Epsilla vs Voyage AI

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Epsilla RAG	Voyage AI RAG
Tagline	Agent-as-a-Service platform with managed RAG and a no-code builder for vertical enterprise AI.	State-of-the-art embedding models and rerankers purpose-built for retrieval and RAG.
Category	RAG	RAG
Pricing	Freemium· Free Tier: $0/month · Starter Tier: $29/month · Professional Tier: $249/month · AI Concierge: $2,499/month · Enterprise Tier: Custom/month	Freemium· Free tier: 200M free text tokens per account for current models (50M for older specialized). Text embeddings $0.00002–$0.00018 per 1K tokens depending on model tier. Rerankers $0.00002–$0.00005 per 1K tokens after 200M free. Multimodal $0.12 per 1M text tokens + $0.60 per 1B pixels. Batch API 33% discount. File storage $0.05/GB/month.
Model	Multi-model	in-house (voyage-3.5, voyage-4 series, voyage-code-3, voyage-finance-2, voyage-law-2, voyage-multimodal-3.5, voyage-context-3, rerank-2.5)
Editorial score	7.3 / 10	—
Use cases	enterprise-ragai-agentsknowledge-base-chatbotsvector-searchvertical-ai	Production RAG chatbot over proprietary docsTwo-stage retrieval with embed + rerankCode search across a monorepoLegal contract semantic searchFinancial filings and research retrievalMultimodal image-and-text searchLong-context document embedding (32K tokens)Context-aware chunk embedding for dense passagesBatch embedding of large historical corporaMongoDB Atlas Vector Search backends
Pros	Free tier with 10M vectors and no credit card required No-code agent builder usable by non-engineers On-prem and private-cloud deployment for regulated industries Multi-tenancy, SSO and access controls built in Underlying vector engine has an open-source lineage on GitHub	Consistently near the top of MTEB and BEIR retrieval leaderboards — measurable recall gains over OpenAI text-embedding-3-large in most public evaluations. Short output dimensions (as low as 256 or 512) cut vector storage and ANN latency 3x–8x versus 1536/3072-dim competitors. Domain-tuned models (code, finance, legal) meaningfully outperform general embeddings on in-domain corpora. voyage-context-3 embeds chunks with awareness of surrounding document context, reducing the classic 'lost context' problem in fixed-window chunking. Rerank-2.5 with instruction-following gives a clean two-stage retrieval pipeline without training a custom cross-encoder. Generous 200M-token free tier per account makes prototyping and small production workloads essentially free. Batch API offers a 33% discount for large offline embedding jobs. MongoDB acquisition (2025) means tight, ongoing integration with Atlas Vector Search.
Cons	Specific supported LLMs and embedding models not listed on marketing pages Hosted agent platform itself is closed-source Professional tier jumps steeply from $29 to $249/mo Vertical focus may feel heavyweight for a single small chatbot	API-only closed models — no self-hosting option, so latency-sensitive or air-gapped deployments are ruled out. Not an end-to-end RAG stack — you still need a vector database, LLM, and orchestration layer, which increases integration surface. Post-MongoDB acquisition, product roadmap and standalone longevity depend on MongoDB's priorities. Domain models cover finance, legal, and code but nothing else — medical, scientific, or multilingual-heavy corpora fall back to general models. Documentation is competent but sparser than OpenAI's or Cohere's — fewer end-to-end recipes for advanced patterns like hybrid search or query expansion. Pricing per token is competitive but not the cheapest — self-hosted open models (e.g. BGE, E5) are free at inference if you have GPUs.
Website	epsilla.com	www.voyageai.com

Pick Epsilla if

✅ Free tier with 10M vectors and no credit card required
✅ No-code agent builder usable by non-engineers
✅ On-prem and private-cloud deployment for regulated industries
✅ Multi-tenancy, SSO and access controls built in

Pick Voyage AI if

✅ Consistently near the top of MTEB and BEIR retrieval leaderboards — measurable recall gains over OpenAI text-embedding-3-large in most public evaluations.
✅ Short output dimensions (as low as 256 or 512) cut vector storage and ANN latency 3x–8x versus 1536/3072-dim competitors.
✅ Domain-tuned models (code, finance, legal) meaningfully outperform general embeddings on in-domain corpora.
✅ voyage-context-3 embeds chunks with awareness of surrounding document context, reducing the classic 'lost context' problem in fixed-window chunking.

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