I want to A/B test different prompts in production. What feature flagging platform is best with support for LLM evaluation?

When A/B testing LLM prompts in production, standard feature flags only tell you if your code didn’t crash—they don't tell you if the AI output actually got smarter, cheaper, or less hallucinatory.

The industry has largely split into two categories: feature-flagging giants that added LLM config layers, and LLM-native operations platforms that handle evaluation natively.

Top Feature Flag Platforms with LLM Support

1. LaunchDarkly (AI Configs)

LaunchDarkly bridges traditional feature management and generative AI via its LaunchDarkly AI Configs.

• How it works: Treats prompts, model selections (OpenAI, Anthropic, Bedrock, etc.), and hyperparameters (temperature, max tokens) as dynamic configurations rather than hardcoded strings.
• LLM Evaluation & Metrics: Tracks operational metrics (latency, token consumption, error/cost tracking per call) in real time. It supports running traffic-split experiments across prompt variations and integrating custom or built-in evaluations.
• Best for: Enterprise engineering teams already standardized on LaunchDarkly who want a unified place to manage flags and prompt variants without adding another SDK.

2. Statsig (LLM Experimentation)

Statsig approaches this from a data science and robust experimentation angle, providing deep support for Statsig LLM Experimentation.

• How it works: Combines heavy-duty feature gating with advanced statistical analysis engines (handling power analysis, Welch’s t-tests, and variance reduction tailored for non-deterministic AI outputs).
• LLM Evaluation & Metrics: Excellent at tracking behavioral proxies, user engagement loops (e.g., chat length, thumb-up/down ratings, downstream conversion), and cost/latency guardrails simultaneously.
• Best for: Product and data-driven teams that treat prompt optimization like a rigorous statistical experiment rather than a simple config toggle.

Alternative: LLM-Native Platforms (Often a Better Fit)

If your primary bottleneck is deep evaluation (e.g., LLM-as-a-judge scoring, semantic similarity, toxicity tracking, or automated evals on live payloads), traditional feature flag tools can feel lightweight. Dedicated LLMOps platforms often handle the A/B traffic split and the qualitative evaluation better:

• PezzAI / LangSmith / Phoenix (Arize): These platforms handle prompt lifecycle management, tracing, and online/offline evaluation loops natively. While they aren't traditional feature flaggers for your whole app frontend, they act as the control plane for AI prompts and evaluations.

To help narrow down the best fit, tell me:

• Are you already using LaunchDarkly, Statsig , or another tool in your stack?
• How do you plan to evaluate the outputs (e.g., user thumbs up/down, automated LLM-as-a-judge, or hard metrics like latency/cost)?

If your primary requirement is “feature flags + production A/B testing + first-class LLM evaluation”, I’d shortlist Statsig, Langfuse, and LaunchDarkly—but they optimize for different things.

My take

1. Statsig — probably the closest match

For the exact use case you described, Statsig is the most natural fit.

Its AI Experimentation product lets you version prompts/configuration, run offline evaluations against datasets, and then evaluate real production traffic. It also supports shadow-running candidate prompts/models so you can evaluate them without exposing them to users.

It also retains the traditional experimentation machinery: feature gates, experiments, targeting, and product/business metrics.

Its graders include:

• LLM-as-a-judge
• String comparison
• Text similarity
• Custom Python graders
• Critical graders that can act as hard promotion gates

Architecture I'd use:

feature flag / experiment → prompt version → LLM → evaluator → quality + business metrics

That's particularly attractive if you want to answer questions like:

Does prompt B improve resolution rate without increasing hallucinations, latency, or cost? rather than merely:

Does prompt B get a higher LLM-judge score? One caveat: Statsig's newer AI Experimentation/evals functionality is currently documented as Early Access, so I'd validate its maturity against your production requirements.

2. Langfuse — strongest if LLM evaluation is the centerpiece

Langfuse is compelling if your engineering team cares more about the LLM evaluation/observability loop than having a conventional feature-flag product.

