Both do model fallback — so what's actually different?

Where it runs and what it protects. Future AGI's fallback lives in a gateway that proxies calls your application makes — you operate that path. Functional AI's fallback is a property of the hosted Agentic Function itself: the platform executes the call and fails it over, and the calling app never changes.

Isn't self-improving better than manual versions?

It's a trade. Automatic optimization can lift quality continuously, but production behavior then changes without a human approving each change. Functional AI's bet is that teams burned by silent prompt changes want the opposite: nothing ships unless a version passed its evals and someone chose to ship it. Which posture is 'better' depends on how much you need to trust — and audit — what your AI did.

Future AGI is open source — does Functional AI have a self-hosted option?

No. Functional AI is a hosted platform only. If Apache-2.0 licensing or self-hosting is a hard requirement, Future AGI wins that criterion outright.

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Functional AI vs Future AGI

Verdict

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 is in private beta. Future AGI details reflect their published pages as of 2026-08; corrections welcome.

Choose Functional AI for

Explicit, auditable versions — you approve what ships, evals certify it first
Hosting the whole Agentic Function (prompt, agent, workflow), not proxying calls your code makes
Teams that want reliability guarantees without an auto-mutating optimization loop

Choose Future AGI for

An automatic optimize-from-production loop — prompts and routing tuned from scored traces
Gateway-level routing across 100+ providers: fallback chains, hedged requests, cost-aware routing
Open source (Apache 2.0) and self-hostable
Real-time guardrails and large-scale simulation/synthetic data

Side by side

Feature	Functional AI	Future AGI
What it is	Agentic Function Runtime — hosts and runs your AI as an Agentic Function	Eval/observability stack + AI gateway proxying your calls
Hosts and executes your AI	Yes — prompts, agents, and multi-agent workflows	No — the gateway proxies calls your code makes
Eval gate before shipping	Yes — human-approved versions certified by evals	Post-hoc: automatic optimization from scored production traces
Model fallback	Automatic runtime failover per Agentic Function version	Yes — gateway fallback chains, circuit breakers, hedging
Change model	Explicit versions; pin, diff, roll back	Self-improving — the loop mutates prompts/routing automatically
Open source / self-host	No — hosted platform	Yes — Apache 2.0 monorepo
Stage and pricing	Private beta; Team / Scale / Enterprise plans	GA; free tier + usage-based, plus free self-hosted OSS

At a glance

Both offer automatic model fallback — Future AGI at the gateway (fallback chains, circuit breakers, hedged requests), Functional AI inside the hosted Agentic Function's runtime. Neither side can claim exclusivity here.
The philosophical split is the change model: Future AGI's loop automatically rewrites prompts and routing policies from scored traces; Functional AI ships only explicit versions a human approved and evals certified.
Future AGI's gateway proxies LLM calls your code still makes; Functional AI executes the entire Agentic Function and your code calls it.
Future AGI is Apache-2.0 open source and self-hostable; Functional AI is hosted only.
Future AGI is generally available with a free tier; Functional AI is in private beta.

Honest pros and cons

Functional AI pros

Every production behavior traces to a version someone approved and evals certified
The runtime owns execution end to end — no gateway config, no proxy in your call path to operate
Multi-agent workflows versioned and hosted as one unit

Functional AI cons

Private beta — access is via the waitlist or the design-partner program
No automatic prompt/routing optimization — improving an Agentic Function is deliberate work
Not open source and no self-hosted option
No published customer stories or review scores yet

Future AGI pros

Genuinely broad open-source stack: evals, tracing, guardrails, simulation, gateway
Sophisticated gateway routing — fallback chains, hedging, latency- and cost-aware strategies
The self-improving loop can lift quality without ongoing hand-tuning
Free tier plus free self-hosting

FAQ

Both do model fallback — so what's actually different?: Where it runs and what it protects. Future AGI's fallback lives in a gateway that proxies calls your application makes — you operate that path. Functional AI's fallback is a property of the hosted Agentic Function itself: the platform executes the call and fails it over, and the calling app never changes.
Isn't self-improving better than manual versions?: It's a trade. Automatic optimization can lift quality continuously, but production behavior then changes without a human approving each change. Functional AI's bet is that teams burned by silent prompt changes want the opposite: nothing ships unless a version passed its evals and someone chose to ship it. Which posture is 'better' depends on how much you need to trust — and audit — what your AI did.
Future AGI is open source — does Functional AI have a self-hosted option?: No. Functional AI is a hosted platform only. If Apache-2.0 licensing or self-hosting is a hard requirement, Future AGI wins that criterion outright.

Evaluating options? Join the Functional AI waitlist or become a design partner — design partners shape the roadmap and get early access.