Baserun
ProductFreeLLM testing and monitoring with tracing and automated evals.
Capabilities10 decomposed
end-to-end request tracing with llm-specific context capture
Medium confidenceAutomatically captures complete execution traces for LLM application requests, including prompt inputs, model outputs, token counts, latency metrics, and intermediate steps across multiple API calls. Uses instrumentation hooks at the SDK level to intercept LLM provider calls (OpenAI, Anthropic, etc.) and structured logging to correlate related operations into unified traces without requiring manual span creation.
Provides LLM-native tracing that automatically captures model-specific metadata (token counts, model names, temperature settings) without requiring developers to manually define spans, using provider-agnostic instrumentation that works across OpenAI, Anthropic, Cohere, and other LLM APIs
Deeper than generic APM tools (Datadog, New Relic) because it understands LLM semantics; simpler than building custom tracing because it requires zero manual span instrumentation
automated evaluation framework with custom function support
Medium confidenceExecutes user-defined evaluation functions against LLM outputs to measure quality, correctness, and safety. Supports both deterministic checks (exact match, regex, schema validation) and LLM-based evaluations (using another model to judge outputs). Evaluations run asynchronously on captured traces and can be parameterized with custom scoring logic, thresholds, and aggregation rules.
Combines deterministic and LLM-based evaluation in a unified framework where users write simple Python/JS functions that can call external APIs, use regex, or invoke another LLM for judgment — all executed server-side without requiring infrastructure setup
More flexible than fixed evaluation libraries (RAGAS, DeepEval) because it allows arbitrary custom logic; more integrated than standalone evaluation tools because evals run automatically on all captured traces without manual dataset creation
regression testing with baseline comparison and ci/cd integration
Medium confidenceAutomatically compares LLM outputs from new code versions against baseline traces to detect quality regressions. Integrates with CI/CD pipelines (GitHub Actions, GitLab CI, etc.) via webhooks and status checks, allowing tests to block deployments if evaluation scores drop below thresholds. Baselines are established from previous runs and can be manually curated or automatically selected.
Treats LLM outputs as testable artifacts with statistical regression detection, using baseline comparison rather than fixed assertions — automatically blocks deployments when evaluation scores degrade, integrated directly into Git workflows via status checks
More sophisticated than simple output snapshot testing because it uses evaluation metrics rather than exact matching; tighter than external testing tools because it's built into the LLM observability platform with automatic trace correlation
multi-provider llm instrumentation with unified trace format
Medium confidenceAutomatically instruments calls to multiple LLM providers (OpenAI, Anthropic, Cohere, Azure OpenAI, self-hosted models) through a single SDK, normalizing responses into a unified trace schema regardless of provider. Handles provider-specific response formats, streaming responses, and error states transparently, allowing developers to switch providers without changing instrumentation code.
Provides transparent instrumentation across heterogeneous LLM providers by intercepting at the SDK level and normalizing to a unified schema, allowing cost/performance comparison without application code changes or provider-specific wrappers
Simpler than building custom provider abstraction layers because normalization is built-in; more comprehensive than provider-specific monitoring because it works across OpenAI, Anthropic, Cohere, and others with identical instrumentation
cost tracking and token usage analytics across llm calls
Medium confidenceAutomatically extracts token counts and pricing information from LLM provider responses, aggregates costs by model/provider/user/feature, and provides dashboards showing cost trends and per-request breakdowns. Integrates with provider pricing APIs to stay current with rate changes and supports custom pricing configuration for self-hosted models.
Automatically extracts cost data from LLM provider responses without requiring separate billing API calls, providing real-time cost attribution at the request level with multi-dimensional aggregation (by model, user, feature, etc.)
More granular than provider billing dashboards because it attributes costs to application features; more automated than manual cost tracking because it extracts token counts from every request without configuration
dashboard and visualization of llm application behavior
Medium confidenceProvides web-based dashboards displaying traces, evaluation results, cost metrics, and performance trends with filtering, search, and drill-down capabilities. Includes trace timeline visualization showing request flow, latency breakdown by component, and side-by-side output comparison views for regression analysis. Built on time-series data from captured traces.
