5 best no-code LLM evaluation tools for product teams in 2026

---

A product manager needs to know whether a prompt change improves the answers customers receive. Reviewing a few hand-picked responses is not enough: the revised prompt should face the same test cases as the current version, with criteria the team agrees on.

The best no-code LLM evaluation tools make that comparison accessible through a visual interface. Braintrust is our first recommendation for teams that want product judgment to inform the tests engineers use. Its shared datasets connect production examples to repeatable evaluations, and its Starter plan lets colleagues participate without seat fees. This guide compares five options by the work a PM can do independently and the limits that affect collaboration.

What can a PM evaluate without code?

For a prompt-based feature, a PM should be able to supply examples and define what a good answer looks like. The interface should make individual failures inspectable, with a way to preserve results for the next review. An average score alone cannot show whether a prompt fixed the customer problem that motivated the change.

Testing a complete application adds a prerequisite. An engineer must connect the application or prepare the evaluation task before a PM can run it from a browser. No-code describes the PM's evaluation workflow; it does not mean that custom application code configures itself.

The comparisons below distinguish workspace access from paid review features and model usage. Check each vendor's linked documentation and pricing before making a purchase, as plans and limits can change.

1. Braintrust

Best for: Product managers who want customer failures to become shared evaluation cases that engineers can keep testing.

Build a test from a customer problem

Suppose a support assistant gives the wrong answer about a refund. Once the application is logging to Braintrust, a PM can inspect that example and add it to a dataset. Include the expected answer so the team has a concrete reference for acceptable behavior. Braintrust's PM workflow documentation describes moving production examples into evaluation datasets. Those cases let PMs and engineers check whether a later change fixes a failure customers encountered.

If production logging is not connected yet, start with manually entered cases or a CSV/JSON upload. The playground can also create a dataset inline, so a first prompt comparison need not wait for logging integration.

Use the current prompt as the base task and add the revision as a comparison task. Keep the model and its settings unchanged for this test so you can attribute differences to the prompt. Include the same policy context in both candidates, along with examples the current prompt handles correctly. For example, an LLM judge could ask whether the answer follows the supplied refund policy without promising an exception the policy does not allow. This criterion evaluates the answer against the policy, rather than rewarding a more confident or polished response. The PM should inspect its reasoning against the expected answer before treating its score as evidence that the revision helped.

Keep evidence from each iteration

The playground's diff mode highlights differences between candidates, so a PM can check whether the revision fixes the refund example without breaking other cases. A higher average score is not enough if a previously correct policy answer now fails.

This comparison tests the prompt with the context you supplied. It does not establish whether the live assistant retrieves the right policy or uses a backend correctly; evaluating those behaviors requires the configured application task described below.

Playground runs overwrite previous results. Select + Experiment to preserve a run before the next iteration. Braintrust documents up to 365-day experiment retention on all plans, separately from the shorter window for logs and playground data.

Run application evaluations after engineering setup

For a feature that depends on retrieval or custom logic, an engineer can configure a remote evaluation server. A PM can then run that configured task from the playground without cloning the repository or installing dependencies. The engineer can expose inline or saved parameters, which become UI controls in the playground, making it possible for a PM to test supported changes independently.

Braintrust also offers managed sandbox evaluations, where engineers push an execution artifact — a code bundle or container snapshot — instead of maintaining a running server. Sandboxes are in public preview and require a Pro or Enterprise plan. Both approaches let a PM evaluate configured application behavior from the playground.

Pros:

• Production examples can become shared regression cases, keeping product reviews connected to engineering tests.
• Starter includes unlimited users and one configurable human review score per project.

Cons:

• Starter keeps logs and playground data for up to 14 days, so save comparisons as experiments when you need to revisit them later.
• Playground annotations require Pro or Enterprise. Free workspace membership does not include every feedback feature.
• Starter's only built-in permission group is Owners, which grants full organization access, including inviting and removing members and deleting resources. Engineers and Viewers require Pro, and scoped project, object, or per-member direct permissions require Enterprise. Inviting a reviewer on Starter is not least-privilege collaboration.

Pricing: Starter has a $0 platform fee, with monthly allowances of 1 GB processed data, 10,000 scores, and $10 model credits. Usage beyond those allowances is billed separately. The plans documentation distinguishes included human review scoring from paid annotations and dataset snapshots. Built-in model access requires an owner with a work email or a payment method; model use pauses when credits run out if on-demand usage is disabled.

2. LangSmith

Best for: Product teams already using LangChain that want to evaluate prompt changes against existing LangSmith datasets.

In the LangSmith playground, a PM can select a dataset and run a prompt configuration across its examples. The batch becomes an experiment. An LLM-as-judge evaluator can be added through +Evaluator; a custom-code evaluator is a separate path that requires code.

