I want to compare the performance of a new fine-tuned model against our production model. What is the best platform for model A/B testing or canary deployments?

If your goal is “put fine-tuned Model A next to production Model B, send controlled traffic to each, and decide whether A is actually better,” I’d separate the problem into evaluation and traffic management.

My recommendation

For most LLM teams: LangSmith is the strongest starting point. It supports offline experiments, side-by-side/pairwise evaluation, historical-data backtesting, and online evaluation of production traces.

The architecture I'd use

Don't rely on a simple 50/50 A/B test alone. I'd do:

1. Offline benchmark first

Take a representative dataset from production—especially difficult/edge cases—and run both models against exactly the same inputs.

Measure things such as:

• Task success / correctness
• Human preference or pairwise win rate
• LLM-as-judge score
• Hallucination/error rate
• Structured-output validity
• Latency
• Token consumption / cost
• Safety/guardrail violations

LangSmith explicitly supports experiments that compare different application/model configurations and pairwise evaluation, as well as backtesting against historical production data.

2. Shadow deployment

Send a copy of production requests to Model A, but don't expose its response to users.

This lets you compare:

Production response → Model B Shadow response → Model A while measuring latency, errors, token usage, and quality on realistic traffic.

3. Canary

Then give Model A something like:

• 1% → 5% → 10% → 25% → 50%

with automatic rollback if key metrics deteriorate.

For this part, I wouldn't necessarily make the evaluation platform your traffic router. A model gateway/service mesh/API layer can handle deterministic traffic allocation, while LangSmith/Braintrust/etc. handles the “is A better?” question.

4. Online evaluation

Sample real Model A and B responses and run automated evaluators. LangSmith supports online evaluators on production traces, including code-based and LLM-as-judge evaluations, with sampling controls.

Which one I'd choose

• You care primarily about model quality/evaluation: LangSmith
• You want an experimentation-first LLM platform: Braintrust
• You want open-source/self-hosted observability: Arize Phoenix
• Your models are deployed through GCP: Vertex AI
• Your models are deployed through AWS: SageMaker
• You already have a model gateway: keep the gateway for canary routing and add LangSmith/Braintrust for evaluation.

One important distinction: A/B testing and canary deployment aren't quite the same thing. A canary asks “is this new version safe to expose to some traffic?”; an A/B test asks “does this version perform better than the incumbent?” For a fine-tuned LLM, you ideally want both, with pairwise quality evaluation layered on top of conventional operational metrics.

If you tell me where Model A/B are hosted (OpenAI, vLLM, SageMaker, Vertex, Kubernetes, etc.) and whether you're evaluating a chatbot, RAG system, or API, I can recommend a concrete stack and architecture.

• Task success / correctness
• Human preference or pairwise win rate
• LLM-as-judge score
• Hallucination/error rate
• Structured-output validity
• Latency
• Token consumption / cost
• Safety/guardrail violations

LangSmith explicitly supports experiments that compare different application/model configurations and pairwise evaluation, as well as backtesting against historical production data.

2. Shadow deployment

Send a copy of production requests to Model A, but don't expose its response to users.

This lets you compare:

Production response → Model B Shadow response → Model A while measuring latency, errors, token usage, and quality on realistic traffic.

3. Canary

Then give Model A something like:

Comparing a fine-tuned model to a production baseline requires handling traffic routing, latency tracking, cost monitoring, and qualitative/quantitative evaluations. The best platform depends heavily on whether you need an AI Gateway (for live traffic splitting/canary routing) or an LLM Observability/Evaluation Platform (for scoring the results).

The top platforms for model A/B testing and canary deployments are categorized below by their primary strengths:

1. AI Gateways (Best for Traffic Splitting, Shadowing, and Canary Rollouts)

These sit between your application and your models, letting you split traffic dynamically (e.g., 90% Model A, 10% Model B canary) without changing core application code.

