// compare
Functional AI vs Agenta
Verdict
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 is in private beta. Agenta details reflect their published pages as of 2026-08; corrections welcome.
Choose Functional AI for
- A hosted runtime that executes the Agentic Function — not a registry your code fetches config from
- Eval thresholds enforced as a shipping gate rather than a manual promote step
- Automatic model fallback without writing failover logic
- Teams that would rather not operate another self-hosted system
Choose Agenta for
- Open-source-first teams — MIT licensed, identical cloud and self-hosted code
- Self-hosting for data-residency or procurement reasons
- Building agents collaboratively, including via its chat interface with human-in-the-loop approvals
- Unlimited users on every plan, billed on agent runs
Side by side
| Feature | Functional AI | Agenta |
|---|---|---|
| What it is | Agentic Function Runtime — hosts and runs your AI as an Agentic Function | Open-source workspace: prompt registry, evals, tracing |
| Hosts and executes your AI | Yes — prompts, agents, and multi-agent workflows | Mostly no — your code fetches config and makes the calls |
| Eval gate before shipping | Yes — enforced by the runtime | Evals available; promotion to an environment is manual/CI |
| Automatic model fallback | Yes — runtime failover to a configured backup | Not advertised |
| Ship without redeploying | Yes — versioned Agentic Functions | Yes — apps pull latest config via SDK/API |
| Open source / self-host | No — hosted platform | Yes — MIT, Docker/Helm self-hosting |
| Stage and pricing | Private beta; Team / Scale / Enterprise plans | GA; free OSS + cloud tiers billed on agent runs |
At a glance
- Both version prompts and configs, deploy them to environments, and let your app pick up changes without a redeploy.
- Agenta's core architecture serves configuration to code you run — your application still owns the LLM calls; Functional AI executes the Agentic Function itself.
- Functional AI enforces evals as a gate before a version ships; in Agenta, evals inform a promote decision your team makes.
- Agenta is MIT-licensed and self-hostable with identical code in cloud and self-host; Functional AI is a hosted platform only.
- Agenta does not advertise automatic model fallback; Functional AI fails over between models at runtime.
- Agenta is generally available; Functional AI is in private beta.
Honest pros and cons
Functional AI pros
- Execution handled: one Agentic Function call replaces orchestration, eval wiring, and failover code
- Certification before exposure — versions that fail evals never serve traffic
- Model outages absorbed by the runtime, not your on-call
- Multi-agent workflows are the same unit as a single prompt
Functional AI cons
- Private beta — access is via the waitlist or the design-partner program
- Not open source and no self-hosted option — a hard blocker for some teams
- No published customer stories or review scores yet
Agenta pros
- MIT open source with no lock-in — leave the cloud, keep the product
- Versioned registry for prompts, skills, and tools with per-environment deploys
- Tracing with cost tracking, and user feedback that becomes test cases
- Unlimited users on all plans
FAQ
- Does Agenta run my agents for me?
- In its classic prompt-management mode, no — Agenta serves versioned configuration and your code makes the LLM calls. Agenta's newer agent workspace can build agents inside Agenta itself, but if your existing application owns the execution path, that path stays yours. Functional AI inverts this: the platform executes the Agentic Function and your app just calls it.
- If open source matters to us, is Functional AI an option?
- Honestly: not on that criterion. Functional AI is a hosted platform with no self-hosted edition. If MIT licensing or self-hosting is a requirement, Agenta is the better fit — the comparison only becomes interesting if hosted-with-guarantees beats self-hosted-with-ownership for your team.
- Both claim shipping without redeploys — what's different?
- The mechanism. Agenta's SDK fetches the latest config at request time or on a cache TTL, so your running code picks up the new prompt. Functional AI versions the whole Agentic Function and the runtime serves the new version — including its eval certification and fallback config — behind the same call.
- What are alternatives to Agenta for prompt management?
- The right alternative depends on whether you want to self-host a prompt registry or have a runtime execute your AI for you. Agenta is open-source (MIT) — it versions your prompts and config and your code fetches them to make the LLM calls. Functional AI takes the opposite position: it hosts your prompt, agent, or multi-agent workflow as a versioned Agentic Function and executes it for you, with eval thresholds enforced before any version ships and automatic model fallback at runtime. If a hosted runtime that owns the eval-gate-and-execution loop is a better fit than a self-managed config layer, 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.