LangSmith is a practical option when a PM's colleagues already keep examples and traces there. The PM can compare prompt candidates in the environment engineers use to inspect application behavior.

Pros:

• Playground batches become experiments that the team can revisit.
• The UI supports adding an LLM judge without writing an evaluation script.

Cons:

• The free Developer plan has one seat. A PM and engineer who both need workspace access should account for a paid team plan.
• The prompt playground is not a substitute for configuring evaluation of the full application's behavior.

Pricing: Developer costs $0 for one seat and includes up to 5,000 base traces per month, followed by pay-as-you-go usage. Plus costs $39 per seat monthly and includes up to 10,000 base traces. Model and judge calls can incur separate charges.

Also read: LangSmith vs. Braintrust

3. Comet Opik

Best for: Teams that want automatically saved prompt comparisons and a free hosted workspace for up to 10 members.

In Opik's prompt playground, a PM selects a dataset or test suite and runs prompt variants against its items. Each variant becomes a separate experiment, saved automatically. Side-by-side outputs help reviewers locate differences, while full traces provide detail for an engineer investigating a failure.

Dataset runs support evaluation metrics, and test suites support assertions. Metrics and assertions let reviewers check specified behavior rather than relying only on differences between outputs.

Pros:

• Automatic experiment saving reduces the chance of losing a comparison during prompt iteration.
• The hosted free plan gives a small product team shared access and 60-day retention.

Cons:

• The cloud allowance counts spans. Tests involving multiple traced operations consume more than one span per case.
• Choosing the open-source deployment makes the team responsible for operating it.

Pricing: Free Cloud includes up to 10 members and 25,000 spans per month. The open-source software is free to self-host; infrastructure and model use can still cost money.

4. PromptLayer

Best for: Product teams that prefer reviewing test cases and computed results in a spreadsheet-style interface.

PromptLayer Tables accepts cases from a CSV, manual entry, or request history. Computed columns run prompts and checks against those inputs. A PM can review the resulting scorecard or revisit run history when investigating a change.

The ability to execute selected cells is useful when only a few cases need another test. Each additional computed column can increase cell executions for a run, which matters on plans with monthly execution limits. Use Tables for new work; the legacy evaluation pipeline is deprecated.

Pros:

• Inputs and computed results sit together in a familiar table layout.
• Selected rows or cells can be rerun without executing the entire table.

Cons:

• The free plan's 250 monthly eval cell executions can be restrictive for repeated comparisons across several computed columns.
• A 10 MB per-dataset limit constrains how much test material the free workspace can hold.

Pricing: The Free plan includes five users in one workspace, 2,500 requests per month, and 10 playground runs per day, alongside the limits above. Model-provider charges may apply separately.

Also read: Braintrust vs. PromptLayer

5. Maxim AI

Best for: Teams that want UI-based test reports for prompts or an existing HTTP agent endpoint.

Maxim's evaluation interface accepts a CSV or manually entered test cases. A PM can select evaluators and run a batch, then inspect scores and evaluator reasoning for individual queries.

Maxim AI's comparison dashboard lets reviewers compare runs against a baseline. They can also inspect cost and latency when candidates receive similar quality scores. Comparison reports require Professional or higher, so this workflow is not available on the free Developer plan.

Pros:

• Baseline comparisons help reviewers assess changes across multiple test runs, on Professional and above.
• Per-query evaluator reasoning supports investigation beyond the aggregate score.

Cons:

• Agent testing requires an existing HTTP endpoint, configured in Maxim with its URL, headers, and a mapping from the response payload to the field evaluators score.
• The free Developer plan caps the workspace at three seats with three-day data retention and excludes comparison reports, so a product team reviewing results together will reach a paid tier quickly.

Pricing: Developer is free for up to 3 seats, with 10,000 logs per month and 3-day retention. Professional is $29 per seat monthly with 100,000 logs and 7-day retention, Business is $49 per seat monthly with 500,000 logs and 30-day retention, and Enterprise is custom.

Comparing the five tools

The main differences are how each product saves a batch, helps a PM investigate individual results, and gives colleagues access to the review.

Choosing a tool

Start with Braintrust when the goal is to turn customer failures into tests that product and engineering can review together. For the first evaluation, use a small dataset containing a real failure and examples the current prompt handles well. Define the expected behavior before reviewing the candidate.

Save the result and ask an engineer to inspect cases where the automated score conflicts with your judgment. Add confirmed failures to the shared dataset so the next evaluation tests for the same problem.

Start free with Braintrust and invite the colleagues who judge answer quality at no extra cost.