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Dify vs Google Gemini

Bottom line

You can run Dify on your own machine or server; Google Gemini is hosted by the vendor.

Which one should you try?

Dify

Freemium

Best for Teams building RAG, chatbot or agent apps with a visual builder

Visit Dify See full review

Google Gemini

Freemium

Best for Enterprise deployments requiring advanced reasoning

Visit Google Gemini See full review

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

Dify

Dify

LLM app platform with visual workflows, RAG, agents and app APIs. Free Sandbox and self-hosted Community edition.

Google Gemini

Google Gemini

The headline capability is the context window: the vendor states Gemini 1.5 Pro supports up to 2M tokens, which means you can load entire codebases or research corpora in a single pass without chunking. The mixture-of-experts architecture lets the Pro-tier models handle complex multi-step reasoning and tool use, while Flash and Flash-Lite variants absorb high-volume, cost-sensitive workloads. Multimodal input — text, image, video, audio — is native, not bolted on, so vision and audio tasks route through the same API surface. The ceiling shows up at the intersection of rate limits and latency: teams with sustained high-throughput workloads report queuing pressure on the free tier, and Pro-tier access is paid-only.

AttributeDifyGoogle Gemini
PricingFreemiumFreemium
PriceFree Sandbox; Professional $590/workspace/year; Team $1,590/workspace/year (annual billing)Free; AI Plus $4.99/mo; AI Pro $19.99/mo; AI Ultra from $99.99/mo
Pricing checkedOct 17, 2026Oct 19, 2026
Free trialNot statedNot stated
Open sourceNoNo
Has APIYesYes
Self-hosted optionYesNo
PlatformsDify Cloud (web); self-hosted with Docker Compose (min. 2 CPU cores, 4 GiB RAM)Web, Android, iOS, Windows and macOS (Gemini app); API via Google AI Studio and Vertex AI
Languages—Multilingual; Gemini 3 models have a knowledge cutoff of January 2025
Released—2023-12-06
Pros
  • Workflows, RAG, agents and logging in one platform, with an API for every app.
  • Connects to many model providers, including any OpenAI-compatible endpoint.
  • Free self-hosted Community edition via Docker Compose, alongside hosted Dify Cloud.
  • 2M-token context window on Pro models, so entire codebases or lengthy research documents can be processed in a single pass — eliminating chunking and the retrieval errors that come with it.
  • Native multimodal input across text, image, video, and audio via a unified API surface, which means teams avoid stitching together separate vision and audio models with separate error budgets.
  • Function calling and tool use built into the API, so agents that need to call external systems mid-task do not require a separate orchestration layer to hand off between reasoning steps.
  • Flash and Flash-Lite variants carry a free tier, so teams can prototype and validate use cases before committing production budget to Pro-tier token costs.
  • Provider access through both Google AI Studio and Vertex AI, which means teams already in the Google Cloud ecosystem can deploy without adding a new vendor relationship or access control surface.
Cons
  • The modified Apache 2.0 license requires authorization for multi-tenant services and bars removing frontend branding.
  • Multiple workspaces and commercial license authorization are Enterprise features.
  • Model API usage is billed by providers once included cloud credits run out.
  • The free tier imposes rate limits that cause requests to queue under sustained load — teams running automated pipelines or batch workloads during peak hours hit this ceiling before they can validate production throughput, and the path forward is paid access, not a configuration change.
  • Pro-tier models are paid-only, and at high token volume the per-token cost compounds quickly; teams with cost-sensitive, high-volume workloads that cannot route to Flash for quality reasons move to DeepSeek-V3 or self-hosted alternatives specifically to recover margin.
  • There is no self-hosted option — all inference runs on Google infrastructure, which blocks deployment in air-gapped environments or jurisdictions where data residency rules prohibit third-party API calls, forcing a switch to open-weight models regardless of capability preference.
  • Complex multi-agent workflows that require precise, auditable branching logic expose gaps in the function-calling interface at scale — teams building more than two or three dependent agent steps report adding a dedicated orchestration layer, which means they are maintaining external state and retry logic that the API does not handle natively.

Made up your mind?

Dify

Freemium

Best for Teams building RAG, chatbot or agent apps with a visual builder

Visit Dify See full review

Google Gemini

Freemium

Best for Enterprise deployments requiring advanced reasoning

Visit Google Gemini See full review

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Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.

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