Is Google Dialogflow Best For Your Business? [Review]

If you're evaluating Dialogflow today, the most important fact isn't in Google's marketing pages: Dialogflow CX's standalone console was deprecated on October 31, 2025, and existing users are now routed to Google's new Conversational Agents console, a unified surface that consolidates Dialogflow CX with Vertex AI Agent Builder. The underlying service still exists. The authoring experience has changed.

That migration is the single most useful lens for evaluating Dialogflow right now. If you're a current user, you're moving consoles regardless. If you're a new buyer, you're walking into a product whose authoring surface has been folded into something larger. Either way, this is the right moment to ask whether Dialogflow is still the right call. Or whether the migration friction is a clean opening to evaluate alternatives like Voiceflow, Cognigy, Kore.ai, or Rasa.

What follows is an honest review: what Dialogflow does well, where it's stale, what the Conversational Agents shift means in practice, and when Voiceflow is the better fit.

Recent developments (as of May 2026)

The big changes since the last 18 months:

• October 31, 2025: the standalone Dialogflow CX console was deprecated. Users are routed to the Conversational Agents console, which combines Dialogflow CX and Vertex AI Agent Builder into a single interface.
• 2026 roadmap: deeper Gemini integration in the flow builder, with more generative capabilities available without separate Vertex AI configuration.
• Underlying API and runtime are unchanged: the migration is about the authoring console, not the runtime. Existing CX agents keep working.
• Vertex AI Agent Builder as a brand is winding down: its features are being absorbed into Conversational Agents.

If you're auditing a Dialogflow deployment, the most important question right now is whether your team has the bandwidth to relearn the Conversational Agents console mid-cycle, or whether you'd rather use the migration window to evaluate alternatives that aren't being reorganized.

What is Dialogflow?

Dialogflow is Google's conversational AI platform: chat and voice agents for websites, apps, and contact centers, now authored through the Conversational Agents console. It comes in two editions:

• Dialogflow ES (Essentials): older, smaller-business-oriented. Intent-and-entity model, simpler flows, public pricing.
• Dialogflow CX (Customer Experience): graph-based agent design, generative features via Gemini, integration with Vertex AI Search for RAG, voice support through Google Cloud Contact Center AI.

Core capabilities:

• Visual flow builder: directed graphs of pages and intents. Branching is explicit and deterministic.
• NLU and intent matching: strong baseline accuracy on classic intent classification, with prebuilt entity types.
• Vertex AI Search data stores: RAG over your own documents, returning grounded answers.
• Generators: Gemini-backed prompt nodes for free-form responses inside CX flows.
• Omnichannel: one agent surfaces to web, mobile, Google Chat, and (via CCAI) phone.
• Built-in analytics: agent-level conversation traces and quality metrics.

What you don't get out of the box: model choice (you're on Gemini), a dedicated playbook primitive for LLM-driven reasoning, or a unified voice-and-phone stack that doesn't require Google CCAI as a separate product.

What companies like about Dialogflow

Customers who stay on Dialogflow tend to give the same three reasons. The NLU is well-tuned. Intent classification is reliable at scale, even on noisy speech-to-text input. Integration with the rest of Google Cloud is tight: BigQuery for analytics, Vertex AI Search for RAG, and Google Workspace through native connectors. And Google's pricing model includes usage-based billing that's predictable for high-volume deployments.

For teams already inside the Google ecosystem with Workspace, BigQuery, and GCP for everything else, that gravitational pull is real.

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Knowledge bases on Dialogflow

Dialogflow CX's knowledge surface runs through Vertex AI Search data stores. You point it at documents, websites, or BigQuery tables, and the agent can query at runtime for grounded answers. ES has a thinner FAQ-style knowledge feature with limited filtering.

Voiceflow takes a different approach. The Knowledge Base is a first-class primitive. It does chunked semantic search over OpenAI embeddings, optional LLM synthesis using your agent's configured model, MongoDB-style filter operators on the query path, and environment-scoped content (dev, staging, and production each carry their own copy). The point isn't that one is better than the other in the abstract. The point is that Voiceflow's KB is decoupled from a specific model, while Dialogflow's is tied to Google's stack.