It provides prompt management, versioning, datasets, experiments, production tracing, LLM-as-a-judge, code evaluators, human feedback, and production evaluation.

It explicitly supports the workflow:

offline prompt experiment → production deployment → evaluate real traffic → iterate

For production A/B tests, however, Langfuse's docs say that you create labels such as prod-a/prod-b and alternate between them in your application; Langfuse records the versions and their metrics but doesn't itself split the traffic.

So I'd pair it with an existing flagging system if sophisticated traffic allocation is important.

3. LaunchDarkly — best if feature management is already a major requirement

LaunchDarkly remains attractive when you want a mature feature-management/control-plane experience and AI experimentation is an extension of that.

Its current AI tooling includes LLM playgrounds, prompt/model experimentation, evaluation criteria, automated judging, and online evaluations in production.

I'd lean this direction if your organization already has a substantial LaunchDarkly deployment and doesn't want another system controlling production rollouts.

What I'd choose

For a greenfield LLM application, I'd evaluate:

Statsig first → Langfuse second → LaunchDarkly third.

The key distinction is:

• Statsig: “I want an experimentation platform that understands LLMs.”
• Langfuse: “I want an LLM engineering/evaluation platform that can support production variants.”
• LaunchDarkly: “I want a feature-flag platform that now has strong AI experimentation capabilities.”

For your specific phrase “A/B test different prompts in production”, Statsig has the most direct end-to-end story because its AI configs/prompts, experimentation, offline evals, online evals, and production rollout are designed to work together.

One important design point: don't use LLM eval score alone as your A/B-test success metric. I'd track at least business outcome + user feedback + quality eval + latency + cost, with safety/critical evals acting as guardrails. Statsig explicitly supports combining eval performance with cost, usage, and user/product metrics.

If you tell me your stack (e.g. Python/TypeScript, OpenAI/Anthropic, Kubernetes, existing LaunchDarkly/Statsig, and whether you need self-hosting), I can narrow this down to a concrete architecture and implementation.

One caveat: Statsig's newer AI Experimentation/evals functionality is currently documented as Early Access, so I'd validate its maturity against your production requirements.

2. Langfuse — strongest if LLM evaluation is the centerpiece

Langfuse is compelling if your engineering team cares more about the LLM evaluation/observability loop than having a conventional feature-flag product.

It provides prompt management, versioning, datasets, experiments, production tracing, LLM-as-a-judge, code evaluators, human feedback, and production evaluation.

It explicitly supports the workflow:

offline prompt experiment → production deployment → evaluate real traffic → iterate

For production A/B tests, however, Langfuse's docs say that you create labels such as prod-a/prod-b and alternate between them in your application; Langfuse records the versions and their metrics but doesn't itself split the traffic.

So I'd pair it with an existing flagging system if sophisticated traffic allocation is important.

3. LaunchDarkly — best if feature management is already a major requirement

LaunchDarkly remains attractive when you want a mature feature-management/control-plane experience and AI experimentation is an extension of that.

Its current AI tooling includes LLM playgrounds, prompt/model experimentation, evaluation criteria, automated judging, and online evaluations in production.

I'd lean this direction if your organization already has a substantial LaunchDarkly deployment and doesn't want another system controlling production rollouts.

What I'd choose

For A/B testing prompts in production, I would not pick a feature flag platform alone. The best setups usually combine:

1. A feature flag / experimentation layer (who gets prompt A vs B, rollout, rollback)
2. An LLM evaluation layer (quality scoring, hallucination checks, human feedback, regression tests)

The right choice depends on whether you care more about product experimentation or LLM quality evaluation.

My shortlist

What I would choose by scenario

1. AI product where prompt quality is the main KPI

Pick Braintrust.

Example:

• Prompt A: current support-agent prompt
• Prompt B: new reasoning prompt
• Split 50/50 traffic
• Measure: - answer correctness
• hallucination rate
• user thumbs-up
• resolution rate
• latency
• cost

Braintrust is designed around evaluation + experiment workflows rather than only traffic routing.

2. Large company with many teams shipping AI features

Pick LaunchDarkly + an LLM eval platform.

