Mira vs Voyage AI
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Mira | Voyage AI |
|---|---|---|
| Pricing | Free (open-source, self-hosted) | Contact us (pay-as-you-go or enterprise contract) |
| Primary Function | AI code reviewer for GitHub PRs | Embedding models & rerankers for RAG |
| Deployment | Self-hosted (Docker, SQLite/PostgreSQL) | Cloud API |
| Target Users | Engineering teams (privacy-conscious, cost-aware) | Enterprise RAG teams |
| Data Control | Full control on own infrastructure | Data sent to Voyage API (SOC 2, HIPAA compliant) |
| Customization | Custom per-repo rules, global rules, any LLM via OpenRouter | Fine-tuning available for enterprise |
Choose Voyage AI if your priority is building a high-accuracy RAG pipeline with domain-specialized embeddings and rerankers, and you have budget for enterprise pricing. Choose Mira if you need a free, self-hosted AI code reviewer with full privacy, customizable rules, and the flexibility to use any LLM. They solve entirely different problems, so your decision hinges on whether you need retrieval or code review.
Self-hosted open-source AI code reviewer that indexes your repo and works with any LLM.
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Specialized embedding models and rerankers for high-accuracy enterprise RAG, with 32K-token context and multimodal support.
Visit WebsiteWhat real users say: Mira 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.
Mira
86 mentions across 6 sources · 40% positive — mixed
Hacker News, YouTube, Product Hunt, App Store, GitHub, Lemmy
What users praise
- • Full-repo indexing gives project-wide context, not just a diff.
- • Bring-your-own-LLM: pay providers directly, no markup.
- • Fully open-source and self-hosted; complete data control.
- • Severity and confidence scoring helps triage review comments.
What frustrates them
- • Setup requires Docker and DevOps knowledge; not turnkey.
- • Documentation is sparse for advanced setups like Postgres.
- • GitHub Actions integration requires manual webhook setup.
- • Learning loop's quarantine can hide valid findings.
Researched Aug 27, 2026
Voyage AI
41 mentions across 4 sources · 48% positive — mixed
Hacker News, YouTube, Stack Overflow, Lemmy
What users praise
- • High accuracy for RAG retrieval, especially with the reranker models.
- • Domain-specific models for finance, legal, and code deliver better results.
- • Low-dimensional embeddings cut vector storage costs by up to 8x.
- • Supports long contexts up to 32K tokens, useful for large documents.
What frustrates them
- • Data-training clause in terms raises privacy red flags for enterprises.
- • Pricing is opaque, requiring contact with sales.
- • Community support is sparse — few Stack Overflow answers or forum threads.
- • No clear free tier, so trying it costs time with sales or API credits.
Researched Aug 26, 2026
Who should pick which
- Enterprise RAG developerPick: Voyage AI
Voyage AI offers domain-specialized embeddings (finance, legal, code) and 32K token context, essential for high-accuracy retrieval on complex documents. Enterprise compliance (SOC 2, HIPAA) and low-dimensional embeddings reduce storage costs.
- Privacy-conscious engineering leadPick: Mira
Mira is self-hosted and open-source, so no code leaves your infrastructure. You can use any LLM (including local ones) and customize review rules. Latest news confirms full control with no paid tier.
- Startup building an MVP with RAGPick: Mira
Voyage AI requires sales calls and has opaque pricing. Mira is free and easy to set up for code review, but for RAG you might consider other free embedding APIs. If your startup has budget, Voyage AI could work, but it's not ideal for tight budgets.
- GitHub team wanting automated PR reviewsPick: Mira
Mira integrates directly as a GitHub App, indexes your full repo, and uses any LLM. Features like inline comments, severity scores, and one-click suggestions reduce manual review. Latest v0.3.1 adds review thinking mode for adjustable depth.
- Data scientist needing multimodal embeddingsPick: Voyage AI
Voyage AI announced voyage-multimodal-3.5, enabling retrieval across text and images. No comparable feature in Mira, which is purely code-focused.
Frequently Asked Questions
Mira vs Voyage AI: which should you choose?
Choose Voyage AI if your priority is building a high-accuracy RAG pipeline with domain-specialized embeddings and rerankers, and you have budget for enterprise pricing. Choose Mira if you need a free, self-hosted AI code reviewer with full privacy, customizable rules, and the flexibility to use any LLM. They solve entirely different problems, so your decision hinges on whether you need retrieval or code review.
Which tool is better for RAG pipelines?
Voyage AI is purpose-built for RAG with domain-optimized embeddings, rerankers, and long context. Mira is a code reviewer and not suitable for RAG.
Can I use Mira for code review outside of GitHub?
No, Mira currently only supports GitHub. The documentation does not mention GitLab or Bitbucket integration.
Is Voyage AI free to try?
Voyage AI requires contacting sales for pricing and access. There is no free trial mentioned in the facts.
Does Mira support any LLM?
Yes. Mira uses OpenRouter and supports Anthropic, OpenAI, Google, DeepSeek, MiniMax, and any model available through OpenRouter. You can also configure custom models via MIRA_MODELS_JSON_PATH (v0.3.1).
Which tool offers better data privacy?
Mira, because it is self-hosted and open-source. Your code never leaves your infrastructure. Voyage AI is cloud-based but SOC 2 and HIPAA compliant.
Can Voyage AI generate code reviews?
No, Voyage AI is an embedding and reranking API, not a code review tool. Use Mira for code reviews.
Are there any recent updates to Mira's pricing?
No, Mira remains free and open-source. The latest news (v0.4.0, v0.3.0, v0.3.1) does not introduce any paid features.
Does Voyage AI have a multimodal model?
Yes, Voyage AI announced voyage-multimodal-3.5, enabling retrieval across text and images. This is in addition to their text embedding models.
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Last reviewed: July 3, 2026