# Alternatives to Ada > Source: https://theagentsindex.com/ada/alternatives (current datasheets, evidence-ranked) Candidates share the baseline category or are previously researched peers. Requirements, preserved strengths, explicit trade-offs and research coverage decide the order; paid tier does not. ## Shortlist ### 1. Sierra Conversational AI platform for building enterprise customer-service agents across voice, chat, email and ChatGPT. **Research coverage:** 33/35 criteria **Where it improves on Ada:** - [Debugs and fixes: partial — Ghostwriter diagnoses its own failed simulations and implements the fix; the failure it repairs is agent behaviour, never a defect in your software.](https://sierra.ai/product/ghostwriter) - [Writes tests: partial — Conversation and voice simulations are generated and run on every agent build, and Agent Studio keeps them as a regression suite; these test agent behaviour, not your code.](https://sierra.ai/product/ghostwriter) - [Multi-agent: yes — Named agents pass work to each other: Explorer analyses conversations and hands a recommendation to Ghostwriter, which implements it.](https://sierra.ai/product/explorer) **Trade-offs against Ada:** - [Agent permissions: partial — Autonomy is configured, not fixed: messaging is fully autonomous by default while you declare which steps need human sign-off, and unresolved cases hand off to your care team.](https://sierra.ai/product/horizon) - [Claude: partial — Anthropic is a named subprocessor for large-language-model serving inside the platform, but Sierra routes each turn itself, so choosing Claude is not something a customer does.](https://trust.sierra.ai/updates) - [GPT: partial — OpenAI is listed as an AI-services subprocessor with US and EU processing, and Sierra publishes an agent channel inside ChatGPT; the customer does not select the model.](https://trust.sierra.ai/subprocessors) [View Sierra](https://theagentsindex.com/sierra) ### 2. Fin Customer service AI agent priced per resolved outcome, deployable on any helpdesk. **Research coverage:** 32/35 criteria **Where it improves on Ada:** - [Multi-agent: partial — Two agents exist: Fin faces customers, Operator runs operations and tunes Fin. The vendor does not describe agents collaborating on one conversation.](https://fin.ai/operator) - [Free tier: partial — 14-day trial with unlimited outcomes and no credit card, but no free tier afterwards; the non-Intercom path is contact-sales.](https://fin.ai/pricing) - [Seat model: partial — No seat cost on third-party helpdesks, where teammates are unlimited; on Intercom, helpdesk seats are $29/month each on top of outcomes.](https://fin.ai/pricing) **Trade-offs against Ada:** - [Open models: no — The enumerated provider list is OpenAI, Anthropic, Google and Intercom's own models; no open-weight option is offered.](https://fin.ai/trust-reliability) - [API access: partial — Fin Agent API and Data Connectors are publicly documented; the Fin API Platform (Apex, RAG, Retrieval, Reranker) is gated at $250k annual spend.](https://fin.ai/api-platform) [View Fin](https://theagentsindex.com/intercom-fin) ### 3. Zendesk AI agents Customer-service AI agents for messaging, email and voice, billed per automated resolution. **Research coverage:** 35/35 criteria **Where it improves on Ada:** - [Multi-agent: partial — Admins can build many task-scoped custom agents alongside the channel AI agents, but Zendesk does not describe several agents coordinating on one task.](https://support.zendesk.com/hc/en-us/articles/10724438136858-Creating-custom-agents-in-Zendesk-EAP) - [Free tier: partial — 14-day self-serve trial with no card; there is no permanent free tier, and startups can apply for a longer free program.](https://www.zendesk.com/service/ai/ai-agents/) - [Bring your own key: partial — No bring-your-own-LLM-key or own-cloud inference. BYOK exists only as encryption-key control in a paid privacy add-on.](https://www.zendesk.com/trust-center/) **Trade-offs against Ada:** - [Claude: partial — Claude is not what powers the agent; it is reachable as an external action in action flows using your own Anthropic key.](https://support.zendesk.com/hc/en-us/articles/10630133525018-Using-Claude-actions-in-action-flows) - [Open models: no — Every generative path the vendor enumerates is a managed hosted API; no open-weight or customer-supplied model is offered.](https://www.zendesk.com/trust-center/) - [MCP server: partial — Zendesk is an MCP client: AI agents can call tools on external MCP servers from Suite Growth / Support Team upward. No vendor-documented MCP server exposing the AI agents to other agents.](https://support.zendesk.com/hc/en-us/articles/11105393853210-Announcing-the-general-availability-of-the-MCP-client) [View Zendesk AI agents](https://theagentsindex.com/zendesk-ai-agents) ### 4. IrisAgent