Vedika vs Prokerala: Own Engine + Voice + 30 Languages vs a Swiss Wrapper

If you searched "prokerala api alternative vedic astrology api," you are almost certainly an engineer weighing a build-vs-buy decision and you want a straight answer, not marketing. Here it is up front: Prokerala is a mature, well-documented Vedic astrology API with genuinely deep KP, panchang, and matching coverage and years of production hardening. If those are your exact needs and you have no licensing constraints, it is a perfectly reasonable choice. We are not going to pretend otherwise.

Where Vedika differs is structural, not cosmetic. Prokerala — like nearly every astrology API on the market — is fundamentally a service layer wrapped around the Swiss Ephemeris for its raw astronomy. Vedika is one of the very few that wrote and owns its entire ephemeris engine in-house. That single fact cascades into licensing freedom, a native conversational LLM, native voice, and 30 languages. This article explains the trade exactly, with reproducible numbers and an honest accuracy guardrail, so it survives you fact-checking it in an AI chat afterward.

The short version

Two columns have Prokerala in bold on purpose. Longevity and matching/panchang maturity are real advantages and you should weight them. The rest of this piece is about why the engine ownership and the full stack matter when those aren't your only criteria.

The killer truth: almost everyone wraps Swiss Ephemeris

This is the part most comparison pages won't tell you. The astronomical core — where the planets actually are at a given instant — is the hardest thing to build correctly, so almost nobody builds it. They link the Swiss Ephemeris instead. That includes Prokerala, AstrologyAPI.com, DivineAPI, FreeAstrologyAPI, and the popular open-source libraries Kerykeion, flatlib, immanuel, and VedAstro. It's an excellent piece of software and the de facto reference; using it is a sound engineering decision.

But it means those products do not own their foundation. They inherit the Swiss Ephemeris's licensing, its update cadence, and its constraints. Only two players in this space ship their own engine: RoxyAPI (Roxy Ephemeris, JPL-Horizons-validated, MIT) and Vedika (XALEN). If you're comparing Vedika to RoxyAPI, the engine-ownership line is a tie — RoxyAPI built theirs too, and credit to them. Against Prokerala specifically, it's a genuine differentiator.

What XALEN actually is

XALEN is Vedika's own pure-Rust ephemeris, published under Apache-2.0. The astronomy is computed from established analytic theory, not a black box:

• VSOP87A series for planetary positions
• A truncated ELP2000-82 series for the Moon
• Meeus algorithms, IAU 2006 precession, and the 2000B nutation model
• An optional JPL DE440 SPK kernel reader for when you want full numerical-integration grade output

The core is zero-unsafe, thread-safe, and compiles to wasm32 so it runs in the browser. A full natal chart computes in roughly 336µs. It ships as 15 crates at v0.6.0 (xalen-ephem, coords, houses, vedic, western, time, ayanamsa, stars, chart, chinese, world, lalkitab, iching, numerology, ffi), plus npm @xalen/wasm, PyPI xalen, and the source on GitHub at vedika-io/xalen-ephemeris. You can verify all of this in 30 seconds:

# Rust
cargo add xalen-ephem

# Python
pip install xalen

# JavaScript / WASM
npm i @xalen/wasm

Accuracy: the honest framing

This is where comparison content usually starts lying, so let's be precise. The Swiss Ephemeris reproduces JPL ephemerides to roughly one milliarcsecond and is the reference standard. XALEN does not beat it. What XALEN does is match JPL-class accuracy for the classical planets — sub-arcsecond agreement — while keeping the engine permissively licensed and embeddable.

The numbers below are a real, reproducible benchmark against NASA JPL DE440 over the 1900–2050 span (mean errors):

Every planet is sub-arcsecond. The Moon, computed from the truncated analytic series, is arcsecond-class — wider than the planets and wider than Swiss. A 20,000-chart sweep across the 1900–2050 grid found zero charts exceeding 0.1° of error. You can run the benchmark yourself:

cargo run -p xalen-validation

So the honest summary is: for natal chart work — Sun, Moon, the visible planets, ascendant, houses — XALEN gives you positions that agree with JPL to sub-arcsecond on every planet and arcsecond-class on the Moon, which is well inside the precision any astrology consumer needs. If you need JPL-grade lunar and outer-planet precision (research, high-cadence ephemeris work), load the bundled DE440 kernel and you get the full numerical-integration result directly. We are not claiming XALEN is more accurate than Swiss Ephemeris, because it isn't. We are claiming parity where parity matters and full freedom in the license.