Dialogflow tutorials and docs

Google's Conversational Agents documentation is the canonical starting point. The ES quickstarts walk through building a simple agent, calling the API, and testing flows. Anyone coming from older Dialogflow ES tutorials should know two things. The CX experience is structurally different (graph-based, not intent-and-rule-based). And the console UI changed again with the 2025 migration.

Dialogflow API

Dialogflow's API ships in two editions: ES for small/midsize teams and CX for enterprise. Both expose intent detection, fulfillment, speech recognition, and webhook integration. The CX API is materially richer. Pages, flows, and parameters are first-class, and it's the path forward.

If you're new to building API-driven agents, Voiceflow has a primer on agent APIs that covers the same ground in a model-agnostic way.

Dialogflow's founder

Dialogflow was founded in 2010 as API.AI by Ilya Gelfenbeyn. After Google's 2016 acquisition, Gelfenbeyn led Google Assistant Investments until 2020. He's currently co-founder and Executive Chairman of Inworld AI, an AI character platform.

Dialogflow's parent company

Alphabet, Google's parent, has a market cap of roughly $4.8 trillion as of May 2026, with TTM revenue of $402.83 billion. In Q1 2026 alone, revenue was $109.9 billion, up 20% year over year. Translation: Dialogflow isn't going anywhere as a product line. The migration to Conversational Agents is a product reorganization, not a sign of platform abandonment.

Dialogflow pricing

Google publishes usage-based pricing that varies by edition. ES has per-request pricing for text and audio. CX is priced per session (with text and voice sessions billed differently) and includes a free tier of $600 in credit on first activation, valid for 12 months. Enterprise customers typically negotiate volume pricing through a Google Cloud sales rep. Expect quoted figures to be heavily customer-specific.

Dialogflow's customer base

Google has won over a long list of brand-name customers, mostly through CCAI and CX deployments in contact centers:

Dialogflow alternatives

If Dialogflow's Google lock-in or the ongoing console migration are friction points, the alternatives worth real evaluation:

• Voiceflow: the rest of this article. Model-agnostic, full voice + phone + chat, Workflows + Playbooks + Tools as separate primitives.
• Cognigy: enterprise CX platform, recently acquired by NICE for $955M. Stronger in voice and contact-center deployments. Same "competitor in transition" caveat applies.
• LivePerson: pivoting hard into voice after the SoundHound acquisition. Worth a look if you're consolidating chat and voice.
• Kore.ai: enterprise virtual-agent platform with deep BFSI presence.
• Sierra AI: newer entrant, focused on agentic CX for high-touch consumer brands.
• Rasa: open-source, code-first conversational AI. Good if you want full control and have engineering capacity.
• IBM watsonx Assistant: IBM's enterprise agent platform, multilingual NLU.
• Decagon AI: agentic CX for support automation.

For a broader survey, see our roundups of the best AI chatbots and best agent management platforms.

Voiceflow vs Dialogflow at a glance

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Why Voiceflow instead

Five reasons that hold up under enterprise scrutiny. Not generic feature-list claims, but things you can verify in a free-tier trial.

1. You're not locked to Google's models

Voiceflow's runtime exposes a wide model catalog: Anthropic's Claude (Sonnet 4.6 as the default), OpenAI's GPT-5 and GPT-5.2, Google's Gemini 3.1 Pro, AWS Bedrock-hosted Claude, Voiceflow-native GLM 5, Groq, and OpenRouter. You pick per task. You swap when something better lands. Dialogflow's generative features are bound to Gemini.

For builders who actively avoid model lock-in (most senior AI/ML teams in 2026), that's the cleanest single differentiator.

2. Voice and phone are first-class, not a separate product

Voiceflow ships native voice and phone in the core platform: call_forward for human escalation, dtmf for IVR menus and secure data capture, two-socket transport for live transcripts plus TTS audio, multi-provider STT (Deepgram, Google) and TTS (ElevenLabs, Google, Polly), and barge-in for natural turn-taking. If you're building a voice agent or AI call center agent, it's the same agent and the same authoring surface as your chat agent.

