TL;DR
- Best for enterprise contact center AI with voice, telephony, and 30+ language NLU: Dialogflow (Google Cloud, GetApp 4.4/5, TrustRadius 7.7/10, used by enterprise contact centers for voice and text automation)
- Best for all-in-one AI agents with forms, data, workflows, and email: TinyAgents (7 LLM providers, natively connected to TinyForms, TinyTables, TinyWorkflows, TinyEmails)
- Pricing: Dialogflow CX costs $20 per 100 sessions (~$200/mo for 1,000 sessions). TinyCommand starts free, paid from $19/mo for the full platform.
- The real difference: Dialogflow is a conversation design platform built for structured dialogues — intent matching, entity extraction, slot filling. You design conversation trees manually. TinyAgents uses LLMs (Claude, GPT-4, Gemini) that understand context naturally without manual intent training. Different approach, different trade-offs.
| Feature | TinyAgents | Dialogflow |
|---|---|---|
| Pricing | Free tier + $19/mo paid | ES: free tier (limited) / CX: $20 per 100 sessions |
| Free tier | Unlimited forms + responses + 1K credits | ES: unlimited text, 60min audio/mo. CX: none |
| LLM providers | 7 providers (Claude, GPT-4, Gemini, etc.) | Google models only (Gemini) |
| Language support | Depends on LLM (100+ via GPT-4) | 30+ native languages with NLU |
| Voice/telephony | No native voice | Yes (speech recognition + telephony gateway) |
| Built-in forms | Yes (TinyForms, 40+ types) | No |
| Built-in database | Yes (TinyTables, 7 views) | No |
| Built-in email | Yes (TinyEmails) | No |
| Workflow automation | Yes (TinyWorkflows, 100+ apps) | Via webhooks + Cloud Functions |
| Setup complexity | Minutes (visual builder) | Hours to days (requires Google Cloud) |
| Intent training | LLM-based (no training needed) | Manual intent + training phrases |
| Contact center AI | No | Yes (CCAI with live agent handoff) |
| Deployment | Cloud (SaaS) | Google Cloud only |
| Target user | SMBs and startups | Enterprise contact centers |
| Review rating | Growing | GetApp 4.4/5, TrustRadius 7.7/10 |
| Plan | TinyCommand | Dialogflow |
|---|---|---|
| Free tier | $0/mo forever Unlimited forms, 1K credits, all 5 products | ES: $0/mo Unlimited text queries, 60min audio |
| Entry level | $19/mo 10K credits, 3 users | CX: $20/100 sessions Pay per conversation |
| Scale (1,000 sessions/mo) | $49/mo 50K credits | ~$200/mo CX per-session pricing |
| Enterprise (10K sessions/mo) | $149/mo 250K credits | ~$2,000/mo CX + Google Cloud infra costs |
The pricing gap: Dialogflow CX charges per conversation session. At 10,000 sessions per month, you are looking at $2,000+ before Google Cloud infrastructure costs. TinyCommand handles the same volume for $149/month including forms, database, workflows, email, and AI agents. Dialogflow ES is free for text but severely limited in capabilities compared to CX.
"We used to run our lead enrichment and outreach through five different tools. With TinyCommand, it is just one flow."
— Ankit Solanki, InVideo
Dialogflow is Google's conversational AI platform, part of Google Cloud. It comes in two editions: ES (Essentials) for simpler chatbots and CX (Customer Experience) for enterprise contact center automation. With native support for 30+ languages, speech recognition, telephony integration, and live agent handoff, Dialogflow powers some of the largest contact centers in the world.
TinyAgents takes a fundamentally different approach to AI agents. Instead of designing conversation flows with intents and training phrases, TinyAgents connects LLM intelligence (Claude, GPT-4, Gemini, and 4 other providers) to your business operations through native forms, databases, workflows, and email. No intent trees. No training data. No Google Cloud account required.
These tools solve different problems at different scales. Dialogflow builds structured conversational experiences for enterprise call centers. TinyAgents builds AI-powered business automation for SMBs and startups. The overlap is smaller than you might think.
Where Each Tool Wins
Where Dialogflow wins
Enterprise contact center AI. CCAI integration with telephony gateways, live agent handoff, call recording, and sentiment analysis. Purpose-built for contact centers processing millions of calls. TinyAgents is not a contact center platform.
Voice and speech. Native speech recognition and text-to-speech in 30+ languages. Telephony integration with IVR systems. If your AI agent needs to answer phone calls, Dialogflow is one of the few platforms that handles it natively.
30+ language NLU. Native natural language understanding for over 30 languages with language-specific entity extraction and intent matching. Dialogflow's multilingual support is deeper than relying on LLM translation.
