Business Case
Why Anvesh — TCO & ROI
How Anvesh delivers 85% lower total cost of ownership while matching Elasticsearch and Pinecone on capabilities.
The Problem with Existing Solutions
Enterprise search infrastructure is bloated and expensive:
- Elasticsearch requires 32–64 GB JVM heaps per node, driving massive RAM costs
- Pinecone / Weaviate charge $0.10+ per 1M vectors/month with vendor lock-in
- Solr demands a full JVM stack and ZooKeeper coordination overhead
- Separate systems needed for text search, vector retrieval, and image OCR — multiple ops burdens
Anvesh Cost Advantage
| Cost Component | Elasticsearch (3-node) | Anvesh (3-node) | Savings |
|---|---|---|---|
| Compute (monthly) | $1,440 (r6g.xlarge × 3) | $180 (A1.Flex 2-OCPU × 3) | 87% |
| Storage (1 TB) | $230 (gp3 SSD) | $23 (OCI / S3 Object Storage) | 90% |
| Data transfer | $90 | $0 (OCI free egress) | 100% |
| Operations FTE | 0.5 FTE ($5K/mo) | 0.1 FTE ($1K/mo) | 80% |
| Total monthly | $6,760 | $1,203 | 82% |
Why It's Cheaper & Faster
- No JVM overhead — Node.js microservices use ~70 MB RAM vs 32+ GB for Elasticsearch
- SQ8 Vector Quantization — 75% vector memory compression with >98% recall accuracy
- ARM64 native — runs on Ampere A1 instances at 1/3 the cost of x86
- Tiered storage — cold data on $0.023/GB object storage, not $0.10/GB SSD
- GitOps Config-as-Code — automate index updates via CLI or Terraform with zero pod restarts
Feature Parity
Anvesh matches or exceeds heavy search stacks:
- ✅ Full-text BM25 + dense vector hybrid search (RRF)
- ✅ Built-in non-AI OCR, color palette & motif extraction
- ✅ Real-time aggregations and faceting
- ✅ 3-tier protective throttling & concurrency slots
- ✅ Zero-document-loss DLQ
- ✅ Built-in web crawler (Spider)
- ✅ Declarative Config-as-Code & TypeScript SDK