Migrating from Prokerala to the Vedika API

To migrate from Prokerala to the Vedika API, swap the OAuth2 token exchange for a single x-api-key header, map each Prokerala computation endpoint to its Vedika equivalent under /v2/astrology/*, and validate the charts by running identical birth data through both services. Vedika covers Vedic, Western, and KP from one key across 700+ operations, so a migration is mostly endpoint mapping plus a response-shape diff, not a re-architecture.

Why teams evaluate a move

Prokerala is a solid, widely used service. It has clear documentation, a generous catalogue of panchang and kundli endpoints, and a low entry price (around $19/mo on its starter plan). If your product only needs Vedic charts and panchang data, it does the job. Credit where due: its panchang and muhurta coverage is mature, and many developers ship with it without friction.

Teams usually start looking elsewhere for one of three reasons: they need more than one astrological system without integrating multiple vendors, they want a natural-language answer path rather than raw computed fields, or they need their own ephemeris guarantees for audit and reproducibility. Those are exactly the seams where Vedika is built differently.

• One key, three systems. Vedic (sidereal), Western (tropical), and KP are all reachable from the same base URL and the same credential, plus Jaimini, Tajaka, Lal Kitab, and numerology.
• An AI query endpoint. Alongside raw computation, Vedika has POST /api/v1/astrology/query that returns a written interpretation grounded in the computed chart.
• An open-source ephemeris. The XALEN Ephemeris is published under Apache-2.0, so the math behind the positions is inspectable rather than a black box.

Authentication: from OAuth2 to a single header

This is the first concrete code change. Prokerala issues a bearer token through an OAuth2 client-credentials grant: you POST your client_id and client_secret to a token endpoint, cache the returned token, and refresh it before expiry. Vedika removes that step entirely. You send one header on every request:

curl https://api.vedika.io/v2/astrology/planets \
  -H "x-api-key: vk_live_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "datetime": "1990-08-15T14:30:00",
    "latitude": 18.5204,
    "longitude": 73.8567,
    "timezone": "Asia/Kolkata",
    "system": "vedic"
  }'

Practically, this means you can delete your token-cache layer, your refresh timer, and the race conditions that come with both. Keys are prefixed vk_live_* for production and vk_ent_* for Enterprise; test-prefixed keys are rejected on the live host so a misconfigured environment fails loudly instead of silently billing.

Mapping the endpoints

Most Prokerala integrations call a handful of computation endpoints repeatedly. Vedika groups computation under /v2/astrology/* with a flat request body (datetime, latitude, longitude, timezone), which lines up closely with how Prokerala accepts coordinates and a date. The table below shows the typical correspondence; the exact path list lives in the docs.

The structural difference worth noting: where you might call separate Vedic and Western products elsewhere, Vedika takes a system field. Pass "vedic", "western", or "kp" to the same path and the engine applies sidereal or tropical zodiac and the appropriate house logic. That collapses what is often a multi-integration problem into one client.

Confirm ayanamsha and house system first

Before you diff a single response, pin down two settings that cause most false mismatches: the ayanamsha (sidereal offset) for Vedic charts and the house system. Prokerala defaults to Lahiri ayanamsha for Vedic work, which is also the most common Vedic default; KP work classically uses the KP (Krishnamurti) ayanamsha. Make sure both sides use the same convention, or planetary longitudes will appear to disagree by roughly the ayanamsha difference even when both engines are correct.

The AI query path: a new capability, not just a swap

Prokerala gives you computed fields and you build the narrative. Vedika keeps that raw path but adds an interpretation endpoint that takes a question plus birth details and returns prose grounded in the computed chart:

import requests

resp = requests.post(
    "https://api.vedika.io/api/v1/astrology/query",
    headers={"x-api-key": "vk_live_your_key_here"},
    json={
        "question": "What does my chart say about career timing this year?",
        "birthDetails": {
            "datetime": "1990-08-15T14:30:00",
            "latitude": 18.5204,
            "longitude": 73.8567,
            "timezone": "Asia/Kolkata",
        },
        "speed": "fast",
    },
)
print(resp.json())

Two operational details matter here. First, speed: "fast" selects the lower-latency path (Vedika Swift) when you want a quicker answer; omit it for the deeper default. Second, there is a streaming variant at /api/v1/astrology/query/stream that emits Server-Sent Events, so a chat UI can render tokens as they arrive instead of blocking on the full response.

