// compare
Functional AI vs Braintrust
Verdict
Braintrust is a mature eval and observability platform for teams already running agents in production — trace everything, score it, improve it. Functional AI takes a different bet: it hosts your prompt, agent, or workflow as a versioned Agentic Function, gates every version behind evals before it ships, and fails over between models at runtime. Pick Braintrust to observe and improve AI you run yourself; pick Functional AI if you'd rather not run it yourself at all.
Functional AI is in private beta. Braintrust details reflect their published pages as of 2026-08; corrections welcome.
Choose Functional AI for
- Owning the whole loop — hosting, eval gates, fallback, and versioning in one runtime you just call from code
- Blocking a bad prompt version before any user sees it, not discovering it in traces afterwards
- Automatic model fallback without writing failover code
- Small product teams that don't want to assemble an eval stack around their own orchestration code
Choose Braintrust for
- Deep observability over agents you already run in your own infrastructure
- Scoring production traffic at scale with LLM, code, and human evals
- A mature, generally available product with published enterprise customers
- Converting production traces into eval datasets
Side by side
| Feature | Functional AI | Braintrust |
|---|---|---|
| What it is | Agentic Function Runtime — hosts and runs your AI as an Agentic Function | Eval + observability platform for agents you run |
| Hosts and executes your AI | Yes — prompts, agents, and multi-agent workflows | Partially — hosted prompts run via invoke(); agents run in your infra |
| Eval gate before shipping | Yes — a version must pass its thresholds to ship | Evals available; gating is your process, not enforced |
| Automatic model fallback | Yes — runtime failover to a configured backup | Not advertised |
| Ship without redeploying | Yes — versioned Agentic Functions, pin or roll back | Yes for prompts (versioned on save) |
| Production tracing depth | Call metadata (latency, cost, version) | Deep — full traces, tool calls, pattern discovery |
| Stage and pricing | Private beta; Team / Scale / Enterprise plans | GA; free tier, flat + usage Pro, Enterprise |
At a glance
- Both version prompts and let you ship a new prompt version without redeploying your app.
- Functional AI hosts and executes the whole Agentic Function — prompt, agent, or multi-agent workflow — while Braintrust primarily observes and evaluates AI that your own code runs (hosted prompts being the exception).
- Functional AI enforces eval thresholds as a shipping gate; Braintrust provides rich evals but leaves the ship/no-ship decision to your process.
- Functional AI does automatic model fallback at runtime; Braintrust does not advertise failover.
- Braintrust's production tracing and pattern discovery go far deeper than Functional AI's call metadata.
- Braintrust is generally available with published enterprise customers; Functional AI is in private beta.
Honest pros and cons
Functional AI pros
- One runtime instead of an assembled stack: hosting, eval gates, fallback, versioning behind a single Agentic Function call
- Certification before exposure — a version that fails its evals never reaches users
- Model outages are handled for you, not by your on-call
- Nothing to instrument: metadata comes back with every call
Functional AI cons
- Private beta — access is via the waitlist or the design-partner program
- No published customer stories, benchmarks, or review scores yet
- Production tracing is call-level metadata, not Braintrust-depth trace exploration
- Younger product with a smaller integration surface
Braintrust pros
- Best-in-class trace exploration and automatic pattern discovery over production traffic
- Mature eval tooling: LLM, code, and human scorers, plus trace-to-dataset workflows
- Generally available, unlimited seats on every tier, published enterprise logos
- Framework-agnostic SDKs and MCP integration
FAQ
- Can I use Braintrust and Functional AI together?
- Yes, in principle: Functional AI runs the Agentic Function and Braintrust could trace the surrounding application. But most of Braintrust's value assumes your code owns the LLM calls, which Functional AI takes over — so in practice teams pick one as the center of gravity.
- Does Braintrust host my agent?
- Braintrust hosts prompts, which your code can execute via its invoke() API, and its proxy carries those calls. Agents and multi-step workflows still run in your infrastructure. Functional AI hosts the entire Agentic Function — prompt, agent, or multi-agent workflow — and your app calls it.
- Which one stops a bad prompt version from shipping?
- Functional AI enforces it: a version must pass its evaluation thresholds before it can serve traffic. Braintrust gives you the evals to make that call, but the gate itself is whatever your team's process enforces.
- Is Braintrust more mature than Functional AI?
- Yes. Braintrust is generally available with published enterprise customers; Functional AI is in private beta with a waitlist. If you need a proven, GA observability platform today, Braintrust is the safer pick — Functional AI is the option to evaluate if the hosted-runtime model fits how you want to ship.
- What are the best Braintrust alternatives for LLM evals?
- The right alternative depends on which part of the eval loop you want to own. Braintrust observes and evaluates agents you run in your own infrastructure; Functional AI is an alternative that takes the opposite approach — it hosts your prompt, agent, or workflow as a versioned Agentic Function and enforces eval thresholds as a hard shipping gate. A version that fails its evals never reaches users. If you want a hosted runtime that owns the eval-gate loop, rather than an observability layer on top of code you run yourself, Functional AI is the alternative to evaluate. It is currently in private beta.
Evaluating options? Join the Functional AI waitlist or become a design partner — design partners shape the roadmap and get early access.