Conversation control. Intent-based design gives you exact control over every response. No hallucination risk. No unexpected outputs. Every conversation path is explicitly designed and tested. Critical for regulated industries.
Google Cloud ecosystem. Seamless integration with BigQuery, Cloud Functions, Cloud Run, and other Google services. If your infrastructure is on Google Cloud, Dialogflow fits naturally.
Where TinyAgents wins
All-in-one platform. TinyAgents includes native forms (TinyForms), database (TinyTables with 7 views and AI columns), workflow automation (TinyWorkflows with 100+ apps), and email campaigns (TinyEmails). Dialogflow is a conversation layer only — you need separate tools and engineering for everything else.
7 LLM providers. Choose between Claude, GPT-4, Gemini, and 4 other models per agent. Switch models per interaction. Dialogflow locks you into Google's models only.
No intent training needed. LLMs understand context naturally. You do not need to write training phrases, define entities, or design conversation trees. Describe what the agent should do and it works. Dialogflow requires manual intent design for every conversation path.
Self-serve and affordable. Sign up, build an agent, go live. Free tier available. $19/month for the full platform. Dialogflow CX charges per session ($20/100) and requires a Google Cloud account with associated infrastructure costs.
Connected to business operations. Your AI agent can read from TinyTables, trigger workflows, send emails, and update records — natively. Dialogflow requires webhooks and Cloud Functions to connect to external business systems.
Conversation design platform vs LLM-powered business agents
Dialogflow's core model is intent-based. You define intents ("book a flight", "check order status"), write training phrases for each intent, extract entities (dates, names, product IDs), and design the conversation flow. The bot matches user input to the closest intent and follows the scripted response path. This works well when conversations are predictable — customer service FAQs, appointment booking, order tracking.
The CX edition adds advanced features: multi-turn conversations with state management, conversation flows with visual builder, and CCAI (Contact Center AI) integration with telephony gateways and live agent handoff. It is genuinely powerful for enterprise contact centers handling millions of calls.
But the intent-based approach has a ceiling. Every new topic requires new intents, new training phrases, new entity definitions. TrustRadius reviewers note that "training phrases already feel outdated compared to other AI options." When GPT-4 and Claude can understand any question without training data, manually scripting intents feels like programming a chatbot from 2018.
TinyAgents skips the intent layer entirely. It connects LLMs directly to your business data and actions. A customer asks a question through your website → TinyAgents reads your knowledge base in TinyTables, reasons about the answer using Claude or GPT-4, and responds naturally. No intent matching. No training phrases. No conversation trees. The LLM is the understanding layer.
The trade-off is real. Dialogflow gives you precise control over every conversation path. You know exactly what the bot will say in every scenario because you designed it. LLM-based agents are more flexible but less predictable — they can handle unexpected questions but might occasionally generate responses you did not script.
Dialogflow also offers capabilities TinyAgents does not have. Native speech recognition and text-to-speech in 30+ languages. Telephony integration with IVR systems. Live agent handoff with sentiment analysis. These are enterprise contact center features that TinyAgents is not built for.
But TinyAgents offers capabilities Dialogflow does not have. Native form building with TinyForms. A flexible database with 7 views in TinyTables. Visual workflow automation with TinyWorkflows across 100+ apps. Email campaigns with TinyEmails. Seven LLM providers to choose from. One subscription for the entire stack. Dialogflow is just the conversation layer — everything else requires separate Google Cloud services, separate subscriptions, and significant engineering to connect.
Who should choose what
Choose TinyAgents if:
- You want AI agents connected to forms, databases, workflows, and email natively
- You need agents that understand questions without manual intent training
- You want to choose between 7 LLM providers (Claude, GPT-4, Gemini, and more)
- You need a self-serve platform at $19/month instead of per-session pricing on Google Cloud
- You are an SMB or startup that needs AI without an engineering team
- You want one platform for agents + forms + data + automation + email
- You prefer natural language understanding over scripted conversation trees
Choose Dialogflow if:
- You run an enterprise contact center that handles voice calls and needs telephony integration
- You need native speech recognition and text-to-speech in 30+ languages
- You require precise control over every conversation path with zero hallucination risk
- Your infrastructure is on Google Cloud and you want native GCP integration
- You need live agent handoff with sentiment analysis and call recording
- Regulatory compliance requires fully scripted, auditable conversation flows
- You have engineering resources to build and maintain intents, entities, and fulfillment code
This comparison also applies to
- Teams comparing TinyAgents with Amazon Lex (AWS's conversational AI platform)
- Teams comparing TinyAgents with Microsoft Bot Framework / Power Virtual Agents
- Teams comparing TinyAgents with Rasa (open-source conversational AI)
- Companies deciding between intent-based chatbots and LLM-powered AI agents
- Contact centers evaluating whether they need enterprise CCAI or lightweight AI agents
