Ctx 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

DimensionCtxVoyage AI
PricingFree (open-source, MIT)Contact sales (enterprise pricing)
Primary Use CaseLocal search over coding agent session historyDomain-specific embedding & reranking for enterprise RAG
Key Strength50× token-efficient, natural language search, no cloud32K token context, low-dim vectors, fine-tuned models
DeploymentLocal SQLite, Rust CLI, no background serviceCloud API (SOC 2/HIPAA)
IntegrationsClaude Code, Codex, Cursor, Copilot CLI, Gemini CLI, MCPVector databases, LLMs
Target AudienceDevelopers using multiple coding agentsEnterprises needing high-accuracy retrieval on private data

Voyage AI and Ctx serve entirely different needs. Voyage AI is an enterprise-grade embedding API for RAG pipelines, ideal for finance/legal teams needing long-context, low-dimensional vectors with compliance. Ctx is a free, open-source CLI tool for searching coding agent history locally—perfect for developers who want to retain context across sessions without cloud dependency. Choose Voyage for production RAG accuracy; choose Ctx for agent workflow memory.

Ctx
Ctx

Ctx is a local, open-source CLI to search your coding-agent history, blame code lines to the agent session that wrote them, and graph your

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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
Freemium
Paid
Plans
$0
$20/mo
Consumption-based pricing (rates not published on page)
Popularity
6 views
7.4k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
CLI
WebAPI
Categories
💻 Code & Development📡 LLM Observability & Evals
🗄️ Vector Databases & Retrieval
Features
Local search across 32+ coding agent providers
Natural-language search, e.g. ctx search "failed migration"
BM25 lexical ranking with ranked, cited results by default
Search sessions and events that touched a file with --file
Multi-term search using repeated --term flags
ctx show event <id> --window 3 for context around a match
ctx show session <id> for a compact full transcript
Optional local semantic search with an on-device model, no API key
ctx blame by file, line range, commit or PR with evidence citations
ctx graph: codebase graph indexed in SQLite for symbol, dependency and impact questions
ctx sift: local compaction of noisy tool output before the agent reads it
Single executable shipping graph, search, blame and sift as of ctx 2.0
Automatic indexing on by default, completed before updates become visible
Runs entirely offline with no API keys
Export and restore portable history archives (ctx 2.2.0)
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
Integrations
Claude Code
Codex
Cursor
GitHub Copilot CLI
Gemini CLI
Antigravity
Pi
OpenCode
Factory Droid
Goose

What real users say: Ctx 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.

Ctx

86 mentions across 6 sources · 41% positive — mixed (averaged across 6 sources)

Hacker News, Product Hunt, Bluesky, Stack Overflow, GitHub, Lemmy

What users praise

  • • Fully local — no cloud, no API key, no background service.
  • • Written in Rust — fast indexing and sub-second search.
  • • Claims 50× token efficiency versus raw transcript search.
  • • Supports multiple major agent harnesses out of the box.

What frustrates them

  • • No GUI — CLI-only may intimidate some users.
  • • macOS install broken due to TLS redirect issue.
  • • No freshness/decay metadata — stale results can mislead.
  • • Some agent formats lack support (e.g., Pi, Shelley) — check your stack.

Researched Jul 6, 2026

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 engineer
    Pick: Voyage AI

    Needs high-accuracy retrieval on finance/legal docs with 32K context and low-dimensional vectors for cost-efficient storage. Voyage's domain-specific models and SOC 2 compliance are essential.

  • Solo developer using multiple coding agents
    Pick: Ctx

    Ctx indexes histories from Claude Code, Codex, Cursor, etc., enabling natural language search across sessions—all free and local, no vendor lock-in.

  • Startup building a multimodal RAG app
    Pick: Voyage AI

    Voyage-multimodal-3.5 and Voyage 4 series can handle images and text, while Batch API scales with large workloads. Pricing flexibility through sales contact.

  • Team debugging failed agent builds
    Pick: Ctx

    Ctx's ability to search past agent commands and outcomes speeds up debugging. JSON output and MCP integration allow scripting into workflows.

Frequently Asked Questions

Ctx vs Voyage AI: which should you choose?

Voyage AI and Ctx serve entirely different needs. Voyage AI is an enterprise-grade embedding API for RAG pipelines, ideal for finance/legal teams needing long-context, low-dimensional vectors with compliance. Ctx is a free, open-source CLI tool for searching coding agent history locally—perfect for developers who want to retain context across sessions without cloud dependency. Choose Voyage for production RAG accuracy; choose Ctx for agent workflow memory.

Can I use Voyage AI for free?

No, Voyage AI uses contact-based enterprise pricing with no free tier. It requires a sales engagement.

Is Ctx cloud-hosted or self-hosted?

Ctx is fully local. It stores data in a local SQLite database and requires no cloud dependency or API key.

Which tools does Ctx integrate with?

Ctx supports Claude Code, Codex, Cursor, Pi, OpenCode, Antigravity, Gemini CLI, Copilot CLI, Factory, Droid, and MCP.

What embedding dimensions does Voyage offer?

Voyage's low-dimensional embeddings are 3x-8x shorter than standard vectors, reducing storage costs.

Does Voyage support multimodal inputs?

Yes, Voyage offers voyage-multimodal-3.5 for multimodal retrieval.

Can I use Ctx with a team?

Ctx is local-first; a cloud sharing feature (ctx cloud) is mentioned as 'when available' but not yet shipped. For now, sharing requires manual transfer of SQLite files.

Is Voyage HIPAA compliant?

Yes, Voyage AI supports SOC 2 and HIPAA compliance for enterprise workloads.

Does Ctx require a subscription?

No, Ctx is free and open-source under MIT license.

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Last reviewed: July 6, 2026