AI Databases & Vector Stores
How do I contact ApertureData sales for the Custom/Enterprise plan and what info should I bring (data size, QPS, modalities, deployment)?
7 min read
Most teams reach out to ApertureData sales when they realize their multimodal workloads have outgrown “just a vector DB” and they need a foundational data layer that can handle images, videos, documents, text, audio, and embeddings at production scale. The good news: you can contact our sales and solutions team directly, and bringing a few concrete numbers (data size, QPS, modalities, deployment needs) will dramatically speed up sizing, pricing, and architecture discussions.
Quick Answer: You can contact ApertureData sales for the Custom/Enterprise plan via the contact form at aperturedata.io/contact-us or by emailing team@aperturedata.io. For a productive conversation, come prepared with estimates for data size, queries per second (QPS), modalities you need (images, video, docs, audio, text, embeddings), and your preferred deployment model (cloud, VPC, or on‑prem).
Frequently Asked Questions
How do I contact ApertureData sales for a Custom/Enterprise plan?
Short Answer: Use the contact form at aperturedata.io/contact-us or email team@aperturedata.io with a brief description of your multimodal AI use case and scale.
Expanded Explanation:
For Custom/Enterprise plans, the fastest route is the contact form on our site. That routes you directly to our sales and solutions engineering team, who specialize in sizing ApertureDB for production GenAI, RAG, GraphRAG, and agent workloads. You can also reach us by email at team@aperturedata.io if you prefer to share context from your own templates or security questionnaires.
Once you reach out, we typically schedule a short discovery call to understand your data shapes, performance targets (latency/QPS), and deployment constraints (e.g., VPC-only, on-prem, specific cloud). From there, we propose an architecture and pricing tier aligned with your multimodal data volume, retrieval patterns, and reliability requirements.
Key Takeaways:
- Use the contact form or email team@aperturedata.io to talk to sales.
- Expect a discovery call focused on your data, performance, and deployment needs—not generic “AI platform” talk.
What information should I prepare before talking to ApertureData sales?
Short Answer: Bring rough numbers on data size, expected QPS, which modalities you’ll store (images, video, documents, text, audio, embeddings), and how you want to deploy (ApertureDB Cloud, your VPC, or on‑prem).
Expanded Explanation:
Custom/Enterprise conversations go a lot faster when you have a basic system picture: how much data you’re managing, what your query patterns look like, and what constraints your infra and security teams care about. ApertureDB is a vector + graph database optimized for multimodal workloads, so we’ll ask questions that go beyond “how many vectors” to include your metadata volume, graph structure, and media footprint.
Don’t worry about exact numbers—it’s fine to share ranges or growth expectations (e.g., “We’ll grow to 500M images in 12–18 months,” or “We’re targeting 5–10K QPS at sub‑100ms”). The goal is to size a system that delivers sub‑10ms vector search, low-latency graph traversal, and predictable TCO for your real workloads.
Steps:
- Estimate data size: number of items (images, videos, docs, audio files, text records), average file size, and current/expected embedding counts.
- Estimate load and performance: target QPS, latency SLOs, concurrency patterns (bursty vs steady), and peak vs average traffic.
- List modalities and use cases: modalities (images, videos, documents, text, audio, annotations, metadata), plus primary workloads (RAG, GraphRAG, agent memory, dataset prep, visual debugging).
Why do data size and QPS matter so much for the Custom/Enterprise plan?
Short Answer: Data size drives storage and index design, while QPS and latency targets drive compute, replication, and architecture choices required to meet your SLOs.
Expanded Explanation:
ApertureDB’s strength is serving multimodal workloads at scale—sub‑10ms vector search, billions of metadata entries, and ~15ms graph lookups at high QPS. To deliver that in your environment, we need to understand both how big your data is and how hard you plan to hit it.
Larger data volumes (e.g., hundreds of millions to billions of embeddings, plus associated media and metadata) require careful planning around sharding, replicas, and storage tiers. Higher QPS (e.g., >5K queries/sec) and tight latency targets (<100ms end‑to‑end) influence how we configure vector indices, graph layout, and read vs write paths. That’s why the same software looks different architecturally for a 50K-vector prototype vs a production system pushing 10K+ QPS with complex GraphRAG queries.