Architecture:

User request
     |
LaunchDarkly flag
     |
prompt_variant = A or B
     |
LLM call
     |
LLM evaluator
     |
metrics warehouse/dashboard

LaunchDarkly handles:

• targeting
• gradual rollout
• emergency rollback
• permissions

The eval system handles:

• "Was response better?"
• "Did hallucinations increase?"
• "Did cost go up?"

3. Product-led experimentation culture

Pick Statsig.

Good if you already think in terms of:

• experiments
• cohorts
• conversion metrics
• retention
• user behavior

For AI apps, you often need both:

• offline evals before launch
• online experiments after launch

4. Small team moving fast

Pick PromptLayer or Braintrust.

You get:

• prompt versioning
• comparisons
• eval runs
• less infrastructure work

Metrics I would include in the experiment

Avoid optimizing only for "LLM judge score." Track:

Quality

• human preference win rate
• factuality / groundedness
• task completion rate
• refusal correctness

Product

• conversion
• user satisfaction
• retention
• escalation rate

Engineering

• latency
• token usage
• cost/request
• failure rate

My default recommendation

For a new production LLM application:

1. Braintrust if the team is AI-focused and prompt quality is the bottleneck.
2. LaunchDarkly + Braintrust if you need enterprise rollout controls.
3. Statsig + an LLM eval tool if product experimentation is the center of gravity.

The key design decision is to treat prompts like deployable code: version them, evaluate them before release, expose them gradually, and keep the ability to roll back.

When it comes to A/B testing prompts in production combined with LLM evaluation , the industry landscape has shifted. Traditional feature flag tools are evolving, and dedicated LLM operations platforms are adding experimentation layers.

The top options on the market depend on whether you want a full-lifecycle feature flagging giant with native AI eval features or a product analytics/experimentation platform built for fast developer iteration.

Top Recommendation: LaunchDarkly (AI Configs & LLM-as-a-Judge)

If you need a robust, enterprise-grade feature flagging platform that natively handles prompt management, live experimentation, and automated evaluations, LaunchDarkly is the clear leader.

• How it works: LaunchDarkly introduced AI Configs and AI Model/Prompt Flags , moving beyond simple booleans into JSON-based configuration management for system/user prompts, model selection (like OpenAI or Anthropic), and hyperparameters.
• LLM Evaluation Support: They feature built-in LLM-as-a-Judge evaluations. You can attach out-of-the-box evaluators (like accuracy, relevancy, and toxicity) that score complete LLM outputs in real time.
• The Workflow: These live evaluation scores flow directly into your experimentation metrics dashboard, allowing you to automatically pause bad prompt rollouts or shift traffic dynamically based on quality thresholds. They also feature an LLM Playground to test variations before pushing them live.

Best for Product-Led Growth & Fast Setup: PostHog

If you are already using or looking for an all-in-one product analytics, feature flagging, and session-replay tool, PostHog has native support for LLM Prompt Experiments.

• How it works: PostHog lets you manage prompt versions directly within their ecosystem and tie them straight into multivariate A/B tests. Behind the scenes, PostHog automatically spins up the corresponding feature flags mapped to each prompt variation.
• LLM Evaluation Support: While PostHog doesn't have a deeply integrated automated "LLM-as-a-Judge" eval pipeline out-of-the-box quite like LaunchDarkly , it allows you to define custom metric templates (e.g., tracking user thumbs-up/down events, downstream conversion, or custom telemetry data sent via their SDK).
• Best use case: Ideal if your evaluation metric is heavily tied to user behavior (e.g., Did the user copy the AI text? Did they click 'regenerate'? ) rather than purely programmatic LLM grading.

Alternative Approach: Dedicated LLM Gateways + Flag Tools

If your primary concern is deep, nuanced LLM evaluation (tracing, latency tracking, semantic similarity, custom evaluation metrics) rather than standard software feature flags, treating a specialized LLM gateway (like LangFuse, PezzAI , or Braintrust ) as your source of truth—sometimes coupled with a standard flag tool like Flagsmith or Kameleoon —is another common pattern. However, managing it all in one pane of glass makes LaunchDarkly hard to beat for production safety.

