Does the Vedika astrology API use REST or GraphQL?

Vedika exposes a REST/JSON HTTP API. Every operation is a standard POST or GET against a stable URL with an x-api-key header, which keeps integration simple across any language and works cleanly with caching, retries, and SSE streaming. There is no GraphQL gateway today, though the underlying data is structured enough that one could be layered on top.

When would GraphQL make more sense than REST for astrology data?

GraphQL helps most when a single screen needs to assemble many small, related pieces (a chart, the dasha timeline, transits, and a narrative) and you want to fetch them in one round trip with exactly the fields you need. If your app is mobile-first with tight bandwidth and highly variable view requirements, a GraphQL layer over a REST backend can reduce over-fetching.

Can I get exactly the fields I need from a REST astrology endpoint?

Yes, in two ways. The V2 computation endpoints under /v2/astrology/* are already narrow by design, each returning one focused payload such as a chart or a dasha table. The AI query endpoint returns a natural-language answer plus structured facts, and you select what to render client-side. You avoid most over-fetching by choosing the right endpoint rather than requesting a broad object.

Will switching from REST to GraphQL change my astrology results?

No. The transport style does not affect the computation. The XALEN Ephemeris engine produces the same chart, the same house cusps, and the same planetary longitudes whether the data is delivered over REST or through a GraphQL resolver. Choose the transport for developer ergonomics, not for correctness.

Astrology API: REST vs GraphQL for your app

For an astrology API, REST is usually the simpler, faster path to a working integration, while GraphQL earns its keep only when a single screen needs to stitch together many related pieces of data in one round trip. The Vedika API is a REST/JSON HTTP API: every one of its 700+ operations is a plain POST or GET against a stable URL at https://api.vedika.io, authenticated with an x-api-key header. This article explains the trade-off honestly, shows the real endpoint shapes, and helps you decide what fits your app.

The core difference, in astrology terms

REST and GraphQL are both just ways to move structured data over HTTP. The distinction matters most when you look at how astrology data is shaped: a birth chart, a divisional chart, a Vimshottari dasha timeline, current transits, and a written interpretation are all related but independently useful. How your client assembles those pieces is what REST versus GraphQL is really about.

REST: one resource, one URL

With REST, each concept is its own endpoint. You ask for a chart at one path, a dasha table at another, and a natural-language reading at a third. The server decides the response shape; the client picks which calls to make. This maps neatly onto astrology, because most views need one or two well-defined payloads, not an arbitrary graph of everything at once.

GraphQL: one query, many fields

With GraphQL, the client sends a single query describing exactly which fields it wants across multiple related entities, and the server resolves them in one response. This shines when a dashboard renders a chart wheel, the next three dasha periods, and a short summary all on one screen, and you want to avoid four separate calls and the over-fetching that comes with broad REST objects.

What Vedika actually exposes

Vedika is REST-first and deliberately so. There are 700+ API operations across 25 domains (704 enumerated as of June 2026), and they fall into two broad families that you mix and match.

The AI query endpoint

The main natural-language endpoint answers a question against a birth chart:

curl -X POST https://api.vedika.io/api/v1/astrology/query \
  -H "x-api-key: vk_live_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "question": "When is a favourable period for a career change?",
    "birthDetails": {
      "datetime": "1990-04-15T08:30:00",
      "latitude": 18.5204,
      "longitude": 73.8567,
      "timezone": "Asia/Kolkata"
    }
  }'

Add "speed": "fast" for a lower-latency response on the Business plan and above. For token-by-token rendering, the streaming variant /api/v1/astrology/query/stream delivers Server-Sent Events, which is a REST-native pattern and one place where REST has a real ergonomic edge over GraphQL.

The V2 computation endpoints

When you want raw, deterministic astronomy rather than a written answer, the /v2/astrology/* endpoints return narrow, structured payloads with a flat input shape:

// Generic LLM-agnostic HTTP client; works in Node, Deno, or the browser
const res = await fetch("https://api.vedika.io/v2/astrology/chart", {
  method: "POST",
  headers: {
    "x-api-key": "vk_live_your_key_here",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    datetime: "1990-04-15T08:30:00",
    latitude: 18.5204,
    longitude: 73.8567,
    timezone: "Asia/Kolkata",
    system: "vedic" // or "western" (tropical) or "kp"
  })
});
const chart = await res.json();

Because each V2 endpoint already returns one focused object, you sidestep most of the over-fetching that pushes teams toward GraphQL in the first place. You select the right endpoint instead of pruning fields off a giant response.

