PRODUCT · ENGINEERING · QUANTS
ATOM Feed
ATOM data flowing directly into your stack
Structured inference pricing data delivered direct to your systems. Complete SKU-level coverage with 32 queryable fields per record. Built for engineering teams orchestrating inference, quants modeling AI costs, and platforms embedding rates into their products.
OVERVIEW
Pricing Data, Not a Dashboard
What Feed is, what it does, why it matters
"Stop scraping vendor pricing pages. Stop maintaining custom parsers. Stop reconciling units across 51 vendors. Feed gives you the answer."
Feed is ATOM's raw data product. It delivers our complete inference pricing dataset, normalized and structured, directly into your engineering stack. No dashboard, no UI, no abstraction layer. Just the data, ready to power whatever you build on top of it.
Each record carries 32 fields covering vendor, model, modality, pricing direction, normalized rates, deployment method, region, and full audit metadata. Engineered for cost-aware routing logic, model selection algorithms, FinOps automation, and quantitative cost modeling.
THE SCHEMA
32 Fields Per SKU, Normalized
Sample record format and field coverage
Feed records follow a stable schema. Every SKU includes vendor identifiers, model metadata, capability flags, normalized pricing, original pricing, regional context, and audit fields. Normalized prices use a deterministic conversion formula stored alongside the record.
GET/v1/sku_index
LIVE
{ "sku_id": "openai_gpt_4o_input_global", "vendor_name": "OpenAI", "vendor_type": "Model Developer", "model_name": "GPT-4o", "model_family": "GPT-4", "modality": "Text", "context_window": 128000, "tool_calling": true, "open_source": false, "direction": "Input", "original_price": 2.50, "normalized_price": 0.0025, "normalized_unit": "USD per 1K tokens", "delivery_method": "API", "region": "Global", "verification_date": "2026-05-04", // ... 32 fields per SKU }
200 OK · 12ms
application/json · full schema
Complete Field Coverage — 32 fields per record
CAPABILITIES
Engineering-Grade Data Infrastructure
What Feed delivers that nothing else does
FULL SCHEMA
32 Fields Per SKU
Every record carries vendor identifiers, model capability metadata, normalized and original pricing, deployment context, regional availability, and audit metadata. Filter or join on any field.
NORMALIZATION
Deterministic Cross-Vendor Comparison
Every price is normalized to a standard unit using deterministic formulas stored alongside the record. Compare apples-to-apples across 51 vendors without writing parsers.
UPDATE FREQUENCY
Weekly Refresh, Audit Trail Included
Pricing data refreshes weekly. Every record carries a verification_date confirming when the price was last validated against the vendor's published page.
DELIVERY OPTIONS
Pull, Push, or Direct DB Access
REST API, scheduled CSV/Parquet exports, or direct read access to a dedicated Postgres replica. Pick the integration pattern that fits your stack.
DELIVERY
Three Integration Patterns
Pull, push, or direct database access
Feed supports three delivery patterns. Each is engineered for a specific operational profile. All three deliver the same 32-field schema.
| Pattern | How It Works |
|---|---|
| REST API | Authenticated GET endpoints for sku_index, price_index (history), index_values, model_registry, and vendor_registry. Standard query parameters for filtering. JSON responses. Sub-100ms latency. |
| Scheduled Exports | Weekly CSV or Parquet drops to your S3, GCS, or Azure Blob bucket. Full snapshot or incremental delta. Schema-stable. Built for warehouse ingestion. |
| Direct DB Access | Read-only Postgres replica scoped to your account. Query the live schema directly with SQL. Built for engineering teams that prefer database-native access patterns. |
USE CASES
What Engineering Teams Build with Feed
Three patterns we see in production
COST-AWARE ROUTING
Pick the cheapest model meeting capability requirements
Inference orchestration platforms route requests to the most cost-efficient SKU meeting capability constraints. Filter on context_window, tool_calling, modality, then sort by normalized_price. Refresh weekly.
QUANT MODELING
Build pricing forecasts and cost projections
Quant teams ingest Feed time series into proprietary models. Track price evolution across vendors. Build alpha signals from cost compression patterns and competitive pricing dynamics.
PRICING EMBEDS
Surface live rates inside your own product
AI-native platforms display real-time inference pricing inside their dashboards or SDKs. License Feed as a data layer, attribute ATOM as the source, focus engineering on your differentiation.
GET STARTED
Two Paths to Live Data
Evaluate first with a pilot, scale with annual licensing
01
Schedule a Call
30-minute walkthrough of the schema, delivery options, and your specific use case. We'll confirm fit before any commercial conversation.
02
Pilot or Annual
Start with a 3-month pilot to validate integration and data fit. Or move directly to annual licensing for production deployment.
03
Production Integration
Wire Feed into your stack with engineering support. SLA-backed delivery, audit trail, and ongoing schema stability.
EVALUATION
$25,000/ 3-month pilot
Validate integration and data fit before annual commitment.
PRODUCTION
From $100,000/ year
Full annual license. Custom pricing based on use case, delivery pattern, and volume.
3-month pilot agreement·Custom annual pricing on request·Volume discounts available
SECURITY & COMPLIANCE
Enterprise-Grade Infrastructure
SOC 2 Type II, ISO 27001:2022
ATOM is built on SOC 2 Type II certified platforms and ISO 27001:2022 certified data storage infrastructure. Our technology stack undergoes regular third-party security audits by accredited certification bodies. Our infrastructure providers maintain compliance with SOC 2, ISO 27001, ISO 27017 (Cloud Security), ISO 27701 (Privacy), and GDPR. ATOM inherits these security controls and data protection practices through our platform architecture.
