Respan vs Braintrust

Disclosure up front: I run developer relations at Respan, so I am not neutral. Braintrust is the strongest eval-first product in the LLMOps category and I genuinely recommend it for teams whose primary problem is offline eval rigor. This article is an honest comparison, including where Respan loses to them.

The two products overlap on tracing and on prompt management, but they were designed around different center-of-gravity questions. Braintrust was built around: how do we make offline evals great. Respan was built around: how do we make the whole production LLM stack work in one place. The right pick depends on which question dominates your roadmap.

TL;DR: when to pick each

If you want one sentence: Braintrust is the premium eval-first platform that wins on offline eval workflows; Respan is the unified managed platform that wins on having everything in one place with a gateway. Pick the specialist for eval-heavy workflows, pick the unified platform for end-to-end production AI.

The two companies, briefly

Braintrust was founded in 2023 by Ankur Goyal (previously founder of Impira, acquired by Figma). They raised a strong seed and Series A from a16z and others, and built a reputation as the eval-first product in the LLMOps category. The product started as an offline eval engine and expanded into tracing and prompt management. They are positioned as the premium tier in the category and the pricing reflects that.

Respan was founded in 2023 by Andy Li, Raymond Huang, and Hendrix Liu, YC W24. The product ships LLM observability, evals, prompt management, and an LLM gateway in a single platform. The company operated as Keywords AI through 2025 and rebranded to Respan in early 2026 to better reflect the breadth of the platform. We see roughly 80 million LLM requests per day across customer workloads.

The cultural difference: Braintrust feels like a tool built for an ML quality engineer who runs evals as their primary job. Respan feels like a tool built for an AI product engineer who needs the whole stack to work without stitching products together. Both are valid roles. Many teams have both.

Quick comparison

Evals: where Braintrust earns the premium

This is the section where I will be most honest about losing.

Braintrust's offline eval workflow is the deepest in the category. The scoring functions library is broad (autoevals package, custom scoring functions in TypeScript or Python), the dataset versioning is best-in-class, the experiment comparison reports show diff views that no other tool matches, and the playground iteration loop is tight. If you are a team whose primary discipline is "ship better LLM quality through rigorous offline evaluation," Braintrust is the right pick. I will not pretend Respan beats them at this.

What Braintrust does best:

• Dataset versioning and lineage (you can see exactly which version of a dataset produced which experiment)
• Scoring function composition (combine LLM-judge, heuristic, and human scores cleanly)
• Experiment comparison reports (the diff view between two runs is genuinely better than competitors)
• Playground for prompt iteration with score attribution
• Brainstore (their pattern discovery + topic clustering, Pro/Enterprise)
• Loop agent for autonomous test generation (Enterprise)

What Respan does well on evals:

• Online evals on production traffic by default (every trace can be scored as it lands)
• LLM-judge and rule-based scoring wired into the same data model as the traces
• Offline evals exist with datasets and experiments, but they are not as deep as Braintrust's
• A/B testing across prompt versions with eval scores feeding the comparison

The honest read: if your team runs evals as a release gate every week with dataset comparison reports, Braintrust is shaped for that workflow and Respan is not the right pick for that workflow alone. If your team mostly wants quality measured continuously on production traffic with reasonable offline evals as a secondary workflow, Respan is shaped for that. Many teams want both shapes; in that case, the gateway and unified-platform argument tips toward Respan, but you will give up some offline eval depth.

For background, see LLM evals, how to evaluate an LLM, and what is prompt evaluation.

Tracing and observability

Both products ship tracing. The data models are similar (spans, traces, sessions, scores) but the emphasis differs.

Braintrust's tracing is good and getting better. It integrates cleanly with their eval workflow so a trace can be promoted to a dataset entry, scored offline, and compared in an experiment. The UI shows token usage, latency, and cost. Data is billed per GB processed; observability at scale is meaningful spend on Braintrust's pricing.

Respan's tracing is built specifically around agent workflows. Multi-step agent runs, tool calls, sub-agent handoffs, retrieval steps, and online eval scores all attached to the same trace tree. 100% capture by default with no sampling math. The platform leans toward "see everything happening in production AI" rather than "promote interesting traces to a dataset."

If your observability needs are heavy (millions of traces per day, complex agent topologies, real-time alerting on quality regressions), Respan is shaped for that. If your observability needs are modest and you want them to feed an eval workflow that lives in the same product, Braintrust handles it.

For more, see LLM tracing and what is LLM tracing.

Prompt management

Both products treat prompts as first-class objects.

Braintrust has prompts with versioning and a strong playground experience. Pro/Enterprise tiers add playground annotations so you can iterate with feedback signals attached. Prompts integrate with their eval workflow, which is where Braintrust's prompt management feels native: you iterate, score, compare.

Respan has prompt versioning, A/B testing on live traffic, rollback, and a tight loop with the online eval system. You can route 10% of traffic to a new prompt version and watch online eval scores diverge in real time. For teams that ship prompt changes weekly, this online A/B path is where Respan pulls ahead.

If your prompt workflow is "iterate in a playground, score offline, ship," Braintrust is fine. If your prompt workflow is "ship a new version, A/B test on production, roll back if scores drop," Respan is shaped for that. See best prompt management tools.

Gateway: the clearest structural difference

Braintrust does not ship an LLM gateway. They focus on the eval and observability layer. If you want provider fallback, model routing, key management, or rate limiting across providers, you operate that separately (often with LiteLLM or a similar proxy).

Respan ships a built-in LLM gateway. 500+ models behind a single OpenAI-compatible endpoint, provider fallback, key management, caching, rate limiting, load balancing. The gateway and the observability share the same data plane, so traces are populated automatically and you can see routing decisions in the trace.

