Tracify vs Braintrust

At a glance

The operating model

Braintrust emphasizes evaluation specialization and flexibility: test case management, custom scoring functions, dataset versioning, and community-driven evaluator patterns. You own the evaluation workflow and integrate it with your observability stack — tracing, cost data, and experiment infrastructure all live elsewhere and you're responsible for the joins between them. Tracify emphasizes integrated production observability: evaluation is built into tracing, cost tracking, and experimentation from day one, so a quality score, a dollar figure, and a specific span are always three facts about the same event rather than three systems you reconcile by hand. Choose Braintrust when evaluation craftsmanship and dataset quality are your primary concern; choose Tracify when reducing the feedback loop between traces and quality gates matters more than evaluation specialization.

Opening situation: teams building quality gates

Your team is shipping a multi-agent system in production. You need to:

• Detect regressions before users see broken behavior — ideally before the change even ships, not after a support ticket flags it.
• Understand quality drivers: Which evaluators matter? What's the cost of each quality improvement? Is a 2-point quality gain worth a 40% cost increase?
• Iterate rapidly: Measure the impact of prompt changes, model swaps, and routing decisions without waiting a week for an analysis.
• Control costs: Run the best model for each task without overpaying, and know exactly where the money is going when a finance review asks.

Most teams reach for evaluation tooling first, because a broken agent is the most visible failure mode. But the platform you pick for evaluation shapes everything downstream — how fast you can ship a fix, whether cost and quality show up in the same conversation, and how much glue code your team owns a year from now. This article helps you choose between evaluation-focused infrastructure and integrated observability platforms by showing the real operating differences, naming the cost and effort trade-offs, and identifying when each platform accelerates production confidence.

Core difference: specialization vs. integration

Braintrust is built for teams that want evaluation as a first-class discipline. You define test cases, build or customize evaluators (LLM-as-judge, heuristic, semantic similarity), version datasets, and run batch evaluations. You decide how evaluation fits into your CI/CD and observability stack — which means you also decide, and build, how it talks to whatever tool holds your traces and your cost data.

Tracify is built for teams that want evaluation as part of comprehensive observability. Evaluation is native to the platform alongside tracing, cost tracking, and experiments. You don't choose between eval and observability — you get both connected by default, because every evaluation result is stored as metadata on the trace span that produced it.

The choice is not primarily about evaluation capability. It is about whether your observability strategy centers on evaluation quality or production completeness. For context on integrated observability architectures, see Tracify's complete guide to agent observability. If your evaluation program is still forming, Tracify's practical guide to agent evaluation walks through the underlying metrics and test design regardless of which platform you land on.

Architecture comparison

Braintrust: evaluation-first, modular

Braintrust's strength is evaluation depth and flexibility. The platform is built around the idea that evaluation is a craft that benefits from specialization and control. You can:

• Build test datasets with versioning and fine-grained labeling. Create multiple dataset versions to represent different scenarios (e.g., "edge cases," "high-volume happy path," "multilingual queries").
• Define custom evaluators: LLM-as-judge with custom prompts, heuristic scorers, regex patterns, semantic similarity, and custom functions. Evaluators can be composed: run multiple scorers on the same output and aggregate results with your own weighting logic.
• Score at scale across test cases and models. Run batch evaluations across large combinations of test case × model × evaluator and generate detailed scoring reports with statistical summaries.
• Integrate with your stack: Send evaluation results to your CI/CD, monitoring, or external platforms via API. Build custom evaluation workflows that fit your existing tools rather than adopting someone else's opinion of how evaluation should work.
• Version everything: Track changes to test cases, evaluators, and scoring logic over time. Understand which evaluator changes caused quality score shifts, and roll back a scoring change the same way you'd roll back code.
• Draw on community evaluator patterns: Braintrust's ecosystem lets you discover, adapt, and share evaluation logic that other teams have already built for common tasks like summarization or RAG faithfulness.