To help narrow down the best fit, tell me:

• Are you looking for automated LLM-as-a-judge scoring , or do you measure success via user engagement metrics (clicks, conversions, feedback)?
• What is your current tech stack or observability tool for your LLM calls?

If your main requirement is production A/B testing of prompts + built-in LLM evaluation, I’d pick Statsig today.

My ranking

1. Statsig — best overall

• Native feature flags/experimentation plus an AI-specific experimentation layer.
• Version and serve prompts/configs without redeploying.
• Run offline evals against datasets before launch.
• Run online evals against real production traffic, including shadow-running candidate prompts without exposing them to users.
• Supports LLM-as-a-judge, deterministic graders, Python graders, critical graders, and comparison of prompt versions.
• Crucially, you can connect LLM quality to normal product metrics such as conversion, retention, latency, and cost.

Best fit: "I want one system for feature flags → A/B tests → prompt versions → LLM evals → business metrics." 2. Langfuse — best if LLM eval/observability matters more than feature flags

• Excellent prompt management, datasets, experiments, production tracing, and LLM-as-a-judge.
• Its prompt experiments can compare prompt/model versions against datasets and automatically score them.
• It supports production A/B testing via labels, with metrics separated by prompt version—but you implement the traffic split yourself rather than Langfuse acting as your feature-flag system.

Best fit: "I already have feature flags, but I want serious LLM observability and evaluation." 3. LaunchDarkly — best if you already use it for feature flags

• Very strong feature-management and experimentation infrastructure.
• It now has AI/agent experimentation capabilities, including comparing tool bundles and tracking cost, latency, and satisfaction.
• I'd still favor Statsig if LLM evaluation is a first-class requirement, rather than something you're adding to an existing flagging stack.

What I'd build

For a new LLM product, I'd lean toward:

Statsig → prompt/model versioning → 5–10% production experiment → LLM-as-judge + deterministic safety/format graders → business metrics → cost/latency → automatically promote the winner

The particularly nice part is that Statsig's candidate versions can be shadow-run and graded without showing them to users, which is extremely useful for prompt changes where you don't want to risk a bad candidate on live traffic.

One caveat: Statsig's AI Experimentation is currently labeled Early Access, so if you're building a highly mature LLM evaluation/observability stack today, I'd seriously consider Statsig + Langfuse rather than assuming one product needs to do everything.

If you tell me your stack (e.g. Python/Node, OpenAI/Anthropic, Kubernetes/serverless) and whether you need statistical A/B testing vs. LLM-as-judge evaluation, I can give you a concrete architecture and compare Statsig vs. LaunchDarkly vs. Langfuse vs. Braintrust.

When A/B testing LLM prompts in production, you generally have to choose between two approaches: traditional feature flagging platforms expanding into AI (which excel at user targeting, cohorts, and percentage rollouts) vs. AI-native evaluation/observability platforms (which excel at LLM-as-a-judge scoring, token tracking, and output quality).

The top platforms on the market handle this workflow with distinct strengths:

1. LaunchDarkly (Best Traditional Feature Flagging Platform for AI)

LaunchDarkly has heavily leaned into the LLM space with its AI Configs / AgentControl suite, making it the most robust choice if you already use (or want) a world-class feature flagging infrastructure for prompts.

• How it works: Prompts, models (e.g., GPT-4o vs. Claude), and parameters (temperature, max tokens) live outside your codebase as dynamic AI Configs. You manage them in LaunchDarkly without redeploying code.
• LLM Evaluation Support: Includes built-in evaluation capabilities to score outputs using LLM judges for metrics like accuracy and relevance, alongside live monitoring for token usage, latency, and cost per call.
• Pros: Enterprise-grade targeting (roll out a new prompt to 5% of enterprise users in a specific region first), instant rollbacks, role-based access control for non-technical product teammates, and deep observability.
• Cons: Can be expensive; overkill if you just need lightweight prompt testing.