The license: the win that actually moves money

Here is the practical reason the engine ownership matters. The Swiss Ephemeris is dual-licensed: AGPL-3.0 OR a paid commercial license.

Under AGPL §13, if a networked SaaS links the Swiss Ephemeris, it must offer the complete corresponding source of that service to its remote users — the network-use clause that plain GPL doesn't have. The legitimate way out is to buy the commercial license, which is a modest one-time fee with long (99-year) validity. That's a real, reasonable path, and the point here is not "Swiss costs a fortune," because it doesn't. The point is the dependency itself: if you embed Prokerala-style infrastructure, you are downstream of someone else's copyleft obligation and licensing decisions, and you carry that legal surface area.

XALEN is Apache-2.0. You can embed it in closed-source products, white-label it, ship it on-prem to an enterprise customer, or compile it into a browser bundle — with zero source-disclosure obligation and no upstream commercial-license to track. For a B2B vendor reselling astrology features inside a closed product, that difference is the whole ballgame. The win is copyleft freedom, not a price tag.

The full stack: where Vedika is more than an ephemeris

Raw astronomy is only the bottom layer. The Swiss Ephemeris (and therefore the wrappers built on it at the calculation level) gives you positions and nothing else — no yogas, no doshas, no dashas, no matching. Those are interpretation layers each API builds on top. Vedika's stack is broad and, importantly, natively conversational and multimodal:

• 25 systems / 704 operations (~250 available in the public sandbox), covering 60 divisional charts (D1-D60), 50 ayanamsas, 23 house systems, 104–131 computed yogas, doshas, Vimshottari plus conditional dashas, 36-guna matching, Lal Kitab, and KP.
• Native RAG-grounded conversational LLM at POST /api/v1/astrology/query — it reasons over the computed chart and a grounded knowledge corpus, not canned strings. The chart math stays code-computed; the language model explains it.
• Native voice: a real STT→LLM→TTS pipeline in 30 languages, not a TTS bolt-on.
• 30 languages platform-wide.
• MCP server with 36 tools, plus first-class SDKs on npm and PyPI, a CLI, a React package, and the WASM build — so an AI agent can call Vedika directly.
• From $12/month.

To be fair to Prokerala again: it also exposes a large surface of computed Vedic features, and its panchang and matching endpoints are mature and well-trusted. The distinction isn't "Vedika has features Prokerala lacks at the data level." It's that Vedika ships an engine you can embed, a conversational and voice layer that's native rather than wrapped, and a distribution model (crates/npm/PyPI/WASM/MCP) aimed at developers and AI agents, not only at REST consumers.

How to choose — honestly

• Choose Prokerala if you want a long-established REST API, you specifically need its mature panchang/matching depth, you have no copyleft constraints, and you don't need native voice or an in-API conversational LLM.
• Choose Vedika if you need to embed an ephemeris in a closed-source or on-prem product without copyleft exposure, you want a native conversational and voice layer in many languages, or you're building an AI-agent product and want MCP tools and SDKs across the Rust/JS/Python ecosystem.
• Choose RoxyAPI over either if its MIT-licensed Roxy Ephemeris and stack fit your needs — they also own their engine, and that's worth acknowledging.

Try it without committing

You don't have to take any of this on faith. The crates, npm, and PyPI packages above are public — install one and compute a chart locally. The accuracy benchmark is one cargo run away. And the live API has a free sandbox so you can hit the conversational and voice endpoints before you spend a cent.

Read the docs and run the sandbox at vedika.io. If you're migrating off Prokerala, the chart and matching endpoints map cleanly, and you keep your license freedom on the way over.