If you do not want or need a gateway, this is a non-issue and Braintrust's narrower scope is a feature. If you do want a gateway, Respan being one product instead of two products plus glue is meaningful. See what is an LLM gateway and best LLM gateways.

Pricing

Verified against the public pricing pages today.

Braintrust:

• Starter (Free): 1 GB processed data, 10k scores, 14-day retention. Overage at $4/GB and $2.50/1k scores.
• Pro: $249/month base, 5 GB data, 50k scores, overage at $3/GB and $1.50/1k scores, 30-day retention
• Enterprise: custom, on-prem or hosted, RBAC, SAML, SOC 2 Type II, BAA

Respan:

• Free tier exists with generous traces and evals for most production starts
• Pro: usage-based, includes the full platform (observability + evals + prompts + gateway) without per-feature unbundling
• Enterprise: custom, includes self-host

Honest read: Braintrust is a premium-priced product and they are upfront about that. The $249/month starting point at Pro is meaningfully higher than entry tiers across the category. If you are spending $50k/year on AI tooling and evals are your top priority, that is fine. If you are pre-revenue or running a side project, the free tier is generous enough to start, and you should expect costs to scale up quickly if your data volume grows.

Respan's pricing is usage-based and bundles the gateway in. Teams that already pay for a gateway product (Portkey, LiteLLM Enterprise, etc.) plus an eval product plus an observability product often see consolidation savings by moving to Respan. Teams whose only need is offline evals usually find Braintrust cheaper at small scale.

Target user: who each product is built for

This is the most useful framing I can give you.

Braintrust's target user is the ML quality engineer. Someone whose job description is "make sure the LLM features we ship are high quality before they ship." They run offline evals as a primary discipline, they care deeply about dataset versioning, and they want a tool that respects the rigor of their workflow. The product is shaped for that role and earns its premium price by being best-in-class at it.

Respan's target user is the AI product engineer. Someone whose job description is "ship LLM features end-to-end and keep them working in production." They need observability, they need a gateway, they need prompt management, and they need evals, but they do not want to operate four separate products. The product is shaped for breadth and integration rather than depth in a single discipline.

If your team has both roles, both products can coexist. If your team has one or the other, pick the one that matches.

How to choose

A decision framework that holds up across the conversations I have had with teams evaluating both:

Pick Braintrust if:

• Evals are the bottleneck on shipping LLM features and you want the deepest offline workflow
• You have a quality-engineering culture and someone whose job is running comparisons before releases
• You want best-in-class dataset versioning and experiment comparison reports
• You do not need an LLM gateway in the same product
• You have budget for a premium specialist tool

Pick Respan if:

• You want one product for observability, evals, prompts, and gateway
• You want continuous online evals on production traffic without writing scoring code yourself
• You want 100% trace capture without per-GB data billing
• You want prompt A/B testing on live traffic wired to eval scores
• You want a managed LLM gateway with provider fallback in the same tool

Pick both if:

• You have an ML quality engineer who needs Braintrust's depth, and an AI product engineer who needs Respan's breadth, and budget for both
• This is a real pattern at larger AI-native companies; the two products do not interfere

Frank's take

If I were leading an AI feature team where the primary discipline was offline evaluation, where I had a dedicated quality engineer running before-and-after comparisons every release, where my dataset hygiene mattered more than my gateway uptime, I would pick Braintrust. They are best-in-class at that and the price is fair for what you get.

If I were leading an AI product team where I needed everything to work in production end-to-end, where the gateway was a real operational concern, where I wanted to score live traffic continuously rather than only at release time, I would pick Respan. The unified platform is what I would build for myself if I were not already building it.

I have seen teams pick wrong in both directions. Teams that picked Braintrust when their actual need was a gateway and tracing ended up gluing three products together. Teams that picked Respan when their actual need was deep offline eval rigor found themselves wishing for Braintrust's comparison reports. The honest framing is: what is your top problem this quarter. Pick accordingly.

FAQ

Does Braintrust ship an LLM gateway? No. Braintrust focuses on evals, tracing, and prompt management. For a gateway you would use a separate product. Respan ships a built-in gateway.

Does Respan match Braintrust's offline eval depth? Not today. Braintrust's scoring functions library, dataset versioning, and experiment comparison reports are best-in-class in the offline workflow. Respan covers offline evals adequately and wins on online evals and integration, but if your team is eval-first, Braintrust is shaped for that workflow.

Is Braintrust expensive? Premium-tier in the category. The Pro tier starts at $249/month base with overage charges for data and scores. For eval-heavy teams it tends to be worth it. For light usage the free tier is fine.

Can I migrate from Braintrust to Respan (or the other way)? Yes, with engineering effort. Both products accept OpenTelemetry, and both have dataset import/export. Migrating historical traces and experiments is the heavy lift. Most teams that switch do so because their bottleneck moved (more obs/gateway or more eval rigor).

Which is better for production observability? Respan is shaped for production observability with 100% trace capture by default and a UI emphasizing agent traces. Braintrust's tracing is good but data is billed per GB and the product emphasizes promoting traces to evals rather than running observability as a primary discipline.

Can I use both Respan and Braintrust together? Yes, and some teams do. Respan as the gateway and production observability backbone, Braintrust as the offline eval workhorse. The double cost is real, but for some workflows the depth Braintrust adds on evals is worth it.

Does Braintrust have a free tier? Yes, the Starter tier is free with 1 GB data and 10k scores per month and 14-day retention. Generous enough for a side project, tight for production at scale.

Related

• LLM Evals: How to Score, Compare, and Ship LLM Quality
• Best LLM Evaluation Tools
• LLM Observability: The Complete Guide
• LLM Gateway: One API for Every Model
• Best Prompt Management Tools