AI support agent that resolves chat, email and phone tickets inside your existing helpdesk. **Research coverage:** 33/35 criteria **Where it improves on Ada:** - [Multi-agent: partial — One deployment carries several specialised agents (support, IT help desk, knowledge, ticket automation, QA), but the vendor never documents several agents coordinating on a single conversation.](https://irisagent.com/on-premise-ai/) - [Open models: yes — Fine-tuned open-weight models handle the cheapest routing tier on IrisAgent's own infrastructure, and private deployments can run open models inside your boundary.](https://irisagent.com/blog/how-our-multi-llm-engine-routes-queries-to-the-right-model/) - [Model choice: partial — On the hosted product the vendor routes each query by intent and complexity and states the model choice is not the buyer's; only private deployments let you bring approved models.](https://irisagent.com/llm-customer-support/) **Trade-offs against Ada:** - [Claude: no — The security FAQ enumerates exactly two model options for the hosted product, in-house private models or OpenAI, and never names Anthropic as a model IrisAgent runs.](https://irisagent.com/security/) - [Audit log: partial — A per-answer trail showing the response served and its source is claimed for enterprise deployments, but logging of administrative configuration changes is never asserted for IrisAgent itself.](https://irisagent.com/enterprise-ai-chatbot/) - [Single sign-on: partial — Every account signs in through Google, Microsoft, Salesforce or Okta OIDC with no local passwords, but the plan comparison lists SSO / SAML as an Enterprise-only row, so SAML federation reads as gated.](https://docs.irisagent.com/security-and-compliance/Single-Sign-On-Options) [View IrisAgent](https://theagentsindex.com/irisagent) ### 5. Crescendo AI-native CX platform whose agents resolve customer contacts across voice, chat, email and messaging. **Research coverage:** 29/35 criteria **Where it improves on Ada:** - [Done-for-you service: yes — Crescendo deploys its own CX team alongside the software and takes escalations through a network of human specialists.](https://www.crescendo.ai/) - [Multi-agent: yes — Named specialised agents (Concierge, Quality, Applied Insights, Optimization, Integration, Simulation, Knowledge, Workforce) run one operation together.](https://www.crescendo.ai/news/crescendo-launches-the-ai-native-customer-experience-platform-one-system-that-runs-and-continuously-improves-the-entire-cx-operation) **Trade-offs against Ada:** - [Claude: no — The sub-processor enumeration lists OpenAI, Google Gemini/Vertex AI, Groq and Azure as AI providers; Anthropic appears nowhere.](https://www.crescendo.ai/subprocessors) - [MCP server: partial — Crescendo calls itself MCP-native for reading live backend systems; it never documents an MCP server another agent could query.](https://www.crescendo.ai/crescendo-vs-fin) - [Pooled budget: partial — Buyers hold one contracted conversation volume rather than per-seat balances, and low-CSAT conversations are excluded from it; no allocation or sub-budget controls are described.](https://www.crescendo.ai/crescendo-vs-fin) [View Crescendo](https://theagentsindex.com/crescendo) ### 6. Decagon Enterprise AI support agents for chat, voice and email, built from natural-language Agent Operating Procedures. **Research coverage:** 32/35 criteria **Where it improves on Ada:** - [Multi-agent: partial — A separate supervisor model reviews responses and Duet iterates on the agent, but the vendor describes a network of specialised models rather than agents coordinating on one task.](https://decagon.ai/security) - [In your pipeline: yes — Agent versions can be tested inside existing CI/CD practices before release.](https://decagon.ai/product/testing-qa) - [Getting out: yes — Customer data can be retrieved or deleted via public APIs at any time, and is deleted irrecoverably after termination; no credit balances to expire.](https://decagon.ai/legal/dpa-security-annex) **Trade-offs against Ada:** - [Done-for-you service: no — Software the buyer's own CX team builds and iterates on; the vendor positions itself against professional-services delivery.](https://decagon.ai/product/aop) - [Agent permissions: partial — Guardrails gate sensitive actions such as identity verification and refunds, and Duet's agent changes are staged for human review before release.](https://decagon.ai/product/overview) - [Claude: partial — Anthropic is named as one of the AI providers behind the platform, but the vendor selects models and says most traffic runs on its own.](https://decagon.ai/security) [View Decagon](https://theagentsindex.com/decagon) ## Method Unknown data is never treated as a benefit or a trade-off. Featured placement is labelled but cannot alter recommendation order.