The trade-off: you own the integration between evaluation and other observability signals — tracing, cost, and performance metrics live in whatever tools you've chosen for those, and unifying them into one dashboard is work you do, not work the platform does for you.

Tracify: integrated, end-to-end

Tracify's strength is production completeness with evaluation built into every level of the observability stack. The platform assumes that in production systems, quality, performance, and cost are inseparable — and your observability should reflect that rather than fragment it across three logins. You get:

• Native evaluation. Define test cases within Tracify or upload from external sources. Test cases are tied to actual production traces, so you can compare evaluation results against real-world performance instead of a synthetic sample.
• Automatic cost analysis. Per-call cost breakdown by model, provider, region, and route — paired with quality metrics from evaluations. See immediately: "Claude is 40% cheaper than GPT-4o on this task with equivalent quality," without exporting anything.
• Integrated experimentation. Run A/B tests on prompts, models, routing strategies, and system parameters with automatic quality and cost comparison. Statistical significance testing is built in; no external tools needed.
• Session tracking. Multi-turn agent interactions are preserved as sessions with context attached. Evaluation results roll up from individual calls to session-level quality scores, making it easy to spot which conversation steps degrade quality.
• Unified dashboard. Traces, evaluation results, cost, and experiments are visualized together. Filter a quality regression and immediately see the associated latency, cost, and model changes on the same screen.
• Deployment safety. Gate deployments on evaluation results: "Don't promote this version unless quality score exceeds threshold," enforced automatically instead of hoped for.

The trade-off: evaluation is less specialized than Braintrust (you can't write arbitrary custom scoring code); SaaS-only with no self-hosting option; you accept Tracify's mental model of evaluation as a first-class observability signal rather than a standalone discipline.

The two architectures produce a real cost difference, not just a philosophical one — the full breakdown behind this chart is in the cost and TCO section below.

Scenario 1: Building robust evaluation datasets

Your team is shipping an agent that summarizes customer support tickets. The agent must preserve key details, maintain neutral tone, and never omit liability-related information. You need to catch quality regressions before they reach production. Evaluation accuracy is your primary constraint, and legal has asked to review the scoring criteria before launch.

With Braintrust:

• Create a test dataset of 500 real tickets with reference summaries and fine-grained labels (2-3 days). Tag each summary: "liability_present: yes/no," "tone_appropriate: yes/no," "detail_level: high/medium/low."
• Build a custom evaluator combining multiple scoring methods (about a week of experimentation). Use LLM-as-judge for overall quality, ROUGE for factual completeness, and custom regex patterns to detect missing liability language.
• Version the dataset as quality standards evolve (ongoing, tracked in Braintrust). When legal feedback arrives, create a new dataset version with improved examples rather than overwriting the old one.
• Run evaluation in CI/CD before deploying; block if quality score drops below threshold (a straightforward Braintrust API integration once someone owns writing it).
• Customize evaluators for domain-specific rules. Add a scorer that penalizes summaries missing regulatory keywords: "liability," "indemnity," "severability," and similar terms your legal team specifies.
• Iterate on scoring logic: if false negatives increase, adjust the evaluator weights without touching the test dataset, and keep a changelog of why each weight moved.
• Result: evaluation is precise and domain-adapted. You control the scoring logic completely. Your evaluators become institutional knowledge — stored in version control, reviewed by domain experts, reusable across future projects that hit the same liability-review requirement.

