Amazon FinSpace
Amazon FinSpace is a data management and analytics service built specifically for the financial services industry. It reduces the time you spend on time-consuming data preparation tasks and makes it easy for analysts to access and analyze petabytes of financial data with a few clicks.
Solid contracts, transparent operations, and an easy start — typically complete on four or five facets with one clear soft spot.
API Evangelist profiles Amazon FinSpace the way a machine reads it — 45 machine-readable artifacts across 6 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Amazon FinSpace scores 68.5/100 (strong), with a separate agent-readiness read of 48/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
How we profile Amazon FinSpace
Each block below is one kind of artifact we hold for Amazon FinSpace. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 6
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Amazon FinSpace Environments API
Manage FinSpace environments
Amazon FinSpace Kdb Clusters API
Manage kdb compute clusters
Amazon FinSpace Kdb Databases API
Manage kdb databases
Amazon FinSpace Kdb Environments API
Manage Managed kdb Insights environments
Amazon FinSpace Kdb Users API
Manage kdb users
Amazon FinSpace Tagging API
Tag FinSpace resources
Open Collections 1
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
Amazon FinSpace API
OPEN COLLECTIONPricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Amazon Finspace Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Amazon Finspace Finops
FINOPSFeatures 7
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Managed kdb Environment
Fully managed kdb+ (kdb+/q) compute infrastructure with HDB, RDB, Gateway, and Tickerplant cluster types.
Financial Analytics Workspace
Isolated FinSpace environments with preconfigured tools for financial data ingestion, preparation, and analysis.
Petabyte-Scale Data
Store and query petabytes of financial time-series data including tick, OHLCV, and alternative datasets.
kdb+ Cluster Autoscaling
Configure auto-scaling policies for kdb clusters to match intraday compute demand.
Multi-AZ Clusters
Deploy kdb clusters across multiple availability zones for high availability.
IAM-Integrated Users
Map FinSpace kdb users to IAM roles for fine-grained permission control.
SageMaker Integration
Access financial data from FinSpace environments directly within Amazon SageMaker Studio.
Scroll within the panel for all 7 ·
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Amazon Finspace Context
JSON-LDSpectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Amazon FinSpace API Rules
SPECTRALAmazon FinSpace API Rules
SPECTRALJSON Schema 5
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
Environment
JSON SCHEMAKxCluster
JSON SCHEMAKxDatabase
JSON SCHEMAKxEnvironment
JSON SCHEMAKxUser
JSON SCHEMAJSON Structure 5
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Amazon Finspace Environment Structure
JSON STRUCTUREAmazon Finspace Kx Cluster Structure
JSON STRUCTUREAmazon Finspace Kx Database Structure
JSON STRUCTUREAmazon Finspace Kx Environment Structure
JSON STRUCTUREAmazon Finspace Kx User Structure
JSON STRUCTUREExamples 5
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Security Posture 4
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Use Cases 5
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Tick Data Management
Ingest, store, and query high-frequency market tick data (trades, quotes, order books) using kdb+ clusters.
Risk Analytics
Run intraday risk calculations and post-trade analytics on financial time-series data at low latency.
Quantitative Research
Provide quants and data scientists with managed kdb environments for backtesting and strategy development.
Regulatory Reporting
Aggregate and transform trade and order data for regulatory submissions and compliance.
Alternative Data Processing
Ingest and correlate alternative datasets (news, satellite, ESG) with market data for signal generation.
Resources
Every other property we hold for Amazon FinSpace — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 3
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 3
Pagination, idempotency, versioning, errors, and events
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 4
Authentication, authorization, and security posture
Learn 1
Tutorials, courses, talks, and written guidance
Operate 3
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
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