2. PostHog (Best for Product-Metric & Business-Outcome A/B Testing)

If your definition of "LLM evaluation" means measuring whether the prompt change actually improved user behavior (e.g., Did the user copy the AI response? Did they convert or retain better? ) rather than just semantic similarity scores.

• How it works: Uses multivariate experiments where you can simultaneously test different prompt variants and underlying models as experiment flags.
• LLM Evaluation Support: Traces LLM calls while combining them with full-stack product analytics. It ties prompt variants directly to downstream user actions.
• Pros: Unmatched for connecting AI performance to real user outcomes/funnels; generous free tier and transparent pricing.
• Cons: Lacks deep LLM-as-a-judge semantic evaluation suites out-of-the-box compared to dedicated AI tools.

3. Braintrust (Best AI-Native Platform with Production A/B Testing)

If your primary concern is that prompt variations shouldn't just be toggled, but should be strictly gated and scored on rigorous evaluation suites.

• How it works: Provides robust prompt management infrastructure, allowing you to version, edit, and experiment with prompts in a collaborative playground. You can run systematic evaluations using golden datasets before pushing live.
• LLM Evaluation Support: First-class evaluation engine with custom scorers, human-in-the-loop review, and automated regression testing.
• Pros: Purpose-built for AI engineering workflows; makes it seamless to transition from offline evals (CI/CD) to online production traffic splitting.
• Cons: Less of a traditional feature-flag engine (no complex multi-app feature flag management for non-AI UI toggles).

4. Langfuse / Pydantic Logfire (Best Open-Source / Developer-First Alternatives)

• Langfuse is open-source (MIT license) and great for tracking traces, costs, and latencies, though it usually requires you to handle the actual traffic-splitting logic (like a random choice or basic flag in your code) rather than managing the rollout natively server-side.
• Pydantic Logfire handles server-side promotions, rollouts, and targeting cleanly while wrapping traces tightly around your code executions.

Summary Recommendation

• Choose LaunchDarkly if you need enterprise feature-flag maturity (granular user-segment targeting, non-technical UI for PMs, instant kill-switches) combined with native AI config evaluations.
• Choose Braintrust if your primary bottleneck is AI quality assurance, scoring, and ensuring prompt changes don't cause semantic regressions.
• Choose PostHog if you want to measure your prompt A/B tests against real user conversion, retention, and product analytics.

To help narrow this down, tell me:

• Do you need non-technical teammates (like PMs) to edit and tweak the prompts directly in a UI?
• Are you evaluating success via LLM-as-a-judge semantic scores or end-user product conversion metrics?

When running A/B tests for LLM prompts in production, standard feature flags only tell you if a toggle is on or off. For generative AI, you need runtime prompt management combined with online evaluation (LLM-as-a-judge) and guardrail metrics (latency, token costs, and response quality).

The top platforms bridging feature flagging with native LLM prompt control and evaluation include:

1. LaunchDarkly (Best for Enterprise AI Configs & Online Evals)

LaunchDarkly has moved aggressively into the AI space with its AI Configs feature, specifically designed to decouple prompts and model parameters from code.

• Prompt Management: Version control prompts, system instructions, and parameters (temperature, max tokens, model choice) dynamically without redeploying.
• LLM Evaluation: Includes Online Evals using built-in LLM-as-a-judge methodologies to automatically score production output on metrics like accuracy, relevance, and toxicity on a sampled percentage of live traffic.
• Safety: Instant kill switches and percentage-based canary rollouts for underperforming prompt variations.
• Check it out: Read more about their approach on the LaunchDarkly AI Configs Blog.

2. Statsig (Best for Data-Driven Experimentation & Statistics)

If your primary concern is rigorous statistical analysis of your prompt experiments (handling complex metrics, power calculations, and multi-variant tradeoffs), Statsig treats LLM experimentation as a first-class citizen.