With Tracify:

• Add an API key and instrumentation to your agent (2-3 hours). Every ticket summarization is traced automatically from that point forward.
• Create 500 test cases in Tracify's dashboard or upload a CSV. Tag each case with metadata: business_unit, difficulty, language.
• Build evaluation using Tracify's native evaluators: LLM-as-judge ("Is this summary complete and neutral?"), ROUGE ("Does the summary overlap with the reference?"), and regex pattern matching ("Does the summary contain liability keywords?").
• Combine evaluators into a quality score using weighted logic: 60% LLM-as-judge, 20% ROUGE, 20% regex. This becomes your quality gate without a separate scoring script to maintain.
• Link evaluation to CI/CD: Tracify automatically blocks deployments if new code drops your quality score below your threshold (e.g., 85%).
• When legal feedback arrives, update your test cases and re-run evaluation. Tracify shows you immediately: "New dataset version shows a 2% quality improvement," with the specific spans that moved.
• Bonus: Tracify's dashboard shows the correlation between quality score and production latency and cost. You notice: "Our improved evaluator detects regressions 30 minutes before customers complain," because the same dashboard that flags a quality dip also shows the deploy that caused it.
• Result: evaluation is production-integrated and reaches velocity fast. You trade customization (no arbitrary custom scorers) for speed. Setup to first quality gate: 4-6 hours instead of roughly two weeks.

Scenario 2: Optimizing cost and quality across model choices

Your team runs a multi-model system: Claude (reasoning-heavy tasks), GPT-4o (accuracy-critical tasks), and Mistral (cost-sensitive, high-volume tasks). You need to optimize which model handles each request type without sacrificing quality. Your current setup: 60% GPT-4o, 25% Claude, 15% Mistral, on roughly 75,000 calls a month. Your goal: reduce spend by 20% while maintaining quality, and be able to show the CFO a number that holds up.

With Braintrust:

• Instrument each request with the model used, task type, and outcome.
• Create an evaluation dataset: 1,000 representative requests across task types with quality labels (correctness, latency, tone).
• Run batch evaluation on each model's output: which model performs best on which task?
• Manual cost tracking: fetch provider pricing from OpenAI and Anthropic docs. Build a spreadsheet: model × task type → cost + quality score, and remember to update it the next time a provider changes pricing.
• Analyze results: "Claude scores 92% on reasoning tasks; GPT-4o scores 94% but costs roughly 3x more per call."
• Export results to your routing system; hand-build new routing logic: "Use Claude for reasoning, GPT-4o only for complex disambiguations."
• Deploy and monitor manually; if quality drifts, repeat the analysis — typically a half-day of someone's time each time you want a fresh read.
• Result: evaluation quality is high. Cost optimization is a one-time analysis; ongoing monitoring requires manual work, so most teams that go this route do it quarterly rather than continuously.

With Tracify:

• Instrument traces with task type and context (the same instrumentation work as Braintrust — this part doesn't get easier or harder based on platform).
• Tracify automatically fetches real provider pricing for each call. Your dashboard shows: "This month: $12,500 spend, 78% GPT-4o, 15% Claude, 7% Mistral," refreshed continuously rather than as of your last spreadsheet pull.
• Define evaluation test cases: 1,000 production requests, categorized by task type (reasoning, disambiguation, translation, summarization).
• Create an experiment: "Route reasoning tasks to Claude instead of GPT-4o." Tracify automatically runs evaluation on both versions using your defined quality criteria.
• Experiment results (automatic): Claude version costs 65% less on reasoning tasks, quality score 91% vs. 94%. The recommendation: "Switch 80% of reasoning to Claude; reserve GPT-4o for edge cases where quality exceeds 98%."
• Deploy the change with a single click. Tracify's quality gates prevent rollout if the quality score drops below 90%.
• One week later: Tracify sends an alert. "Quality on disambiguation tasks dropped to 88%. Likely cause: Claude model update. Recommendation: test routing change." You revert or adjust in minutes instead of waiting for the next scheduled analysis.
• Month-end dashboard: spend reduced to $10,200 (18.4% savings), quality maintained at 91% average.
• Result: cost optimization is continuous and evidence-led. Experiments are one-click. Deployment is safe. Quality monitoring is automatic instead of scheduled.