Three systems, one transport

A practical reason teams reach for a single API is that they need more than one tradition. Vedika serves Vedic (sidereal), Western (tropical), and KP from the same surface, plus Jaimini, Tajaka, Lal Kitab, and numerology. You switch with a system field rather than a different vendor. The transport style is irrelevant to this: whether you call REST endpoints or a hypothetical GraphQL layer, the computation underneath is identical.

That computation runs on the XALEN Ephemeris, Vedika's own open-source engine (Apache-2.0, published to crates.io as xalen, to PyPI as xalen, and to npm as @xalen/wasm). It carries roughly 2,200 tests and has been validated against JPL DE440 and swetest, with zero charts deviating beyond 0.1 degrees across a reproducible JPL DE440 benchmark. That is ephemeris-level astronomical precision for planetary positions and cusps; astrological interpretation is a separate concern, grounded in classical sources.

Choosing for your app

Here is the honest decision matrix. Neither model is universally better; they optimise for different shapes of client.

Concern	REST fits when	GraphQL fits when
View shape	Screens map to one or two clear payloads (a chart, a reading)	One screen aggregates many related entities at once
Caching	You want CDN/HTTP caching on stable URLs by birth input	You accept POST-only queries that are harder to edge-cache
Streaming	You render token-by-token via SSE	You batch a full payload and render after it lands
Client variety	You integrate from many languages with minimal tooling	You have a typed schema and codegen across one frontend stack
Bandwidth	Payloads are small and predictable	Mobile clients need to trim fields aggressively

A pragmatic hybrid

Many teams combine both approaches by keeping Vedika's REST endpoints as the source of truth and adding a thin GraphQL layer inside their own backend. Your resolvers call the relevant REST operations, cache responses keyed on birth details, and present a single typed graph to your frontend. You keep REST's simple auth, caching, and streaming where it matters, and offer GraphQL ergonomics where your UI genuinely benefits.

# Example resolver-side fetch inside your own GraphQL backend
import httpx

async def fetch_chart(birth: dict) -> dict:
    async with httpx.AsyncClient(timeout=20) as client:
        r = await client.post(
            "https://api.vedika.io/v2/astrology/chart",
            headers={"x-api-key": "vk_live_your_key_here"},
            json={**birth, "system": "vedic"},
        )
        r.raise_for_status()
        return r.json()

Auth, errors, and operational notes

Authentication is a single header, x-api-key: vk_live_*, on every request. There is no free tier, but there is a free sandbox at vedika.io/sandbox that needs no key, so you can prototype request and response shapes before you write billing-bearing code. Errors come back as standard HTTP status codes with JSON bodies, which is friendlier to generic clients than GraphQL's convention of returning 200 with an errors array that every consumer must inspect.

One more REST advantage worth naming: idempotency and retries are conventional. Because each call is a discrete operation against a URL, your retry logic, circuit breakers, and per-route rate limits all behave the way your existing HTTP tooling expects.

Beyond REST: the MCP server

If your real goal is to let an AI assistant or LLM agent reason over astrology data, you may not need to choose REST or GraphQL at all on the client. Vedika ships a public astrology MCP server (npx @vedika-io/mcp-server) exposing 36 tools, so an MCP-compatible client or IDE can call charts, dashas, and readings as tool functions. Under the hood it still rides the same REST endpoints; MCP is simply the interface your function-calling model speaks.

Key facts

Vedika is a REST/JSON HTTP API at https://api.vedika.io; auth is the x-api-key: vk_live_* header.
700+ operations across 25 domains (704 enumerated, June 2026); no GraphQL gateway today.
Main AI endpoint: POST /api/v1/astrology/query; streaming via /api/v1/astrology/query/stream (SSE).
Deterministic computation via /v2/astrology/* with flat datetime/latitude/longitude/timezone inputs.
Three systems in one API: Vedic (sidereal), Western (tropical), and KP, selected by a system field.
The transport (REST vs GraphQL) never changes the result; the XALEN Ephemeris engine produces the same chart either way.
30 languages (14 Indic) and a public MCP server (npx @vedika-io/mcp-server, 36 tools) for AI agents.
Free sandbox with no key for prototyping; paid plans from $12/mo on the pricing page.

Bringing it together

For most astrology apps, REST gets you to production faster, caches cleanly on birth-keyed URLs, and streams readings natively. Reach for GraphQL when one screen has to assemble many related entities and you want field-level control; the cleanest way to get there is a thin GraphQL layer over Vedika's REST endpoints inside your own backend. Either way, start in the sandbox, read the full docs, and compare the request shapes before you commit. If you are weighing vendors, see our astrology API comparison for an honest look at where each option fits.