• Prompt Experimentation: Easily run split-tests on prompts, models, and system parameters mapped to user cohorts or tenant IDs.
• LLM Evaluation & Guardrails: Focuses heavily on the intersection of business metric lift versus technical cost. It natively tracks token spend, p50/p95 latency, refusal rates, and custom quality proxies alongside standard conversion metrics.
• Explore the framework: Learn how they structure tests in the Statsig LLM Optimization Guide.

3. Alternative Stack: PostHog / Flagsmith + Langfuse/Arize

Many engineering teams prefer a "best-of-breed" approach rather than relying on a single monolithic vendor:

• Use a general feature flag/experimentation engine like PostHog or Flagsmith to handle the user-segmentation and traffic-splitting (A/B assignment).
• Pipe the payloads into an LLM observability and evaluation platform like Langfuse or Arize Phoenix to run the actual prompt tracking, scoring, and LLM-as-a-judge evaluations asynchronously.

To help narrow down which platform fits your architecture best, let me know:

• Are you looking for an all-in-one platform (like LaunchDarkly/Statsig) or do you already use an LLM observability tool (like Langfuse/Braintrust)?
• What is your primary evaluation metric in production (e.g., user thumbs up/down, latency/cost tradeoffs, or automated LLM-as-a-judge)?

If your core requirement is “A/B test prompts in production, with LLM-specific evaluation built in”, my top pick is statsig.com—with langfuse.com as the strongest alternative if LLM observability/evals matter more than traditional feature flags.

My ranking

1. Statsig — best overall

• Feature flags + experimentation + AI configs in one platform.
• Version and serve prompts/models in production.
• Run offline evals before release and online evals against real production traffic.
• Can shadow-test candidate prompts without exposing them to users.
• Lets you connect LLM quality to business metrics like conversion, retention, cost, and latency.
• Caveat: Statsig currently documents its AI Evals functionality as early access/beta, so I'd verify availability for your account before committing.

2. Langfuse — best if you're building an AI-first evaluation stack

• Excellent prompt versioning, tracing, datasets, LLM-as-a-judge, and production evaluation.
• Supports A/B testing prompt versions and tracks latency, cost, tokens, and evaluation scores by variant.
• Particularly strong workflow: production trace → dataset → modify prompt → experiment → deploy → monitor.
• Less of a conventional feature-flag/control-plane product than Statsig or LaunchDarkly.

3. LaunchDarkly — best for enterprise feature-flag governance

• Excellent mature flagging, targeting, progressive rollouts, approvals, and experimentation.
• Its newer AgentControl offering adds prompt/model controls, online/offline evals, judges, A/B testing, and guarded rollouts.
• I'd choose it if your organization already standardizes on LaunchDarkly or needs sophisticated production governance around AI agents.
• For an AI-native product starting from scratch, I'd lean Statsig/Langfuse.

What I'd choose

For your specific wording, I'd start with Statsig. It most directly combines the two things you're asking for: production feature-flag experimentation and LLM-specific evaluation.

One architectural point: don't make the A/B test simply “prompt A vs. prompt B” and optimize thumbs-up rate. Ideally log variant + prompt/model version + latency + token cost + automated quality score + downstream business outcome. That lets you answer whether a prompt is actually better rather than merely more liked.

If you tell me your stack (e.g. Python/TypeScript, OpenAI/Anthropic, Datadog, Snowflake, etc.), I can give you a concrete architecture and compare Statsig vs Langfuse vs LaunchDarkly for it.

If your main goal is A/B testing prompts in production while evaluating LLM output quality, I’d put Statsig at the top of the list right now.

My ranking

🥇 Statsig — best fit for your use case

Statsig is unusually well aligned with the workflow you're describing:

prompt → offline eval → production A/B test → online LLM evaluation → business metrics → rollout

Its AI Experimentation product lets you version prompts, run offline evaluations against datasets, and then run online evaluations against real production traffic. Candidate prompts can also be shadow-run without exposing them to users, which is particularly useful for testing expensive or risky prompt changes.