• Portkey : Best for a hosted split-traffic UX. It allows weighted routing, fallback mechanisms, and real-time logging of user feedback tied directly to specific model variants.
• LiteLLM : Best open-source, self-hosted Python-native proxy. If your team requires data sovereignty or wants to manage load-balancing, fallbacks, and canary percentages via a lightweight proxy configuration, LiteLLM is extremely popular.
• Helicone : Best for per-request proxy-mode experiments. It provides robust cost tracking, latency metrics, and custom user-tagging out-of-the-box, making it seamless to compare cost-vs-performance of a fine-tune.
• Cloudflare AI Gateway : Best if your infrastructure already leverages Cloudflare Workers. It handles basic analytics, caching, and rate limiting with near-zero latency overhead.

2. LLM Observability & Evaluation Platforms (Best for Scoring & Analysis)

Once you capture traffic or run experiments, these tools determine which model actually performed better using code-based metrics or LLM-as-a-judge.

• Braintrust : Highly opinionated for the evaluation loop. Excellent for running side-by-side production traces or test datasets through human, code, and LLM evaluators.
• Langfuse : A powerful, open-source (MIT licensed) platform covering tracing, evaluations, and prompt/model management. Great if you want granular production monitoring without aggressive per-event pricing.
• Arize Phoenix : Built on open standards like OpenInference, making it ideal for teams coming from traditional ML observability who need deep debugging on embeddings, drift, and evaluations.

Recommended Decision Framework

To help narrow down the ideal setup, could you share:

• How your application is hosted (AWS, Cloudflare, self-hosted Kubernetes, etc.)?
• Your current traffic volume (approximate requests per day)?
• How you plan to judge success (latency reduction, cost, or LLM-as-a-judge quality scores)?

If your goal is specifically “fine-tuned Model A vs. production Model B, then gradually expose real traffic and decide whether to promote A,” I’d separate the problem into two layers:

1. Evaluation/A-B testing — determine whether A is actually better.
2. Traffic management/canarying — safely expose A to production users.

My top pick: Braintrust + your existing deployment layer

braintrust.dev is probably the best fit if the core problem is model evaluation and comparison. It supports immutable experiments, side-by-side comparison, production-trace evaluation, LLM-as-a-judge and custom scorers, and CI/CD integration.

I'd use it like this:

                    ┌── Model A (fine-tuned)
Request ── Router ──┤
                    └── Model B (production)

                         ↓
                 traces + outcomes
                         ↓
                 Braintrust evals
                         ↓
       quality / latency / cost / failure rate

Then progressively move traffic:

1% → 5% → 10% → 25% → 50% → 100%

with automatic rollback if Model A crosses predefined thresholds.

Strong alternative: Arize Phoenix

arize.com is especially attractive if you want open source/self-hosting and deep observability. Phoenix can create datasets from production traces, replay the same inputs against different models, run evaluations, and compare experiments side-by-side.

It's a particularly good choice if your organization doesn't want evaluation data going into a proprietary SaaS.

What I would choose

The important distinction is that I wouldn't use an evaluation platform as the actual traffic router. Let your gateway/service mesh/application own the percentage-based routing, while Braintrust or Phoenix tells you whether A is winning.

For a fine-tuned model, I'd also avoid judging the winner purely on aggregate “quality.” I'd track at least:

• Task success / correctness
• Human preference
• Hallucination/error rate
• Safety/policy violations
• p50/p95 latency
• Cost per request/token
• Fallback/error rate
• Performance by important user/use-case segments

A particularly strong setup is replay testing first, then a randomized online A/B test. Replay the same representative production dataset through A and B to establish a baseline; then expose A to a randomized slice of real traffic and compare the same metrics. Braintrust explicitly supports experiments plus asynchronous scoring of production traces, which maps well to this workflow.

If you tell me your stack (e.g. Kubernetes, AWS, GCP, OpenAI/vLLM/TGI, Python/TypeScript, etc.), I can recommend a concrete architecture for the A/B router + evaluation platform and show how I'd implement the canary.

Choosing the best platform for LLM A/B testing or canary deployments depends on whether you want a dedicated AI Gateway to handle the traffic splitting at the network layer, an Observability/Eval platform to score the results, or Infrastructure-level tools if you are self-hosting your fine-tuned weights.