Deep comparison: feature by feature

Evaluation framework

Braintrust:

• Custom evaluators with complete control. Write LLM-as-judge prompts with your own scoring rubric. Combine heuristic scorers, regex patterns, semantic similarity, and custom functions. Chain evaluators: "Only penalize for bias if the output also contains factual errors."
• Fine-grained test case labeling and metadata. Attach rich context to each test case: difficulty, language, domain, expected output traits. Use this for stratified analysis: "How does our evaluator perform on hard cases vs. easy cases?"
• Batch evaluation runner for large combinations. Evaluate 100 test cases × 5 model versions × 3 evaluator versions in one run; get statistical summaries and detailed results.
• Dataset versioning and change tracking. Compare evaluation results across dataset versions. When you add 50 new edge-case examples, see immediately: "New version shows a 3% quality decrease; edge cases are being handled poorly."
• Community-oriented evaluator patterns. Adapt and reuse evaluators other teams have already built for common tasks.
• No integration with tracing or observability data. Your evaluation results are decoupled from latency, cost, or performance metrics unless you build the bridge yourself.

Tracify:

• Production-ready native evaluators (LLM-as-judge, ROUGE, regex, semantic similarity, custom heuristics). Pre-built and tuned for common patterns. You can customize LLM-as-judge prompts, but you can't write arbitrary custom scoring logic.
• Test case management tied to production traces. Every test case is linked to the traces that produced it. See: "This test case failed; here's the exact span where quality degraded."
• Batch runner integrated with experiments. Run evaluation as part of A/B tests. See results immediately: "Treatment version: 91% quality. Control version: 89% quality. Statistical significance: 95%."
• Evaluation results attached as span metadata. Every production trace carries its evaluation score. This makes evaluation part of your production data, not a separate system you query on the side.
• Built-in evaluators are production-tested and maintained by Tracify. You don't maintain evaluator code; Tracify updates and fixes evaluators in the platform.
• Full integration with tracing and cost data. Filter by quality, latency, and cost together: "Show me all traces where quality < 85% and cost > $0.05."

Winner: Braintrust for evaluation specialization; Tracify for production integration. Choose Braintrust if evaluator customization and dataset quality are your primary constraints. Choose Tracify if linking evaluation results to traces and costs matters more and you want to ship evaluation infrastructure in days instead of weeks.

Observability integration

Braintrust:

• Evaluation-focused; tracing via integrations, not a native concept.
• Can consume trace data from another tool and apply evaluation on top of it.
• Requires external tools to correlate quality with performance, latency, and cost.

Tracify:

• Evaluation, tracing, cost, and experiments unified in one product.
• Evaluation results are span attributes, not a separate record you join later.
• Quality metrics automatically correlate with latency, cost, and model.

Winner: Tracify. Observability integration is where the separation becomes stark: Braintrust evaluation is disconnected from production traces unless you build and maintain the connection.

Cost tracking and optimization

Braintrust:

• Token counting via integrations. You pull token counts from your LLM provider's API and correlate them with Braintrust's evaluation results yourself.
• No native cost breakdown. Braintrust doesn't fetch provider pricing or calculate cost per call. You must manually integrate OpenAI, Anthropic, or cloud pricing data and keep it current as vendors change prices.
• Cost optimization is a manual analysis step. Query Braintrust evaluation results; download cost data from your billing console; use a spreadsheet to analyze cost vs. quality.
• Useful for one-time optimization projects. Not designed for continuous cost monitoring as part of your evaluation workflow.

Tracify:

• Automatic cost calculation using real, current provider pricing. Every trace includes the exact cost the moment it lands.
• Per-call cost breakdown by model, provider, region, and route. See: "Claude 3.5 Sonnet: $0.0042 per call; GPT-4o: $0.0090 per call; Mistral Large: $0.0012 per call."
• Cost trending and optimization suggestions. Tracify analyzes your cost distribution and suggests routing changes: "Migrate 30% of task type A to Mistral without quality impact."
• Cost paired with quality metrics in experiments. When you A/B test Claude vs. GPT-4o, Tracify shows both quality and cost in the same view. You see immediately: "Claude is roughly 3x cheaper with equivalent quality."
• Built into deployment gates. Block deployment if cost per request exceeds threshold: "New version is 50% more expensive; requires approval."