Provides LLM-specific visualizations including prompt/output side-by-side comparison, token count breakdown, and latency attribution across multi-step chains — not generic APM dashboards adapted for LLMs
More intuitive for LLM debugging than generic APM dashboards because it shows prompts and outputs prominently; more accessible than query-based tools because exploration is visual and interactive
webhook and alert notifications for quality/cost anomalies
Medium confidenceMonitors evaluation scores, cost metrics, and error rates in real-time, triggering webhooks or alerts when values exceed configured thresholds. Supports integration with Slack, PagerDuty, email, and custom webhooks. Alerts include context (affected traces, metric deltas, suggested actions) and can be configured per metric, time window, and alert severity.
Provides LLM-specific alert types (evaluation score drops, cost anomalies, token count spikes) with context-rich payloads including affected traces and metric deltas, integrated with standard incident management platforms
More relevant than generic metric alerts because it understands LLM-specific failure modes; more integrated than building custom monitoring because it connects directly to Slack, PagerDuty, and other platforms
prompt versioning and a/b testing framework
Medium confidenceManages multiple versions of prompts with version control, allowing developers to test different prompt variations against the same evaluation suite. Supports A/B testing by routing requests to different prompt versions and comparing evaluation results. Integrates with CI/CD to promote prompts to production based on evaluation metrics.
Treats prompts as first-class versioned artifacts with built-in A/B testing and statistical comparison, allowing data-driven prompt optimization without manual experiment setup or external tools
More integrated than manual A/B testing because it's built into the evaluation framework; more rigorous than ad-hoc prompt changes because it requires evaluation comparison before promotion
dataset management and test case curation
Medium confidenceAllows users to create and manage datasets of test cases (input-output pairs) extracted from production traces or uploaded manually. Datasets can be used to run evaluations in batch, establish baselines, or create regression test suites. Supports filtering, tagging, and versioning of datasets.
Integrates dataset management with production trace extraction, allowing test suites to be built from real production cases without manual data collection, with built-in batch evaluation
More convenient than external dataset tools because test cases can be extracted directly from production traces; more integrated than standalone evaluation datasets because they're tied to Baserun's evaluation framework
team collaboration with shared dashboards and reports
Medium confidenceProvides shared dashboards, reports, and insights that teams can access to understand application quality, performance, and costs. Supports role-based access control (read-only, editor, admin) to manage permissions, enables team members to comment on test results and share findings, and generates automated reports (daily, weekly) summarizing key metrics. Enables non-technical stakeholders (product managers, executives) to understand LLM application health without direct access to traces or code.
Implements team collaboration for LLM application quality by providing shared dashboards and automated reports that aggregate test results, performance metrics, and costs; enables non-technical stakeholders to understand application health without access to raw traces
More specialized for LLM application teams than generic collaboration tools (like Slack) by providing structured dashboards and reports; simpler than building custom reporting infrastructure
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Keywords AI
Unified LLM DevOps with API gateway, routing, and observability.
Best For
- ✓LLM application developers building production systems with complex multi-step workflows
- ✓teams debugging unexpected model behavior in production
- ✓engineers optimizing token usage and latency across LLM chains
- ✓teams building LLM products who need continuous quality measurement without manual review
- ✓developers implementing custom evaluation logic specific to their domain (e.g., medical accuracy, legal compliance)
- ✓organizations tracking LLM performance regressions across model versions
- ✓teams with mature CI/CD pipelines who want LLM-specific quality gates
- ✓organizations managing multiple LLM model versions and need data-driven promotion decisions
Known Limitations
- ⚠Trace capture requires SDK integration — applications using raw HTTP calls without Baserun SDK will not be automatically instrumented
- ⚠Trace retention and query performance may degrade with very high-volume applications (>100k requests/day) depending on plan tier
- ⚠Custom middleware or non-standard LLM provider integrations may require manual instrumentation
- ⚠LLM-based evaluations add latency and cost (requires additional API calls to evaluation model)
- ⚠Custom evaluation functions must be written in supported language (Python/Node.js) — no visual evaluation builder
- ⚠Evaluation results depend on quality of evaluation function logic — garbage in, garbage out
Requirements
Input / Output
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About
Testing and monitoring platform for LLM applications that provides end-to-end tracing, automated evaluations, and regression testing. Captures full request traces, supports custom eval functions, and integrates with CI/CD pipelines.
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