// compare
How Functional AI compares
The tools you're probably evaluating alongside us, compared honestly. Functional AI is in private beta — where a competitor is the better fit today, the page says so. Each comparison states when its facts were last verified against the competitor's own published pages.
Functional AI vs Braintrust
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 vs an LLM gateway
An LLM gateway sits between your code and your model providers: one API for many models, with fallbacks, spend limits, and logging on every call — while your application still runs the prompts and agents. Functional AI works one level up: it hosts the prompt, agent, or multi-agent workflow itself as an Agentic Function, scores every version against your evals before it ships, and fails over between models for you. Pick a gateway to govern the model calls of code you run; pick Functional AI if you would rather not run the agent at all.
Functional AI vs an LLM router
An LLM router picks a model for each request: a trained router model or task classifier predicts which candidate gives acceptable quality at the lowest cost, so easy prompts go to cheaper models. Functional AI makes that choice per version instead — every version, including one on a cheaper model, is scored against your own dataset before it ships, and at runtime it fails over to a backup model if the primary fails. Use a router when your prompts vary widely in difficulty; use Functional AI when you want every model change proven on your data before users see it.
Functional AI vs LaunchDarkly
LaunchDarkly's AgentControl (formerly AI Configs) manages prompts, model settings, and tool configs as runtime configuration your code fetches — the closest capability overlap with Functional AI of any tool here. The architectural difference: LaunchDarkly serves the config while your application still makes the LLM calls and owns the orchestration; Functional AI hosts and executes the whole Agentic Function. Pick LaunchDarkly if you're already invested in its flag platform and want AI config control in the same place; pick Functional AI if you want the runtime itself — execution, eval gates, and failover — off your plate.
Functional AI vs Agenta
Agenta is an open-source (MIT) workspace for building and iterating on agents — versioned prompt/config registry, environments, tracing, and evals, self-hostable with no lock-in. Functional AI is a hosted runtime: it executes your prompt, agent, or workflow behind an Agentic Function call, with eval gates and model fallback enforced by the platform. Pick Agenta if open source and self-hosting are requirements; pick Functional AI if you want execution and reliability handled for you.
Functional AI vs Future AGI
Future AGI spans evals, tracing, guardrails, and an OpenAI-compatible AI gateway with routing and fallback — an Apache-2.0 open-source stack aimed at self-improving agents that automatically optimize their own prompts and routing from scored traces. Functional AI takes the opposite trust posture: versions are explicit, human-approved, and eval-certified before they ship, then hosted and failed-over by the runtime. Pick Future AGI for an automatic optimization loop over calls your code makes; pick Functional AI for certified versions of Agentic Functions the platform runs for you.
Functional AI vs Galtea
Galtea is an AI evaluation platform for regulated industries — synthetic test generation, adversarial simulation, red-teaming, and production monitoring, with a compliance posture (ISO 27001, self-hosting, SSO). It tests and monitors AI you built elsewhere; it doesn't host, version, or run anything. Functional AI is the runtime: it executes your Agentic Function, gates versions behind evals, and fails over between models. The two are closer to complementary than substitutes — the overlap is evaluation.
Functional AI vs Dataiku
Dataiku is an enterprise AI platform — data prep, ML, analytics, governed LLM access through its LLM Mesh, and agents grounded in enterprise data, sold to large organizations. Functional AI is a developer tool at a completely different grain: one agent, hosted as an Agentic Function your product code calls. If you're standardizing AI for a 5,000-person enterprise, that's Dataiku's job; if you're a product engineer shipping an agent this sprint, it isn't.
Something inaccurate about a competitor here? Tell us and we'll fix it — these pages only work if they're fair.
// faq
Common questions
- Which platforms should I compare for evaluating and running LLM features in production?
- When evaluating agents in production, engineers typically compare three categories: evaluation platforms that score outputs before ship (Braintrust, Agenta); model-routing layers that swap providers on failure (LLM gateways and routers); and agentic function runtimes like Functional AI that combine evaluation, model fallback, versioning, and deployment into one typed function call your app makes. The right scope depends on how much ownership you want over the full stack.
- How is an agentic function runtime different from an LLM evaluation platform?
- An LLM evaluation platform scores your outputs offline and shows you whether a version is better — it's a testing tool. An agentic function runtime like Functional AI hosts your prompt, agent, or workflow as a callable function, runs evals automatically before every ship, switches to a backup model when a provider fails, and lets you roll back without redeploying your app. Evaluation, monitoring, and deployment in one place, as a single function call.