Winner: Tracify by a wide margin. Cost visibility is automatic, continuous, and integrated with evaluation. Braintrust cost analysis requires manual integration and is not well suited to ongoing monitoring.

Session and context tracking

Braintrust:

• Evaluation is turn-level or conversation-level, depending on how you scope your test cases.
• No native session concept; you build one on top if you need it.
• Context correlation across turns is manual.

Tracify:

• Native session tracking for multi-turn interactions.
• Evaluation results attached at both span and session level.
• Context is automatically preserved and correlated across a conversation.

Winner: Tracify. Multi-turn agent interactions are first-class citizens; Braintrust requires external session management for the same visibility.

Experimentation

Braintrust:

• Run evaluation across different prompt versions. Create multiple prompt variants; run batch evaluation on each. Compare evaluation scores manually.
• Score results; analyze manually or export to dashboards. Results arrive as structured data; you build your own visualization and statistical analysis.
• No native A/B testing framework. Braintrust doesn't tie evaluation to deployment. You can test different prompts offline, but A/B testing in production requires your own infrastructure.
• Requires external infrastructure for deployment experiments. If you want to deploy treatment and control versions simultaneously and measure the quality difference in production, you build this yourself using feature flags and custom logging.
• Useful for offline prompt optimization. Less useful for continuous production experimentation where you want deployment decisions tied directly to evaluation results.

Tracify:

• Native experiment runner built into the platform. One click: "Test Claude 3.5 Sonnet vs. GPT-4o on the reasoning task."
• A/B test prompts, models, and routing strategies. Tracify automatically routes traffic: 50% to version A, 50% to version B. Both versions are evaluated. Results flow automatically to your dashboard.
• Automatic quality and cost comparison. Tracify shows: "Version A: 91% quality, $0.040/call. Version B: 89% quality, $0.025/call. Winner: Version B (cost savings outweigh the quality dip)."
• Statistical significance testing built in. Experiments show confidence intervals. You see: "Version B quality is not significantly different (p > 0.05); the cost saving is significant (p < 0.01)."
• Experiments drive deployment decisions. Once an experiment concludes, promote the winner with a single click, and tie deployment gates to experiment outcomes.
• Continuous experimentation culture. Because experiments are easy, teams run more of them. This accelerates learning and iteration velocity in a way that compounds over a quarter.

Winner: Tracify decisively. Experiments are first-class infrastructure. Braintrust evaluation can support offline experimentation, but production A/B testing requires an external build.

Team workflow and governance

Braintrust:

• Evaluator changes typically go through a review process owned by whoever writes the scoring code — often a specific ML engineer or data scientist.
• Dataset and evaluator versioning gives you an audit trail, but connecting that audit trail to "which production deploy used which evaluator version" is manual bookkeeping.
• Works well when one or two people own evaluation as a discipline and the rest of the team consumes their scores.

Tracify:

• Any engineer who can add a test case can extend the quality gate; no separate scoring codebase to review and merge.
• Deployment gates reference the evaluator version automatically, so "which quality bar did this release ship against" is answered by the trace, not a changelog someone has to keep current.
• Works well when evaluation ownership is distributed across the team building the agent, rather than centralized in one evaluation specialist.

Winner: Depends on team shape. Centralized evaluation ownership favors Braintrust's review model; distributed ownership across a small agent team favors Tracify's lower-friction path.

API maturity and ecosystem

Braintrust:

• Strong API for custom evaluation and dataset management.
• A growing set of community evaluators and integrations to adapt rather than build from scratch.
• SDK support across common languages.

Tracify:

• Production API for ingestion, querying, and evaluation.
• TypeScript SDK and Python SDK, both used by the Tracify quickstart flow that ships with every project.
• Integrations with common agent frameworks and monitoring tools.

