a webhook.

For teams already paying for W&B, adding Weave means one fewer vendor and one account covering experiment tracking, the model registry, tracing, and evals together.

Weave bills ingested bytes, and the overage rate is the number to model before committing. Additional ingestion runs $0.10 per megabyte, which works out around $100 per gigabyte against Braintrust's $3, so a team leaving Braintrust because of the processed-data meter should check its own volume carefully before assuming this is the cheaper side of the trade. Verbose agent traces clear the included 1.5 GB quickly.

There is also an eligibility condition on the middle tier. Pro is intended for organizations with fewer than 50 employees, and companies past that threshold are moved to Enterprise, which makes the published $60 a starting point rather than a plan a growing company stays on.

Online evals: Monitors score live traces continuously with alerting when a threshold trips.

Meter: Ingested bytes, with additional Weave ingestion at $0.10 per MB and storage at $0.03 per GB.

Pricing: Free is $0 with 1 GB a month of Weave ingestion, 5 GB of storage, and up to 5 model seats. Pro starts at $60 a month with 1.5 GB of ingestion and 100 GB of storage. Enterprise is custom and adds single tenancy, HIPAA, SSO, SCIM, audit logs, and customer-managed encryption keys.

Best fit is ML teams already standardized on W&B whose LLM work is one workload among several.

8. LangSmith

LangSmith is LangChain's commercial platform for tracing, evaluation, and now deployment, sandboxes, and an engine that analyzes recurring failures on a schedule and proposes fixes. It renders full execution trees including tool selections, retrieved documents, and model parameters, and annotation queues route selected traces to domain experts whose output feeds evaluation datasets directly.

With tools like LangSmith, online and offline evaluators both run, so scoring live traffic is supported. What separates it from most of this roster is the seat meter running alongside the trace meter, which means the bill tracks team size independently of usage. At $39 per seat that discourages exactly the cross-functional access that makes annotation queues valuable in the first place, and it puts a ten-person team at $390 a month before a single trace over the allowance.

Trace overage rates change with the retention setting, at $2.50 per thousand on 14-day retention or $5.00 per thousand on the 400-day tier. Since the traces worth keeping are the ones carrying evaluator scores and human feedback, the features adopted as a team gets more serious are the same ones pushing data onto the more expensive rate. Work performed on the engine, deployments, and sandboxes bills in compute units at $1.50 each, and stored data in storage units at $1.00 each, so the invoice has more inputs than seats and traces alone.

Online evals: Online and offline evaluators run, with annotation queues for human review.

Meter: Seats plus traces, with compute and storage units on top for engine and deployment work.

Pricing: Developer is free with one seat and 5,000 base traces a month. Plus is $39 per seat with 10,000 base traces. Enterprise is custom and is where self-hosted and hybrid deployment live.

Best fit is teams whose stack is LangChain today and will still be LangChain in two years.

9. Galileo

Galileo is an evaluation and guardrails platform built around scoring production traffic continuously rather than only in test runs. Its Luna small language models handle evaluation at low latency, which is the design answer to the cost of calling a frontier model as a judge on every request, and built-in metrics cover retrieval systems, agents, safety, and security.

The continuous-scoring model is the reason to look at it here, since scoring is metered against traces rather than charged per score. Unlimited custom evaluators on the free plan means an eval suite can be built out before spending anything, which is not common on this roster.

The structure of the tiers is where care is needed. Runtime guardrails are the reason most teams evaluate Galileo, and neither tier with a published price includes them. Self-hosting, VPC and on-premise deployment, and SSO sit on the same tier, which makes the practical entry point a sales conversation rather than the $100 the pricing page suggests, unless evaluation alone is what you need.

The center of gravity is evaluation and guardrails rather than prompt iteration or trace debugging, so a team whose main problem is understanding why an agent took a wrong turn will find the tracing surface narrower than on a debugging-first platform.

Online evals: Continuous scoring against live traffic using purpose-built evaluation models.

Meter: Traces, with usage-based overages. Scoring is not separately metered.

Pricing: Free covers 5,000 traces a month with unlimited users and unlimited custom evaluators. Pro is $100 per month billed annually or $150 billed monthly, raising the allowance to 50,000 traces and adding standard RBAC and advanced analytics. Enterprise is custom and is where runtime guardrails, VPC and on-premise deployment, and SSO live.

Best fit is teams whose requirement is packaged evaluation and inline guardrails, with the budget for the tier where the guardrails actually sit.

10. Arize Phoenix

Phoenix is Arize's open-source tracing and evaluation project, built on OpenTelemetry and OpenInference rather than a proprietary trace format. It runs locally, in Docker, or on Kubernetes, and the same instrumentation can point at another backend later.

