Decagon AI vs Quadratic

Decagon AI and Quadratic are both business tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Decagon AI

Decagon deploys AI agents that handle customer support end-to-end: identity verification, order lookups, refunds, subscription changes, and routing to the right team — without a human touching most of it. Workflows are defined in natural language through Agent Operating Procedures, so CX operations teams can update agent behavior without filing an engineering ticket. The platform unifies voice, chat, and email under one intelligence layer, which means the customer's context follows them across channels. Customer stories on the vendor site cite 80% deflection rates and 95% cost reductions — but those are headline outcomes from enterprise deployments with significant onboarding investment. Teams with in-house AI engineering appetite or sub-enterprise ticket volume will find the contract size hard to justify.

Quadratic

Quadratic is a spreadsheet environment where cells can hold Python, SQL, or JavaScript instead of formulas, and an AI agent writes that code from plain-English prompts. You connect live sources — Postgres, Snowflake, QuickBooks, Plaid, Mixpanel — and the sheet stays in sync without CSV exports. The AI handles joins, forecasts, and charts; you review the generated code before it runs, so there is an audit trail. The ceiling appears when your analysis requires orchestration across multiple agents with complex branching — the spreadsheet model stops fitting the logic. Teams at that point reach for a dedicated workflow tool and keep Quadratic for the output layer.

Attribute	Decagon AI	Quadratic
Pricing	Paid	Paid
Free trial	No	No
Open source	No	No
Has API	Yes	Yes
Self-hosted option	No	No
Platforms	Cloud (SaaS)	Web
Released	2023	—
Pros	Natural language Agent Operating Procedures let CX and operations teams update agent workflows without engineering involvement, so behavior changes ship in hours instead of sprint cycles. A single intelligence layer spans voice, chat, and email, which means customer context persists across channels and you avoid the broken handoff where an agent starts the conversation over on a different channel. Built-in A/B testing and QA simulation at scale let teams validate changes against live traffic before fully deploying, so a mis-configured workflow doesn't surface first in production at peak volume. The agent executes transactions — refunds, subscription changes, account recovery — not just lookups, so deflection rates reflect actual resolution rather than customers who gave up and called back. Usage-based pricing tied to conversations or resolutions aligns vendor incentives with actual outcomes, so you are not paying a flat fee for an agent that routes everything to a human.	AI writes Python and SQL from plain-English prompts, so analysts who know what they want but not the syntax stop being blocked — and the generated code is visible in the cell, which means a reviewer can verify the logic instead of trusting a black box. Live connections to Postgres, Snowflake, BigQuery, QuickBooks, Plaid, and Mixpanel mean the sheet refreshes from source data, so you stop chasing down who last exported the CSV and whether it was before or after month-end close. MCP support lets external agents write to and read from the spreadsheet as a tool, so Quadratic can sit inside a larger agent pipeline rather than requiring you to rebuild your entire workflow inside one product. Output lives in a familiar spreadsheet format, so sharing results with a finance director or product manager who will not open a Jupyter notebook is not a conversation you have to have. Replacing VLOOKUP stacks with readable Python reduces the 'who wrote this and why does it break' debugging cycle — the logic is explicit, versioned, and survives column-order changes.
Cons	No self-serve trial and no free tier means you cannot validate fit before entering a procurement cycle — teams that need a proof of concept before budget approval are forced to negotiate access through a sales process, which typically adds weeks before any agent runs a single conversation. Self-hosting is not on offer, which is a hard stop for financial services or healthcare teams with data residency requirements that prohibit sending customer data to a third-party cloud — those teams move to a self-hostable competitor or build on an open-source agent framework instead. Contract structures in the six-figure annual range make Decagon economically indefensible for support operations below a certain ticket volume threshold — teams that are scaling toward enterprise but are not yet there exit for a mid-market tool with per-seat or lower-commitment pricing. Because the platform is fully managed and closed, teams with internal AI engineering capacity who want to own the model selection, retrieval architecture, or fine-tuning pipeline hit a wall — Decagon operates the agent for you, and if that is not what you want, the product is working against your team rather than with it.	Multi-step conditional logic — branching on what a previous query returned, then routing to a different data source based on the result — does not fit the spreadsheet execution model. Teams building that kind of workflow hit this ceiling on the second or third agent and add a separate orchestration layer, at which point they are maintaining two systems. No self-hosted deployment option means every live database connection and every piece of data processed by the AI agent transits Quadratic's cloud. Teams under strict data residency requirements or with security policies prohibiting third-party cloud access cannot use the product and move to a self-hostable alternative. The API and scheduled tasks are paid-only features, so teams evaluating the free tier for automated, recurring reports will find those capabilities gated — the evaluation environment does not reflect what production actually requires. The product targets analysts in a spreadsheet paradigm; engineers building data pipelines or transformation logic that belongs in dbt, Airflow, or a dedicated ETL tool will find the canvas constraining and the collaboration model mismatched to a code-review workflow.

Bottom line

Decagon AI and Quadratic are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between Decagon AI and Quadratic?

Decagon AI is Paid, while Quadratic is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Decagon AI better than Quadratic?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.