Winner: Tie. Both have mature APIs. Braintrust has a larger community of custom evaluators; Tracify's integrations are purpose-built for agent tracing rather than general ML evaluation.

Cost and TCO analysis

Assume a team running 75,000 LLM calls per month across a 12-person team, with evaluation as a core quality gate and cost visibility as a stated goal for the quarter.

Braintrust

Tracify

At this scale, Tracify's total cost is 55% lower ($21,600/year) and reaches a working quality gate roughly ten days faster. Braintrust's total cost is competitive — and can come out ahead — if you don't need cost tracking or experimentation and your evaluator work stays inside that $1,500/month estimate.

The hidden cost in Braintrust is integration work: you must build bridges between evaluation results and your CI/CD, cost tracking, and experimentation systems, and that work grows with the number of signals you want joined together. Tracify's cost is higher per month at small scale but lower in total engineering time, because the bridges are already built. For teams evaluating whether that engineering time is worth reclaiming, Tracify's guide to reducing LLM costs without hurting quality covers the routing techniques that Scenario 2 above puts into practice.

Trade-offs and boundaries

No platform wins every dimension, and pretending otherwise does a disservice to teams with genuinely specialized evaluation needs.

Where Braintrust genuinely wins

• Arbitrary scoring logic. If your quality bar requires a custom function — a proprietary similarity metric, a multi-step verification chain, or logic that calls out to an internal service — Braintrust lets you write it directly. Tracify's native evaluators cover LLM-as-judge, ROUGE, regex, and semantic similarity, but not arbitrary code.
• Deep dataset stratification. When you need to slice evaluation results by a dozen orthogonal dimensions (difficulty, language, domain, reviewer, source) and compare cohorts precisely, Braintrust's dataset tooling was built for exactly that.
• A dedicated evaluation team. If you employ ML engineers whose primary job is evaluation quality — not agent development — Braintrust's specialization matches their workflow better than a generalist observability tool would.
• Evaluator provenance for audits. Regulated industries that need a documented, versioned trail of exactly how a scoring function changed over time, independent of any tracing platform, get that natively from Braintrust.
• You already have tracing and cost solved. If your organization already standardized on a separate tracing and FinOps stack, adding Braintrust for evaluation alone avoids paying for observability capability you don't need.

Choose Braintrust when:

• Evaluation craftsmanship is your primary blocker. You need custom evaluators, fine-grained scoring, or domain-specific quality criteria that a pre-built evaluator can't express.
• You already have observability infrastructure. Tracing, cost tracking, and experimentation are handled elsewhere; you need evaluation to integrate with them, not replace them.
• Your evaluation logic is rapidly evolving. You want complete control over scoring functions and dataset versioning as your quality bar matures.
• Cost of evaluation infrastructure is a constraint. Braintrust's lower platform fee justifies the integration work at your current scale.
• Your team is evaluation-first. Your primary discipline is ML evaluation, and observability is a secondary concern owned by another team.

Choose Tracify when:

• You want evaluation without integration work. Traces, quality gates, cost optimization, and experiments are connected by default.
• Cost visibility is a requirement. You need per-call cost breakdown without manual integration or a monthly spreadsheet ritual.
• Multi-turn agent interactions are central. Sessions and context preservation matter to how you debug and improve the system.
• Iteration velocity is your constraint. You want to A/B test prompts, models, and routing strategies quickly and safely.
• Your team is small or distributed. The operational overhead of integrating multiple tools is a tax on product work you can't afford to pay.
• You are building agents, not ML models. Tracify's paradigm — tracing plus evaluation plus experiments — maps directly to agent development instead of classical model evaluation.

