Top 6 of 13 alternatives
Alternatives to Decagon
Find a better fit. See what each alternative to Decagon does better, where it falls short, and what it costs.
On this page
- 01
Sierra
Enterprise platform for customer-service AI agents across voice, chat, email and messaging, priced by outcome.
Why it's on this listAPI access · YesA headless API is one of the published deployment surfaces; the reference is behind the docs sign-in, so endpoints, auth and limits are not public.Source ↗
Trade-offs against Decagon
- −Self-hosted · NoSierra runs the platform in its own cloud on AWS, offering regional localization rather than deployment onto customer infrastructure.Source ↗
- 02
Ada
Enterprise AI customer service agents across voice, chat, email and messaging.
Why it's on this listAPI access · YesPublic REST APIs are documented for data export, audit log, snapshots, integrations and webhooks, with API keys issued from the dashboard and published rate limits.Source ↗
Where it improves on Decagon
- +MCP server · YesAda ships its own MCP server so an external assistant can read metrics and conversations and write configuration, with role-based limits and staged change sets.Source ↗
- +Audit log · YesA searchable audit log records configuration changes with actor, entity, activity and originating interface including API and MCP, and exports to CSV capped at 10,000 rows.Source ↗
Trade-offs against Decagon
- −Agent permissions · PartlyThe agent acts only through tools you author and switch on, server tools arrive disabled by default, and confirmation steps can gate a write, so autonomy is bounded by configuration rather than open-ended.Source ↗
- −Self-hosted · NoAda runs the platform in its own AWS and Azure accounts and only lets you choose a storage region; nothing in the docs or terms offers customer-infrastructure deployment.Source ↗
- 03
Fin
Intercom's customer agent that resolves support conversations across chat, email, voice and social channels.
From $0.99/outcomeWhy it's on this listBolts AI directly onto the helpdesk you already run -- Zendesk, Salesforce, HubSpot, Freshdesk or Intercom itself -- and says it can go live with any of them in under an hour, against Decagon's 4-12 week AOP configuration. Billing is per resolution from $0.99 (with a 50-resolution monthly minimum) and there's a 14-day free trial, a real number to test against instead of a custom quote.
Where it improves on Decagon
- +Free tier · PartlyYou can start without a sales call, but only on a 14-day cardless trial: there is no standing free plan, though eligible early-stage startups get a year of Fin free under a separate programme.Source ↗
- +MCP server · YesMCP runs both ways: Fin reaches business tools through MCP connectors, and an Intercom MCP server lets outside AI systems read the conversation data Fin produces.Source ↗
- +Seat model · PartlyFin itself carries no seat cost and the vendor states teammates are unlimited, but the floor is a 50-outcome monthly minimum, and pairing Fin with the Intercom helpdesk adds a per-seat charge for Full seats.Source ↗
Trade-offs against Decagon
- −Self-hosted · NoFin runs in Intercom's cloud; the only placement control published is which of three regions holds your data, and none of the pricing, security or integration pages offer a self-hosted or bring-your-own-infrastructure deployment.Source ↗
- −Single sign-on · PartlySSO works through Okta, Azure AD and OneLogin with SCIM and IP restrictions, but the plan comparison lists SSO and identity management only on the top Expert plan, and no page names SAML or OIDC explicitly.Source ↗
- 04
Zendesk AI agents
Customer-service AI agents for messaging, email and voice, billed per automated resolution.
From €25/seat/monthWhy it's on this listFor teams already paying for Zendesk: AI Agents ship included from the Suite Team plan ($55/agent/month) instead of being sold as a separate enterprise contract, automating from day one off the knowledge base you already have. You still pay a separately metered Verified Resolution charge above the plan allowance, but there is no separate sales-led platform to stand up alongside your existing helpdesk.
Where it improves on Decagon
- +GPT · YesGenerative features run on OpenAI via zero-data-retention endpoints; Zendesk names the provider, not a model version.Source ↗
- +Free tier · Partly14-day self-serve trial with no card; there is no permanent free tier, and startups can apply for a longer free program.Source ↗
- +API access · YesAPI access is a checkmark on every self-serve plan in the comparison table; extra API volume is a paid add-on on Suite Professional.Source ↗
Trade-offs against Decagon
- −Self-hosted · NoHosted SaaS on AWS; the only locality control is choosing a Zendesk region.Source ↗
- −Opt out of training · PartlyThird-party LLMs never train on customer data, but Zendesk does train its own generic cross-account classification models on Service Data and publishes no opt-out.Source ↗
- 05
IrisAgent
AI support agents that resolve chat, email, voice and text inside your existing helpdesk.
From FreeWhy it's on this listClaude · YesAnthropic models are scored inside the production IrisAgent stack on its own leaderboard, and the vendor says production routing follows that board.Source ↗
Where it improves on Decagon
- +GPT · YesOpenAI models appear in the same in-stack leaderboard, and the security FAQ names OpenAI-based models as one of the two GenAI options offered to customers.Source ↗
- +Model choice · PartlyThe vendor calls itself model-agnostic but picks the defaults from its own leaderboard and routes on the buyer's behalf, so model selection is a vendor-configured choice rather than a self-serve setting.Source ↗
Trade-offs against Decagon
- −Agent permissions · PartlyAutonomy is configurable per trigger: the same AI answer can be sent straight to the customer or posted as an internal note an agent approves, and enabling deflection everywhere is restricted to admins.Source ↗
- −Admin controls · PartlyAdmins control whether AI answers reach customers at all and scope them with trigger conditions, and role-based access is described for the private deployment; no general policy-engine documentation was found.Source ↗
- −Single sign-on · PartlySign-in is always federated through Google, Microsoft, Salesforce or Okta OIDC, but the plan table puts the SSO / SAML row on Enterprise only, so the two vendor pages disagree on tiering.Source ↗
- 06
Maven AGI
Enterprise AI agent platform that resolves customer conversations across chat, email, voice and internal tools.
Why it's on this listClaude · YesThe Master Services Agreement names Anthropic, alongside OpenAI, as one of exactly two LLM providers generating output today, and lists it as a contracted sub-processor rather than an optional integration.Source ↗
Where it improves on Decagon
- +GPT · YesOpenAI models are named as engines Maven runs on, and the vendor publishes its own enterprise phone-call evaluation of OpenAI's newest realtime voice model, so GPT-family support is asserted rather than implied.Source ↗
- +Model choice · PartlyMaven claims its own architecture is LLM-portable on the platform page, but the contract shows the vendor picks the models and may change them on notice; no buyer-facing model selector is published anywhere.Source ↗
Trade-offs against Decagon
- −In your pipeline · NoThe GitHub connector reads documentation and re-indexes on push; the vendor states outright that it never writes to a repository, so acting on a pull request or in CI is ruled out.Source ↗
- −Self-hosted · NoThe vendor's own marketplace listing classes delivery as SaaS and states the software is not deployed in the buyer's AWS account; no on-premise, VPC or single-tenant option is published on any tier.Source ↗
- −How it meters · PartlyThe vendor names resolution volume as what the price scales with, but publishes no per-resolution rate; the contract meters against a subscription usage allowance with overage priced privately in the order form.Source ↗
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