The top-tier platforms are categorized below by their strengths to help you pick the right fit for your stack:

1. AI Gateways (Best for Traffic Splitting & Canary Routing)

If you want to send𝑋% of production traffic to your fine-tuned model (Model A) and(1 0 0−𝑋)% to your production baseline (Model B) with low latency, an AI Gateway is your best entry point.

• LiteLLM Proxy: Best open-source/self-hosted router. It supports load balancing, fallback routing, and weighted traffic splitting across custom fine-tuned endpoints and standard APIs. Ideal if your team wants granular control and data privacy.
• Portkey: Best for managed governance and guardrails. It features robust routing rules, fallback logic, and real-time request tracking, making it smooth to direct subsets of production traffic safely to a canary deployment.
• Braintrust Gateway: Best for tying routing directly to evaluation. It acts as a unified API gateway while automatically logging every single request as a structured trace, directly feeding your evaluation datasets and online scorers.

2. LLM Observability & Evaluation Platforms (Best for Scoring the A/B Test)

Routing the traffic is only half the battle; you also need to measure whether your fine-tuned model actually performs better on live data (tracking regressions, hallucinations, latency, and cost).

• Arize Phoenix: Best open-source tracing and evaluation framework. Strongly rooted in OpenTelemetry standards, Phoenix excels at letting you evaluate production spans and run side-by-side experiments on live feedback.
• LangSmith: Best if you are built on LangChain/LangGraph. It provides deep trace trees, debugging, and continuous online/offline evaluation pipelines to compare how different model variants handle live user prompts.
• Langfuse: Best open-source LLM engineering platform. Highly modular for tracking user feedback, scores, and cost attribution across distinct model versions, available as self-hosted or managed cloud.

3. Infrastructure & Model Serving (Best if Self-Hosting Open Weights)

If your fine-tuned model is an open-weight model (like Llama or Mistral) running on your own GPUs rather than an API endpoint:

• Anyscale Services : Built on Ray, Anyscale allows you to natively spin up canary version clusters, allocate a percentage of replicas (e.g., 25%) to a new model variant, and gradually shift live traffic while scaling down the primary cluster.

To help me narrow down the ideal choice, tell me:

• Are you calling hosted APIs (like OpenAI/Anthropic fine-tunes) or self-hosting open weights on GPUs?
• Do you already use an observability tool (like Datadog, Langsmith, or Arize) in your stack?

When comparing a fine-tuned model against a production baseline via A/B testing or canary deployments, the "best" platform depends on whether you prefer a dedicated LLM Gateway (fastest setup, no code changes), an AI Observability/Eval Platform (deepest quality insights), or traditional Infrastructure/Feature Flag Tools (maximum traffic control).

The top platforms are categorized below by their core strengths:

1. Dedicated AI Gateways (Best for Instant Traffic Splitting & Canary Weighting)

If you want to split traffic (e.g., 95% Model A, 5% Model B) directly at the API layer without rewriting your application code or redeploying your service, an AI gateway is the cleanest approach.

• • Portkey: Excellent for lightweight canary testing. You can configure weighted traffic distribution directly on their platform, monitor real-time latency, error rates, and cost per request side-by-side without touching app logic. See how it works via the Portkey Canary Testing Blog.
• • Braintrust: Provides a unified routing gateway alongside robust evaluation pipelines. It allows you to log outputs, run automated metrics, and cleanly handle multi-model traffic routing. Learn more on the Braintrust Gateway Overview.
• • Helicone: Offers ultra-fast, proxy-based setup with minimal overhead and easy integration for tracking comparative production logs.

2. AI Observability & Evaluation Platforms (Best for Deep Quality Tracking)

Standard load balancers only track HTTP status codes and latency. For fine-tuned models, you need to track semantic metrics (hallucination rates, tone coherence, task success, and token cost).

• • Maxim AI: A full-lifecycle platform that stands out for bridging evaluation, simulation, and production observation. It supports cross-functional analysis (letting product managers or QA review live failures) and converts live production anomalies back into evaluation test cases. Explore features on the Maxim AI Platform.
• • Arize AI (with Phoenix): Built for enterprise-scale machine learning and LLM monitoring. It handles deep trace collection, embedding drift analysis, and evaluation metrics smoothly. Check out the open-source toolkit at Arize Phoenix.
• • LangSmith / Langfuse: Best if your stack is already tightly coupled with them. LangSmith excels at granular trace-level debugging (especially if you use LangChain/LangGraph), while Langfuse is the go-to open-source, self-hostable option for prompt and trace management.

