Iris Android vs Voyage AI

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-10-18
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At a glance

DimensionIris AndroidVoyage AI
PricingFreeContact sales (custom pricing)
Target UserMobile tinkerers & privacy usersEnterprise RAG pipelines
DeploymentOn-device (Android)API / cloud
Core FeaturesLocal LLM chat, GGUF support, offlineEmbeddings + rerankers, long-context, domain-specific
IntegrationsNone (standalone)Any vector DB / LLM (modular)
Best ForPrivate on-device AIFinance/legal document retrieval

Buy Voyage AI if you're an enterprise building high-accuracy RAG on domain-specific documents and need advanced embeddings/rerankers. Choose Iris Android if you're a developer or privacy enthusiast wanting to run LLMs offline on your phone for free. They serve completely different needs.

Iris Android
Iris Android

Iris Android runs GGUF language models offline on your phone using llama.cpp — no server, no account, no data leaving the handset.

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Voyage AI
Voyage AI

Voyage AI delivers domain-tuned embedding models and rerankers for high-precision RAG retrieval

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Pricing
Free
Paid
Plans
$0
Consumption-based pricing (rates not published on page)
Popularity
8 views
7.4k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
Mobile
WebAPI
Categories
💾 Local & On-Device AI
🗄️ Vector Databases & Retrieval
Features
Runs GGUF language models locally on Android
llama.cpp inference engine
Offline operation after the model file is downloaded
In-app GGUF model browser and download
Import custom GGUF files
Text-only chat interface
Model management: list, delete and switch between stored models
All prompts and chat data stay on the device
Works with mobile data and Wi-Fi switched off
Air-gapped and restricted-network use
Loads multiple open-source GGUF models
Lightweight app footprint
Optimised for mobile hardware
General-purpose embedding models including voyage-3.5 and voyage-3.5 lite
Domain-specific embedding models optimized for finance, legal, and code
Company-specific fine-tuned embedding models on proprietary data
Voyage 4 model series for improved retrieval quality
voyage-multimodal-3.5 embeds images and text in one retrieval pipeline
Low-dimensional embeddings (3x-8x shorter vectors) cut storage and search costs
32K-token long-context support for embedding long documents
rerank-2.5 and rerank-2.5-lite add instruction-following to ranking
voyage-context-3 keeps chunk-level detail with global document context
Batch API for large-scale embedding workloads
4x smaller model with faster inference and superior accuracy
2x cheaper inference with superior accuracy
Plug-and-play with any vectorDB and any LLM
SOC 2 and HIPAA compliance
Deploy on major clouds, in-VPC customer tenants, or on-premise with model licensing

What real users say: Iris Android vs Voyage AI

Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.

Iris Android

No verifiable community signal. We scanned public discussion on Jul 28, 2026 and found posts matching the name “Iris Android”, but could not establish that they are about this product rather than something else sharing its name. Rather than publish a score built on the wrong subject, we publish none.

Voyage AI

64 mentions across 6 sources · 45% positive — mixed (weighted across 6 sources)

Hacker News, YouTube, App Store, Stack Overflow, GitHub, Lemmy

What users praise

  • • Domain-tuned embedders for finance, legal, and code deliver measurable retrieval precision gains
  • • Harvey case study showed 25% fewer irrelevant documents and 3x lower vectorDB costs
  • • Low-dimensional embeddings (3x-8x shorter vectors) cut storage and search costs materially
  • • Instruction-following rerank-2.5 and rerank-2.5-lite let you steer ranking behavior in plain language

What frustrates them

  • • Terms grant Voyage an irrevocable license to train on API customer data by default
  • • Usage-based pricing at $0.12-$0.18 per million tokens can balloon for high-frequency agents
  • • A community benchmarker found a small Jina model outperformed Voyage for their use case
  • • Circular import bug in voyageai v0.3.5 broke lazy import systems before being fixed

Researched Oct 14, 2026

Who should pick which

  • Enterprise RAG developer
    Pick: Voyage AI

    Because Voyage offers domain-specific embeddings, long-context support (32K tokens), and low-dimensional vectors that reduce storage costs—ideal for high-accuracy document retrieval in regulated industries.

  • Privacy-conscious mobile user
    Pick: Iris Android

    Because Iris runs entirely offline on Android, keeping all data on device, with no internet dependency after model download—perfect for sensitive conversations.

  • Startup building multimodal RAG
    Pick: Voyage AI

    Because Voyage's upcoming voyage-multimodal-3.5 enables embedding images along with text, and the Batch API handles large workloads without breaking the bank.

  • Student testing LLMs on phone
    Pick: Iris Android

    Because it's free, supports custom GGUF models, and works offline—ideal for learning about on-device inference without cloud costs.

Frequently Asked Questions

Iris Android vs Voyage AI: which should you choose?

Buy Voyage AI if you're an enterprise building high-accuracy RAG on domain-specific documents and need advanced embeddings/rerankers. Choose Iris Android if you're a developer or privacy enthusiast wanting to run LLMs offline on your phone for free. They serve completely different needs.

Q: Does Voyage AI offer a free tier?

A: No, Voyage AI requires contacting sales for pricing; there is no self-serve free tier.

Q: Can Iris Android handle images or audio?

A: No, Iris only supports text-based LLMs; no multimodal capabilities.

Q: Which tool is better for legal document retrieval?

A: Voyage AI, because it offers a legal-specific embedding model and long-context (32K) support.

Q: Is Iris Android open source?

A: The description doesn't specify open-source; it's based on llama.cpp (open source) but the app itself may not be.

Q: Does Voyage AI integrate with vector databases?

A: Yes, it's modular and works with any vector DB or LLM via API.

Q: Can I use Voyage AI without internet?

A: No, Voyage is a cloud API; internet is required.

Q: What model formats does Iris Android support?

A: It supports GGUF format models, and you can import custom GGUF files.

Q: Which tool is more suitable for a mobile developer?

A: Iris Android, since it's specifically designed for on-device inference on Android.

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Last reviewed: July 3, 2026