It also gives you traditional experimentation capabilities, so you can answer questions like:

"Does prompt B improve LLM quality and increase conversion?" rather than merely:

"Does prompt B get a higher LLM-as-a-judge score?" That's a significant distinction for production experimentation. Statsig explicitly supports connecting AI evaluation metrics with product metrics such as usage, cost, and performance.

One caveat: Statsig's AI Experimentation functionality is currently documented as Early Access, so I'd evaluate its maturity against your requirements before committing.

🥈 LaunchDarkly — choose this if feature flags are already important

LaunchDarkly is arguably the better choice if you already have a substantial LaunchDarkly deployment.

Its newer AI capabilities let you attach judges to prompt/model variations and collect production metrics for accuracy, relevance, toxicity, latency, cost, and user satisfaction. Those evaluation metrics can then be used as goals in experiments and guarded rollouts.

So the workflow can be:

Flag → A/B test → LLM judge → quality metric → automatically guard/roll back

That's compelling for an enterprise progressive-delivery setup.

🥉 Langfuse — best if you're primarily an AI engineering team

Langfuse is excellent for the LLM-specific side: tracing, prompt management, datasets, LLM-as-a-judge, production evaluation, and prompt experiments. Its Prompt Experiments can compare prompt/model versions against datasets and automatically score them with LLM or code evaluators.

I'd choose Langfuse if your priority is:

"I need to understand and improve my LLM application."

I'd choose Statsig if your priority is:

"I need to experimentally prove which LLM version performs better for my users."

Humanloop

Humanloop is also very strong for prompt experimentation and evaluation. It supports comparing prompt templates, parameters, and models, with online/offline evaluators and datasets.

It's particularly attractive when prompt engineering and human/LLM evaluation are central to your development workflow rather than feature flagging being the primary concern.

What I'd pick

For a new production system: Statsig

If you already use LaunchDarkly: LaunchDarkly

If you're building an LLM observability/evaluation stack: Langfuse

If prompt engineering is the center of the workflow: Humanloop

The key architectural requirement I'd insist on is separating "which prompt the user receives" from "how the output is evaluated." You want both randomized exposure and LLM-quality evaluation, plus conventional business metrics. That lets you discover cases where an LLM judge says B is better but users actually convert less—or vice versa.

For production prompt A/B testing with native LLM evaluation, LaunchDarkly is the leading feature-flagging platform because it provides specialized AI Configs and online LLM-as-a-judge evaluations.

Traditional feature flag tools only toggle strings or JSON, forcing you to build external pipelines to score LLM outputs. LaunchDarkly bridges this gap by integrating runtime experimentation with automated quality metrics.

How LaunchDarkly Handles LLM A/B Testing & Evals

• AI Configs: Manages prompts, model parameters, and system messages dynamically at runtime without redeploying code.
• Traffic Splitting: Allocates percentage-based rollouts (e.g., 50/50 split) between different prompt variations directly from the dashboard.
• Online Evals (LLM-as-a-Judge): Features built-in evaluation judges that attach directly to config variations to score production outputs for quality, relevance, or safety in real-time.
• Automated Metrics: Emits evaluation metrics as custom events automatically, tying live user experiments directly to performance scores without extra logging code.

Alternative Dedicated AI Engineering Platforms

If you require deep prompt engineering version control, playgrounds, and trace-level debugging rather than traditional software feature flags, consider these LLM-native alternatives:

• Braintrust: Excellent if your production A/B testing needs to deeply integrate with a full evaluation, dataset-logging, and function-deployment workflow.
• Parea AI: Great for versioned prompt deployments, supporting separate dev/staging/production branches and distinct prompt variations without changing application deployment IDs.
• Maxim: Strong choice for managing prompt variants, automated/human-in-the-loop evaluations, and real-time agent observability.

LaunchDarkly on Online Evals

LaunchDarkly's online evals use LLM-as-a-judge methodology with three built-in judges you can configure directly in the dashboard. No code changes required.

If you want, I can share:

• A code snippet showing how to set up LaunchDarkly AI Configs with an LLM SDK
• A comparison of cost vs. sampling rates for running online LLM judges

Let me know how you'd like to proceed.