3. Feature Flag & Infrastructure Orchestration (Best for User-Sticky A/B Tests)

If your A/B test requires routing specific users or user segments consistently to Model A or Model B (sticky sessions) rather than a pure randomized token split:

• • LaunchDarkly / Unleash: Pair these traditional feature flag engines with your application backend logic. Hash by user_id or session_id in your code to ensure a user never sees alternating responses from different models mid-conversation, then pipe the event outcomes to your observability tool of choice.

Which should you pick?

• Choose Portkey or Braintrust if you want a quick, no-code-change proxy/gateway to start a 1%→right arrow→ 5%→right arrow→ 25% canary rollout today.
• Choose Maxim AI or Arize if your primary concern is automated LLM-as-a-judge quality scoring and catching subtle regressions in live traffic.
• Choose LaunchDarkly if you need strict user-level segmentation for a user-facing A/B product experiment.

To help narrow down the ideal architecture, could you share:

• Are you hosting the fine-tuned model yourself (e.g., vLLM, Together AI, AWS Bedrock) or via an API?
• Do you need user-sticky routing (keeping the same user on the same model), or is a random request-level split sufficient?

If your goal is specifically “take a fine-tuned Model A, compare it against production Model B, then gradually expose real users to A”, I’d separate the problem into offline evaluation and online traffic experimentation.

My recommendation

Best overall for LLM/model evaluation: braintrust.dev

Braintrust is particularly strong when the question is “did this model actually get better?” It supports side-by-side model experiments, datasets, custom/LLM-based scorers, production traces, and CI/CD quality gates. Its current workflow explicitly supports running experiments before release and scoring live traffic afterward.

For your use case, I'd structure it roughly as:

                 ┌── Model A (fine-tuned) ──┐
User request ────┤                           ├── response
                 └── Model B (production) ──┘
                         │
                  experiment/evaluator
                         │
             quality / latency / cost / UX

Then, once A wins offline:

100% Model B
       ↓
95% B / 5% A
       ↓
80% B / 20% A
       ↓
50% B / 50% A
       ↓
100% A

The important distinction is that Braintrust is excellent for the evaluation layer, but I'd consider a feature-flag/experimentation system for the actual traffic split if you need sophisticated targeting and statistically rigorous product A/B tests.

How I'd compare the main options

Langfuse is particularly attractive if you want an open-source/self-hosted stack; its current positioning combines tracing, evaluation, datasets and experimentation. Langfuse Phoenix is compelling if your organization is already standardized on OpenTelemetry and wants local/self-hosted control.

For true online A/B testing, GrowthBook is worth looking at. Its own current guidance makes an important distinction: AI evals tell you whether an output/system is better, whereas controlled experiments tell you whether the change actually improves user or business outcomes.

What I would use

For a serious production model replacement, my preferred stack would be:

Braintrust + your existing feature-flag/A-B infrastructure

Use Braintrust for:

• Golden-set evaluation of A vs. B
• LLM-as-judge and deterministic metrics
• Regression testing
• Human evaluation
• Cost/token analysis
• Latency analysis
• Production trace sampling
• Release quality gates

Use your feature flag system for:

• 1–5% canary
• User/account-level assignment
• Sticky cohorts
• Geographic or customer targeting
• Instant rollback
• Experiment randomization

And measure both model quality and product metrics:

quality score + task success + latency + cost + error rate + user feedback/conversion That's important because a model can score better on an offline benchmark while being worse in production—for example, because it is slower, more expensive, or changes user behavior.

If you tell me your stack (e.g. OpenAI/Anthropic/custom inference, Kubernetes, Python/TypeScript, LangChain, existing LaunchDarkly/GrowthBook/etc.), I can recommend a concrete architecture and shortlist 2–3 platforms rather than giving you the generic comparison.

How I'd compare the main options

Langfuse is particularly attractive if you want an open-source/self-hosted stack; its current positioning combines tracing, evaluation, datasets and experimentation. Phoenix is compelling if your organization is already standardized on OpenTelemetry and wants local/self-hosted control.