Arize AX is the managed platform alongside it, covering agent trajectory visualization, session-level evaluation across multi-turn conversations, custom dashboards and monitors, datasets, and prompt versioning, with Signal clustering failures into a ranked issue list.

Because experiments and traces share one data model, a production failure moves into a repeatable test without a separate export step. Evaluations, experiments, and human annotations are all unlimited on the AX free tier, which means the number of things you score is not what the meter reads.

Two constraints shape the fit. Retention is 15 days on AX Free and 30 on Pro, which is short for regression work that compares across releases. And custom code evaluators sit on Enterprise, so the unlimited evaluations on the lower tiers cover the evaluators Arize provides rather than the ones you write, which matters if your quality criteria are specific to your product.

Online evals: LLM-as-a-judge and custom scoring run on spans, unlimited on the published AX tiers.

Meter: Spans and ingestion. Additional spans are $10 per million and additional ingestion $3 per GB.

Pricing: Phoenix self-hosts free with no usage caps. AX Free covers 25,000 spans a month, 1 GB of ingestion, 15-day retention, and unlimited users. AX Pro is $50 per month for 50,000 spans, 10 GB, and 30-day retention. AX Enterprise is custom and adds self-hosted deployment, SSO, audit logs, HIPAA, and custom code evaluators.

Best fit is teams for whom OpenTelemetry portability is the deciding constraint, or organizations running classical ML and LLM workloads under one vendor.

FAQ

What is Braintrust used for?

Braintrust is used for evaluating LLM applications and, secondarily, for observing them. Teams define an eval as a dataset, a task, and a set of scorers, run it as an experiment, and compare prompt and model versions against the resulting scores. A GitHub Action runs those evals on pull requests and blocks merges when quality drops, production traces convert into dataset entries, and a shared playground lets non-engineers compare outputs. It is strongest where the workflow is release-gated testing and weakest where the primary need is debugging what an agent did in production.

Braintrust vs LangSmith: which should you use?

Braintrust if evaluation results need to control what ships, and LangSmith if your application is built on LangChain or LangGraph. Braintrust's evals run in CI and gate the merge automatically, with a flat $249 on Pro and unlimited users. LangSmith gives native rendering of LangChain execution structure from a single environment variable, along with annotation queues and an engine that proposes fixes, but bills $39 per seat on top of trace volume, so the cost tracks team size as well as usage.

The question underneath is usually how many products you want to run, since neither one routes traffic. A team that also needs failover, caching, and per-key spend limits is adding a gateway either way. Respan runs the gateway, the traces, and the evals on one platform, with judges and code checks scoring live production spans and the free plan covering 100,000 logs and 1,000 scores with unlimited seats.

How much does Braintrust cost?

Starter is free with 1 GB of processed data, 10,000 scores, $10 in model credits, and 14-day retention. Pro is a flat $249 per month for 5 GB, 50,000 scores, $100 in credits, and 30-day retention, with unlimited users on both. Enterprise is custom priced.

Overages are where the structure shows. Processed data bills $4 per GB on Starter and $3 on Pro, scores bill $2.50 and $1.50 per thousand respectively, retention past the included window costs $0.50 per GB per month up to 180 days, and model credits give way to token rates once spent. Because those meters run independently, a team can move up a tier by increasing evaluation coverage without adding a single user.

Is Braintrust open source?

No. The platform, the control plane, and the Brainstore storage engine are closed source, though the SDKs and the autoevals scorer library are published on GitHub. Self-hosting requires an Enterprise contract and the pricing is not disclosed.

If an open license is a requirement rather than a preference, the options on this list that publish the whole platform are MLflow under Apache 2.0 with Linux Foundation governance, Opik under Apache 2.0, LangWatch under Apache 2.0, and Langfuse with an MIT core. Teams with compliance obligations who would rather not run infrastructure have a third path in a managed platform with the certifications already in place, which is what Respan offers with SOC 2, HIPAA and a BAA, GDPR, ISO 27001, PII masking, and an omit-logs option for fields that should never leave your environment.

What is the best Braintrust alternative for evaluating production traffic?

Respan, for most teams whose evaluation work happens after the deploy rather than before it. Judges, deterministic code checks, and human review run against sampled live spans filtered by status, customer, or thread, and the score lands on the same record as the trace and the routing decision that produced it, which removes the correlation step that consumes the first hour of an incident. Scores meter at $1 per thousand past the free allowance rather than on a meter that compounds with trace volume.

The alternatives are strong in narrower places. Langfuse counts a score as one unit like any other event, which makes coverage a capacity question. Opik pairs cheap span volume with an Apache 2.0 core. Galileo scores continuously with purpose-built evaluation models. MLflow removes the meter entirely in exchange for running the infrastructure yourself.