Comparison Snapshot:
- Option A: Small to mid-scale (≤100M items, ≤1K QPS): Focus on rapid adoption, lower instance counts, and headroom for growth.
- Option B: Large scale / high QPS (100M+ items, 1K–10K+ QPS): Focus on index tuning, replication, and topology for low-latency, high‑reliability workloads.
- Best for: Custom/Enterprise is particularly useful once you care about consistent performance under load, predictable TCO, and SLAs for multimodal retrieval—not just experimentation.
What modalities and deployment details should I share with sales?
Short Answer: Be explicit about all modalities (images, videos, documents, text, audio, embeddings, annotations, metadata) and whether you need ApertureDB Cloud, your own VPC, or on‑prem deployment with specific compliance/security constraints.
Expanded Explanation:
ApertureDB is a multimodal-native vector + graph database: it stores and queries text, images, videos, audio, documents, embeddings, and metadata in one system. To size and architect the right deployment, we need to know the mix and relative weight of these modalities and how you plan to use them (e.g., GraphRAG over PDFs + images, video analytics with bounding boxes, agent memory that ties chat transcripts to media).
Deployment matters just as much. Some teams adopt ApertureDB Cloud for speed (prototype → production 10× faster), while others need VPC or on‑prem deployments to meet regulatory, data residency, or security requirements. Sharing your preferences early helps us align on options like AWS/GCP region, network topology, RBAC needs, and how you integrate with your existing observability and CI/CD stack.
What You Need:
- Modality breakdown:
- Media: images, videos, audio files
- Textual: documents, unstructured text, transcripts
- AI artifacts: embeddings (how many per item, dimensions), model types
- Structure: metadata fields, relationships/graph edges, annotations/bounding boxes
- Deployment requirements:
- Preferred model: ApertureDB Cloud, your cloud/VPC, or on‑prem
- Compliance & security: SOC2 needs, data residency, network isolation, SSO/RBAC, SSL termination, and any must‑have security controls
How does this information influence pricing, architecture, and time to production?
Short Answer: Your data size, QPS, modalities, and deployment constraints shape the resource footprint, topology, and support level you need—directly impacting pricing, SLAs, and how quickly we can get you to production.
Expanded Explanation:
Custom/Enterprise plans are not “one size fits all” because multimodal workloads aren’t. A team running a few hundred QPS of RAG over documents has very different needs from a robotics company pushing real‑time image/video queries at 10K+ QPS. When we know your workload shape, we can propose a design that hits your performance goals without over-provisioning—delivering low and predictable TCO.
With the right inputs, we can usually move from initial conversation to a concrete architecture and rollout plan quickly. You get clarity on how ApertureDB will act as your foundational data layer for AI—unifying media, metadata, and embeddings with vector + graph retrieval—and how that translates to SLAs, support tiers, and estimated costs. Many teams save 6–9 months of infrastructure setup and multiple fragile integrations by consolidating onto one system instead of stitching together separate storage, vector DB, and graph DB components.
Why It Matters:
- Better fit, lower TCO: Accurate sizing avoids both underpowered deployments (missed SLOs) and overkill clusters (wasted spend).
- Faster path to stable production: Clear requirements let us design for your QPS, latency, and reliability targets from day one, so your team can focus on agents and applications instead of babysitting the data layer.
Quick Recap
To contact ApertureData sales for a Custom/Enterprise plan, use the contact form or email team@aperturedata.io. For a useful first conversation, come prepared with approximate data size, target QPS and latency, the modalities you need (images, videos, documents, text, audio, embeddings, annotations, metadata), and your deployment requirements (ApertureDB Cloud, your VPC, or on‑prem with specific security/compliance needs). These details let us design and price an ApertureDB deployment that serves as a high‑performance, reliable multimodal memory layer for your GenAI, RAG/GraphRAG, and intelligent agent workloads.