For true online A/B testing, GrowthBook is worth looking at. Its own current guidance makes an important distinction: AI evals tell you whether an output/system is better, whereas controlled experiments tell you whether the change actually improves user or business outcomes.

What I would use

For a serious production model replacement, my preferred stack would be:

Braintrust + your existing feature-flag/A-B infrastructure

Evaluating fine-tuned models against production models in a live environment requires specialized LLM routing, guardrails, and experimentation tools.

Here are the best platforms for LLM A/B testing and canary deployments, categorized by their core strengths:

• Braintrust : Best for full-stack LLM evaluation, logging, and production experimentation, allowing you to run side-by-side comparisons and A/B evaluations with production traffic.
• Arize Phoenix / Arize AI : Best for observability and tracing, giving deep insights into latency, cost, and output quality differences during a canary rollout.
• Pezzo : Best for lightweight, open-source LLM remote config, versioning, and canary deployments directly at the edge or gateway level.
• LangSmith (by LangChain) : Best if you are already building within the LangChain/LangGraph ecosystem, offering robust tracing, playground testing, and evaluation datasets.
• Portkey / LiteLLM Proxy : Best for infrastructure-level routing, letting you set up weighted traffic splitting (e.g., 9 0% production, 1 0% canary) and fallback mechanisms with minimal code changes.

To help narrow down the ideal choice , could you tell me:

• What infrastructure or gateway are you currently using to serve your production model?
• Are you looking for a fully managed SaaS or an open-source tool?

For A/B testing or canarying a fine-tuned model against an existing production model, I’d separate the problem into two layers:

1. Traffic routing / rollout — who gets model A vs. model B?
2. Evaluation / observability — did B actually improve quality, latency, cost, and reliability?

My top pick: Braintrust

Braintrust is probably the best fit if your main goal is measuring whether the fine-tuned model is better than production, rather than merely routing traffic.

It supports:

• Side-by-side experiments against the same dataset, with per-example regression/improvement analysis.
• Production tracing and online scoring, so you can evaluate real user traffic.
• Separate dev/staging/production environments and A/B variants, with users routed to different environments.
• CI/CD quality gates before promoting a model.
• Model/provider abstraction through its gateway, which makes comparing different models relatively straightforward.

A good rollout would be:

Production model → 90% traffic
Fine-tuned model → 10% traffic
↓
Compare task quality + user feedback + latency + error rate + token/cost
↓
25% → 50% → 100% if B passes your thresholds.

Other strong choices

LangSmith is particularly compelling if your application is built around LangGraph: it combines offline experiments with online production evaluation and has managed/self-hosted deployment options.

One important distinction

I wouldn't use a generic deployment platform alone (Kubernetes, LaunchDarkly, AWS, etc.) as your model-testing system. A 90/10 traffic split tells you which model users received, but not whether the fine-tune is actually better.

For an LLM, I'd make the experiment measure something like:

Primary:
  task_success_rate / human preference

Secondary:
  factuality
  instruction following
  structured-output validity
  latency p50/p95
  error rate
  cost/request

Guardrails:
  safety violations
  hallucination rate
  regression on critical customer segments

Then use offline replay first, followed by a small live canary, followed by gradual rollout.

If you tell me your stack (e.g. OpenAI fine-tune, vLLM, AWS Bedrock, Kubernetes, Python/FastAPI, etc.) and roughly how much traffic you have, I can recommend a concrete architecture and compare Braintrust vs. LangSmith vs. Arize vs. a homegrown feature-flag setup for it.

For comparing a fine-tuned model against a production model in a real application, I’d separate the problem into two pieces:

1. Offline evaluation — replay the same production/golden dataset against A and production.
2. Online A/B or canary routing — send a controlled percentage of real traffic to A and compare outcomes.

My shortlist

My default recommendation: Braintrust. Its workflow is particularly well suited to your use case: production traces → datasets → experiments → scorers → regression checks. That makes it easy to say “fine-tuned model A must beat production on these metrics before we increase traffic.”

