pumaDB vs Voyage AI

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

Analysis reviewed Live tool data as of 2026-10-09
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At a glance

DimensionpumaDBVoyage AI
PricingFree tier (20 tables, 1,000 rows each); paid plans TBDContact sales (likely enterprise-tier)
Core FunctionPersistent memory / JSON database for AI agentsEmbedding and reranker models for search/retrieval
IntegrationMCP for ChatGPT, Claude, Codex; REST API for server-sidePlugs into any vector DB or LLM via API
Best ForAgent workflows needing lightweight persistent stateEnterprise RAG with domain-specific retrieval accuracy
Key Limitation1,000 rows per table (20 tables); no relational DB featuresNo free tier; sales-led engagement required
Latest NewsNo recent news capturedVoyage 4 series and voyage-multimodal-3.5 announced

Voyage AI and pumaDB solve entirely different problems – Voyage is for retrieval accuracy in complex enterprise RAG, while pumaDB is a simple memory layer for AI agents. If your need is semantic search over legal or financial docs with long contexts, Voyage is the specialist. If you're building agentic workflows (ChatGPT, Claude, Codex) that need persistent state without a database, pumaDB's MCP-native approach is a natural fit. They are complementary, not competitive.

Try pumaDBTry Voyage AI
pumaDB
pumaDB

Hosted MCP and REST memory that keeps AI agent context consistent across ChatGPT, Claude, Codex, and your own agents.

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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/mo
$99/month
Custom
Consumption-based pricing (rates not published on page)
Popularity
3 views
7.4k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
APIWeb
WebAPI
Categories
🧠 Agent Memory & Runtimes🔌 MCP Servers & Agent Tooling
🗄️ Vector Databases & Retrieval
Features
Shared memory for AI agents via MCP
Streamable HTTP MCP endpoint at api.pumadb.ai/mcp
Email sign-in with OAuth handled by pumaDB, no key pasted into the client
OAuth discovery and dynamic client registration
REST API under /v1/{table} with puma_live_ bearer keys
Schema-less JSON tables created on first write
Rows stamped with id, created_at, and updated_at
Automatic version history, last 10 versions per row, retained 30 days
Row-level restore from archived versions
Batch operations and upsert endpoints
Update-row endpoint with filtered updates
Viewer and download links for sharing results
Named API keys per app or environment
Per-key rate limits (30 writes, 60 reads per minute)
Magic-link authentication for API key creation
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
ChatGPT
Claude
Codex
OpenClaw

What real users say: pumaDB 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.

pumaDB

7 mentions across 1 sources · 75% positive (averaged across 1 source)

Product Hunt

What users praise

  • • Dead simple setup: no database project or schema design needed.
  • • MCP and REST APIs make integration with ChatGPT and Claude trivial.
  • • Automatic version history for all updates and deletes.
  • • Consolidated 'remember' MCP tool with safety metadata.

What frustrates them

  • • No automatic memory capture—agents must explicitly save state.
  • • Memory inspection and correction tools are unaddressed by builder.
  • • Limited community presence outside Product Hunt launch thread.
  • • Free tier table/row limits may not suit serious production workloads.

Researched Jul 2, 2026

Voyage AI

64 mentions across 6 sources · 54% positive — mixed (weighted across 6 sources)

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

What users praise

  • • Domain-tuned legal and finance embedders cut irrelevant docs by 25% in the Harvey case
  • • 3x-8x shorter vectors materially cut vectorDB storage and search costs
  • • rerank-2.5 instruction following lets you steer ranking behavior in plain language
  • • voyage-multimodal-3.5 handles images and text in a single retrieval pipeline

What frustrates them

  • • Default terms train on API customer data with a perpetual, irrevocable license grant
  • • Per-million-token pricing gets expensive fast for high-frequency agent RAG pipelines
  • • A small Jina model reportedly beat Voyage on retrieval in one public benchmark
  • • Open-source ecosystem still thin — Python library has only 114 GitHub stars

Researched Oct 7, 2026

Who should pick which

  • Enterprise RAG developer (legal documents)
    Pick: Voyage AI

    Voyage's legal-specialized model, 32K context, and instruction-following reranker maximize retrieval accuracy for complex legal texts.

  • AI agent builder (ChatGPT/Claude automation)
    Pick: pumaDB

    pumaDB's MCP integration provides instant persistent memory for agents; no database setup needed, with automatic versioning.

  • Solo founder prototyping a RAG app
    Pick: Voyage AI

    If the founder needs domain-specific embeddings for a niche dataset, Voyage's low-dimensional models offer cost-efficient vector storage. However, they may need to pay.

  • System integrator building agent handoff
    Pick: pumaDB

    pumaDB's shareable links and batch upsert endpoints simplify passing context between agents across sessions.

  • Startup wanting multimodal retrieval
    Pick: Voyage AI

    Voyage's announced multimodal model (voyage-multimodal-3.5) enables retrieval from images + text, a key feature for modern RAG.

Frequently Asked Questions

pumaDB vs Voyage AI: which should you choose?

Voyage AI and pumaDB solve entirely different problems – Voyage is for retrieval accuracy in complex enterprise RAG, while pumaDB is a simple memory layer for AI agents. If your need is semantic search over legal or financial docs with long contexts, Voyage is the specialist. If you're building agentic workflows (ChatGPT, Claude, Codex) that need persistent state without a database, pumaDB's MCP-native approach is a natural fit. They are complementary, not competitive.

Can pumaDB replace a vector database for embeddings?

No – pumaDB is a simple JSON store, not optimized for vector search or similarity. It's meant for agent memory, not semantic retrieval.

Does Voyage AI offer a free tier?

No – Voyage AI uses a contact-sales pricing model; there is no self-serve free tier.

Can I use pumaDB with Voyage AI embeddings together?

Yes – they are complementary. Voyage embeddings for retrieval, pumaDB for storing agent state.

Which tool supports multimodal (text+image) retrieval?

Voyage AI announced voyage-multimodal-3.5 for multimodal embeddings. pumaDB stores JSON only.

What is pumaDB's row limit?

Free tier: 1,000 rows per table, up to 20 tables (20,000 rows total).

Does Voyage AI support long documents?

Yes – up to 32K tokens with standard models; voyage-context-3 provides chunk-level context.

Can I self-host Voyage AI models?

No – Voyage AI is a cloud API; no self-hosted option mentioned.

Does pumaDB support authentication for agents?

Yes – MCP clients use email OAuth; server apps use bearer tokens (puma_live_* keys).

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Explore each tool further

pumaDB
View pumaDB reviewpumaDB alternatives
Voyage AI
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Last reviewed: July 2, 2026