Hybrid approach: Braintrust for evaluation, Tracify for observability

Some teams use Braintrust for specialized evaluation and Tracify for tracing, cost, and experiments. This approach lets you leverage Braintrust's evaluation depth while using Tracify's observability integration for everything downstream of a score. The trade-off is maintaining two platforms and mapping evaluation results back to traces manually — typically by pushing a Braintrust score as a custom span attribute on the matching Tracify trace, so at least the two systems agree on which event they're both describing.

When to reconsider your choice

Your initial decision should not be permanent. Revisit your platform choice if:

• Evaluation customization becomes a bottleneck. If Tracify's native evaluators can't express your quality criteria, adding Braintrust is justified. You can run both platforms in parallel during the transition.
• Integration overhead grows. If you're spending 20-plus hours per month building bridges between Braintrust and your other tools, Tracify's unified approach will likely save engineering time within a quarter.
• Experimentation velocity matters more than before. As you ship more agents and need to optimize continuously, Tracify's one-click experiments provide faster iteration than manual Braintrust analysis.
• Cost tracking becomes critical. Regulatory, financial, or operational changes may require automatic cost visibility. Tracify provides this natively; Braintrust requires external integration.
• Your team size changes. Small teams (1-3 engineers) benefit from Tracify's simplicity; large teams (10-plus) with dedicated ML engineers may prefer Braintrust's flexibility for the evaluation layer specifically.

Decision framework: which platform fits your needs?

Find the row closest to your situation for each criterion, and note which column it favors. If most of your answers land in one column, that's your platform; a mixed result means the hybrid approach above is worth considering.

Making the transition

From Braintrust to Tracify

If you already have Braintrust evaluation infrastructure:

1. Keep Braintrust running alongside Tracify for evaluation.
2. Add Tracify instrumentation using the same test case definitions, so the two platforms are scoring the same inputs.
3. Run both systems for 2-3 weeks to compare quality scores and cost breakdown before you trust either one exclusively.
4. Migrate high-value experiments to Tracify's native runner first — they show the fastest return.
5. Once quality gates are validated on Tracify, retire the Braintrust integration and keep the dataset for reference rather than deleting it.

This approach lets you validate Tracify's evaluation reliability before committing fully, instead of cutting over on faith.

From Tracify to Braintrust

If you need deeper evaluation customization later:

1. Export evaluation test cases and results from Tracify via the API.
2. Import test cases into Braintrust; map your scoring logic to custom evaluators.
3. Expect this to be manual work — avoid it later by making the evaluation-complexity call early, using the decision table above.

Trade-off: Braintrust is the stronger choice when custom evaluator logic is the bottleneck; Tracify is the stronger choice when tracing, cost, and evaluation need to share one operational workflow.

const decision = { traceProductionRuns: true, customEvaluatorLogic: false };
const platform = decision.customEvaluatorLogic ? "braintrust" : "tracify";

Action checklist

• [ ] Define your evaluation complexity: standard patterns or domain-specific scoring that needs custom code?
• [ ] Audit your observability needs: evaluation-only, or unified tracing + cost + experiments?
• [ ] Estimate your integration costs: can you afford custom bridges between platforms, or do you need unified infrastructure now?
• [ ] Prototype both platforms with a realistic test dataset (2-3 hours each, using the same 20-50 examples on both).
• [ ] Compare quality scores and consistency across datasets, not just setup speed.
• [ ] Calculate your real TCO: platform cost plus engineering time for integration and maintenance, using the worked table above as a template.
• [ ] Identify your iteration bottleneck: is it evaluation quality, observability integration, or cost tracking?
• [ ] Make a reversible decision: plan a parallel-running period or a documented migration path either way.
• [ ] Set a review date (30 days) to compare evaluation quality and deployment velocity between platforms before you commit further budget.

If you are building production-ready evaluation infrastructure, start with Tracify's agent evaluation guide to understand integrated evaluation patterns before choosing a platform. If you are prioritizing evaluation specialization and customization, Braintrust's documentation and community resources offer deep material for evaluator development.

Frequently asked questions