Dialogflow voice runs through Google CCAI as a separate product. Different config surface, different licensing, different team usually.

3. Workflows + Playbooks + Tools: the modern agent paradigm

Voiceflow exposes three composable primitives:

• Workflows: deterministic step-by-step graphs for the parts of a conversation where order and validation matter (payment, KYC, compliance flows).
• Playbooks: LLM-reasoning agents for the parts where the model should figure out the next step (open-ended support, dynamic routing).
• Tools: Functions, API, and MCP integrations callable from either workflows or playbooks.

You mix freely. A playbook can transition into a workflow for a sub-task and return when it's done. Session state and conversation history persist across the boundary. Dialogflow CX's flow-based model is closer to "graph plus prompt nodes." It's a different generation of the same idea.

This matters for builders who are already designing agentic AI systems where deterministic and LLM-reasoning surfaces need to coexist in one project.

4. Production primitives out of the box

Voiceflow ships:

• Environments: dev, staging, and production with explicit promotion pipelines and isolated KB content per env.
• Evaluations: LLM-driven evals at scale to catch regressions before they ship.
• Observability: conversation-level traces plus aggregate metrics for model quality, tool reliability, and intent matching.
• API + SDK: embed agents in any front-end; programmatic agent control for CI/CD pipelines.

These exist in Google Cloud too, spread across Vertex AI, Cloud Logging, and Looker. The Voiceflow difference is that they're a single integrated surface, not assembled.

5. The customer list isn't theoretical

Voiceflow runs production agents at Turo, StubHub International, Sanlam Studios, and Trilogy. The customer service automation playbook for enterprise ROI is documented; the security and compliance posture covers SOC 2 Type 2 with PII masking on by default. If you're scoping a customer service AI agent for a regulated environment, those are real reference points.

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Frequently asked questions

Is Google Dialogflow free?

Dialogflow CX includes a $600 free credit on first activation, valid for 12 months. Dialogflow ES has a free tier with limited monthly text and audio quotas. Beyond those, both are usage-based and scale by request volume. Voiceflow has an always-on free tier (no time limit) and moves to usage-based pricing only at scale.

How much does Google Dialogflow cost?

It depends on edition and traffic. CX bills per session, with text and voice sessions priced separately and voice meaningfully more expensive. ES bills per request. Enterprise customers typically negotiate volume pricing through a Google Cloud sales rep, and quoted figures are highly customer-specific. Check the pricing page for current rates.

Is Dialogflow an AI tool?

Yes. Dialogflow has been Google's conversational AI platform since 2016, and CX adds generative capabilities through Gemini integration. The 2025 migration to the Conversational Agents console doesn't change that. It consolidates Dialogflow CX with Vertex AI Agent Builder under one authoring surface.

What's the best Dialogflow alternative?

Depends on your stack. If you want model flexibility and unified voice + chat, Voiceflow is the cleanest swap. For enterprise contact-center deployments, Cognigy and Kore.ai are the natural peers, with the caveat that Cognigy is mid-acquisition. For code-first teams, Rasa is open-source. For agentic CX in high-touch consumer brands, Sierra AI is the newest serious entrant.

Can I use ChatGPT with Dialogflow?

Yes, but it's not the path of least resistance. You can wire OpenAI as a webhook from a Dialogflow CX fulfillment, passing the user's input and returning the model's response as the agent's reply. The configuration is bespoke. Token management, error handling, and rate limiting all sit on you. If using a non-Google model is a hard requirement, a model-agnostic platform like Voiceflow is structurally closer to what you want.

What is the Conversational Agents console?

Google's new unified console (released 2025) that consolidates Dialogflow CX and Vertex AI Agent Builder into one authoring surface. The Dialogflow CX console was deprecated October 31, 2025; existing CX agents are accessible through the new console. The underlying runtime is unchanged. The change is in the authoring experience.