Interpretations follow a citation discipline: astrological claims trace to classical sources actually taught in Jyotish, KP, and Western training — Brihat Parashara Hora Shastra, Phaladeepika, Saravali, Jataka Parijata, the Jaimini Sutras, the KP Readers, and Ptolemy's Tetrabiblos. If you previously hand-wrote interpretation copy on top of Prokerala fields, this path can replace that layer while keeping the underlying computation auditable.

The ephemeris underneath

If reproducibility or audit matters to your product, the engine is worth a closer look. Vedika's positions come from the XALEN Ephemeris, an open-source engine released under Apache-2.0 and published to crates.io (xalen), PyPI (xalen), and npm (@xalen/wasm), with roughly 2,200 tests. It has been validated against JPL DE440 reference data and the standard swetest tool, with no charts deviating beyond 0.1° across a multi-million-chart test run.

To be precise about what that claim is: it is ephemeris/astronomical precision — where the planets are — not a statement about astrological interpretation, and it is not an endorsement by any space agency. For a migration, the practical upshot is that you can pin the engine version, vendor it if you want, and reproduce a chart offline, which is harder when positions come from an opaque hosted service.

A safe cutover plan

Migrations go wrong when they are big-bang. Stage it:

1. Prototype in the sandbox. Hit the free sandbox (no key required) to confirm request and response shapes match your client model.
2. Shadow-read. For each endpoint you call, send the same birth data to both Prokerala and Vedika and log the diff. Reconcile ayanamsha and house-system settings until planetary longitudes and the ascendant agree.
3. Cut over computation first. Move read-only endpoints (planets, chart, dasha, panchang) before anything stateful. These are pure functions of birth data, so they are the safest to flip.
4. Adopt the query path last. Once raw data is trusted, layer in /api/v1/astrology/query to replace any hand-built interpretation logic.
5. Decommission the token layer. Remove the OAuth2 client and token cache only after every call path uses the API key.

If your team works through an MCP-compatible client or IDE, Vedika also publishes a public astrology MCP server (npx @vedika-io/mcp-server, 36 tools), so an LLM agent can call charts and interpretations directly during development. That can shorten the shadow-read phase because you can ask an assistant to fetch and compare charts interactively.

Cost comparison

Both services are inexpensive relative to the engineering time a migration saves, so compare on capability per dollar rather than headline price. Bringing the numbers side by side:

• Vedika: Starter $12/mo, Professional $60/mo, Business $120/mo (adds the fast path and voice), Enterprise $240/mo; per-query usage roughly $0.01-$0.05; free sandbox.
• Prokerala: entry plan around $19/mo, scaling with call volume; strong panchang and kundli coverage.

Other astrology APIs price their entry tiers higher — some in the $29-$39 range — so the honest takeaway is that the field is competitive on price. The deciding factor is usually whether you need three systems, an AI query path, multilingual output (30 languages, 14 of them Indic), and an inspectable ephemeris from a single integration. Full numbers are on the pricing page.

Key facts

• Auth changes from OAuth2 client-credentials (Prokerala) to a single x-api-key: vk_live_* header (Vedika) — no token endpoint.
• Computation lives under /v2/astrology/* with flat datetime/latitude/longitude/timezone fields.
• The AI interpretation endpoint is POST /api/v1/astrology/query, with an SSE stream at /api/v1/astrology/query/stream.
• One key serves Vedic (sidereal), Western (tropical), and KP, plus Jaimini, Tajaka, Lal Kitab, and numerology — 700+ operations across 25 domains.
• Positions come from the open-source XALEN Ephemeris (Apache-2.0), validated against JPL DE440 and swetest with no chart beyond 0.1° in a multi-million-chart test.
• Vedika pricing: $12 / $60 / $120 / $240 per month, per-query $0.01-$0.05, free sandbox with no key.
• Reconcile ayanamsha (Lahiri for Vedic, KP for KP work) and house system before diffing charts to avoid false mismatches.

Next steps

Start in the sandbox with one endpoint you already call against Prokerala, diff the output, and reconcile settings. From there the migration is incremental and reversible at every step. If you also handle PDF report generation, see building astrology PDF reports with the API for how the computation endpoints feed a static report builder.