Brain's performance advantage
Production-grade memory layer built for speed and cost. See how Brain aims to compare to generic vector databases and other memory solutions across latency, quality, and pricing.
Target performance
The figures on this page are Brain's internal targets and design goals, not independently verified results. Independent, reproducible benchmarks are in progress; numbers will be updated as they land.
Latency (P99 Read Time) — target
Brain is designed for low-latency retrieval in production agent systems. The targets below are what self-hosted and Cloud deployments aim for; the Cloud API targets p99 < 200ms globally.
| Solution | P99 Latency (target) | Deployment |
|---|---|---|
| Brain (self-hosted) | < 200ms | Your infrastructure |
| Brain (Cloud) | < 200ms | Global multi-region |
| Zep (API) | 200–400ms | Cloud API |
| Mem0 (API) | 500–1000ms | Cloud API |
| Pinecone (Vector DB) | 50–200ms | Cloud + network |
| Qdrant (Vector DB) | 100–300ms | Cloud or self-hosted |
Note: Brain rows are internal targets, not independently verified measurements. Cloud P99 target includes network round-trip. Competitor figures are drawn from public API documentation and benchmark papers where available.
Memory Quality (long-context recall) — target
Memory quality measures how accurately and relevantly a system retrieves information over long contexts. Higher scores indicate fewer false positives and better relevance ranking.
| Solution | Recall score | Notes |
|---|---|---|
| Brain | 85%+ (target) | Internal long-context suite; purpose-built retrieval + fusion |
| Mem0 | 70–75% | Estimated from public benchmarks |
| Generic RAG (Vector DB only) | 60–70% | No memory-specific optimization |
Methodology: Brain's 85%+ is a target measured against an internal long-context test suite — it is not an official LongMemEval submission. We reference the LongMemEval paper for its methodology (retrieval accuracy over long conversation histories); an independent, published LongMemEval run is in progress. Mem0's score is estimated from their public disclosures.
Cost Per 1K Vectors/Month — preview
Pricing for storing and querying vectors at scale. Lower costs enable larger memory windows and longer conversation histories.
| Solution | Cost / 1K Vectors | Model |
|---|---|---|
| Brain (Cloud) | $0.001 (preview) | Pay-as-you-go |
| Brain (Open Source) | Free | Self-hosted, Apache 2.0 |
| Mem0 (API) | $0.01–0.05 | Estimated from public pricing |
| Pinecone (Standard) | $0.096 | Pod-based, high minimum |
| Qdrant (Cloud) | $0.02–0.05 | Pay-as-you-go, variable |
| Zilliz (Cloud) | Contact sales | Enterprise pricing |
Assumptions: 1M vectors, typical query volume (~100 queries/day), 30-day retention. Brain Cloud pricing is preview/target, not a final GA rate. Mem0 and Qdrant estimates based on their public pricing pages as of May 2026. Vector DB costs shown for reference — they solve a different problem (semantic search) than Brain (memory layer).
When to use what
Brain (self-hosted)
- Teams that need memory running on their own infrastructure
- Privacy-first deployments where data stays in your environment
- Full control over the open-source server and its data
Brain Cloud
- Multi-tenant AI systems serving many users with shared memory state
- Enterprise deployments needing GDPR compliance and audit logs
- Global applications targeting consistent p99 < 200ms latency across regions
Brain vs. Vector DBs
- Use Brain for: Agent memory, conversation history, user preferences. Optimized for relevance at recall time.
- Use Vector DBs for: Semantic search on large document corpora, static knowledge bases. Optimized for similarity.
Frequently asked
Are these numbers measured or targets?
They are targets and design goals, not independently verified results. Brain's latency and quality figures come from internal testing; competitor figures come from public docs and benchmark papers. Independent, reproducible benchmarks are in progress and this page will be updated as they land.
How do you define P99 latency?
P99 is the 99th percentile response time. For self-hosted, it's end-to-end retrieval time on your own server. For Cloud, it includes full network round-trip from client to API endpoint. The values shown are the targets we build against.
Why is Brain designed to be faster than other memory solutions?
Brain is purpose-built for memory retrieval with optimized indexing, retrieval fusion, and minimal overhead. Other solutions either use generic vector DBs or add layers of post-processing on top of embeddings, which adds latency.
Can I use Brain with other vector DBs?
You don't need one — Brain ships its own built-in vector index (a memory-mapped arena with an in-RAM HNSW index), so it does similarity search itself. Brain can still sit alongside an existing vector DB or embedding system where it remains your system of record; Brain adds the memory-specific logic (deduplication, fusion, relevance) on top.
Do you have benchmarks for my use case?
If you have specific requirements or workloads, reach out at hello@arc-labs.ai. We can run custom benchmarks to compare Brain against your current stack.