But for the actual canary

I wouldn't necessarily make the evaluation platform responsible for traffic routing.

A clean architecture is:

                    ┌── Model A (fine-tuned)
User → API Gateway ┤
                    └── Production Model

                 ↓
          trace + outcome
                 ↓
       Evaluation / Analytics
                 ↓
     A vs Production metrics

Use a gateway/feature-flag layer to control:

• 1% → fine-tuned model
• 5% → fine-tuned model
• 25% → fine-tuned model
• 50% → fine-tuned model
• 100% → fine-tuned model

while keeping sticky assignment so the same user/conversation consistently sees the same model.

For the comparison, I'd track at least:

• task success / business KPI
• evaluator score
• human feedback
• hallucination/error rate
• latency p50/p95
• input/output tokens
• cost/request
• failure rate
• performance by important traffic slices

When I'd choose the others

LangSmith — I'd choose this if you're already heavily invested in LangChain/LangGraph. Its tracing, datasets, evaluations and deployment workflow are tightly integrated.

Arize Phoenix — my pick if you want open source/self-hosting and don't mind assembling more of the release infrastructure yourself. Phoenix supports tracing, datasets, experiments and evaluation and is designed to run locally, in containers, or in your cloud.

Langfuse — compelling if self-hosting and vendor independence are priorities. It's particularly attractive when you want production observability without committing to a proprietary platform.

MLflow — worth considering if your organization already has MLflow infrastructure. It can be a good choice when this fine-tuned model is part of a broader traditional ML lifecycle rather than solely an LLM application.

What I'd build

For a serious production rollout, I'd use Braintrust + a gateway/feature flag system rather than looking for one tool to do everything:

Offline: 10k–100k representative production examples → A vs production → automated + human evals.

Shadow: Send production requests to A but don't expose its response to users. Compare quality, latency, and cost.

Canary: 1% real users → 5% → 25% → 50% → 100%, with automatic rollback thresholds.

Decision: Don't use one aggregate score. Require A to pass quality + safety + latency + cost gates, with slice-level checks so an improvement in the average doesn't hide a regression for an important customer segment.

If you tell me your stack (e.g. OpenAI/Anthropic/self-hosted model, Kubernetes, AWS/GCP, LangChain or custom) and roughly how much traffic you have, I can recommend a concrete architecture and the best 2–3 platforms for it.

Comparing a fine-tuned model against a production baseline requires an infrastructure stack that handles traffic splitting (canary/A/B routing) and evaluations/observability (logging and judging outputs).

The best platform depends on whether you want an open-source proxy gateway you control or a specialized AI observability/eval platform.

Top Platforms for LLM A/B Testing & Canary Deployments

• LiteLLM (Best Open-Source Gateway / Proxy) : Provides a robust, OpenAI-compatible proxy server with a built-in router class allowing you to split traffic by percentage (e.g., route 5% of requests to your fine-tuned model and 9 5% to production), handle automatic fallbacks, and track costs per model.
• Portkey Gateway (Best for Reliability & Governance) : An open-source and managed control plane that handles production traffic splitting, weighted load balancing between models, automatic retries, and semantic caching.
• Braintrust (Best for Evaluation-Driven Releases) : Combines a production gateway with deep evaluation loops. It lets you route traffic while turning real-time production logs and failing traces directly into regression test suites.
• Maxim AI (Best for Cross-Functional & Agent Workloads) : Features a unified gateway (Bifrost) alongside no-code evaluation workflows. It is strong if product managers or QA teams need to review model output metrics side-by-side without engineering bottlenecks.
• LangSmith (Best for the LangChain/LangGraph Ecosystem) : Offers tight native tracing, evaluation, and experiment monitoring if your production app is already built within the LangChain ecosystem.
• GrowthBook / Statsig (Best for Traditional Product A/B Testing) : If your test measures downstream user behavior (conversion rates, clicks, retention) rather than just linguistic quality, use a dedicated product experimentation platform coupled with your backend router.

To help narrow down the ideal setup, could you tell me:

• Are your models self-hosted (vLLM/Triton) or accessed via hosted APIs?
• Are you measuring success based on LLM-as-a-judge quality metrics or downstream user conversion?