Vedika Pro Ultra vs Amazon Titan Embed v2
Compare Vedika Pro Ultra and Amazon Titan Embed v2: pricing, performance, context window, latency, and best use cases. Side-by-side comparison on XALEN.
Updated 2026-05-21 · By Abhishek Raj · Our methodology
| Feature | Vedika Pro Ultra | Amazon Titan Embed v2 |
|---|---|---|
| Category | Domain Specialist | Embedding |
| Parameters | 120B | ~200M |
| Context Window | 256K | 8K |
| Input Price | $0.12/1M tokens | $0.001/1M tokens |
| Output Price | $0.20/1M tokens | N/A/1M tokens |
| Latency | ~600ms | ~15ms |
Choose Vedika Pro Ultra when:
- ✓ Kundali matching reports
- ✓ Multi-chart analysis
- ✓ Enterprise platforms
256K context, Deep yoga reasoning, Multi-system comparison
Choose Amazon Titan Embed v2 when:
- ✓ AWS RAG pipelines
- ✓ Enterprise search
- ✓ Document indexing
AWS native, Low cost, Reliable
Verdict: Vedika Pro Ultra vs Amazon Titan Embed v2
For cost efficiency, Amazon Titan Embed v2 wins at $0.001/1M input tokens. For speed, Amazon Titan Embed v2 is faster at ~15ms. Vedika Pro Ultra excels at Kundali matching reports while Amazon Titan Embed v2 is better for AWS RAG pipelines. Both are available on XALEN through a single API — try them in the Playground to see which fits your workload.
Detailed Analysis
Pricing Comparison
Vedika Pro Ultra costs $0.12/1M input tokens and $0.20/1M output tokens. Amazon Titan Embed v2 costs $0.001 input and N/A output. Amazon Titan Embed v2 is 120.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.
Performance & Context
Vedika Pro Ultra has a 256K context window with ~600ms latency. Amazon Titan Embed v2 offers 8K context at ~15ms. Vedika Pro Ultra has the larger context window.
Best For
Vedika Pro Ultra (Domain Specialist) is optimized for: Kundali matching reports, Multi-chart analysis, Enterprise platforms. Amazon Titan Embed v2 (Embedding) works best for: AWS RAG pipelines, Enterprise search, Document indexing.
Try Both on XALEN
Both models are available through XALEN's OpenAI-compatible API. Switch between them by changing the model parameter:
from xalen import XALEN
client = XALEN(api_key="xln_test_YOUR_KEY")
# Use Vedika Pro Ultra
response_a = client.chat.completions.create(
model="vedika-pro-ultra",
messages=[{"role": "user", "content": "Your question here"}]
)
# Use Amazon Titan Embed v2
response_b = client.chat.completions.create(
model="amazon-titan-embed-v2",
messages=[{"role": "user", "content": "Your question here"}]
)Frequently Asked Questions
Which is better, Vedika Pro Ultra or Amazon Titan Embed v2?
Vedika Pro Ultra (Domain Specialist, 120B) offers 256K context. Amazon Titan Embed v2 (Embedding, ~200M) offers AWS native. Choose Vedika Pro Ultra for Kundali matching reports or Amazon Titan Embed v2 for AWS RAG pipelines.
How much does Vedika Pro Ultra cost vs Amazon Titan Embed v2?
Vedika Pro Ultra: $0.12/1M input, $0.20/1M output. Amazon Titan Embed v2: $0.001/1M input, N/A/1M output. Both available on XALEN with batch processing at 50% discount.
Can I use both models on XALEN?
Yes. XALEN provides 200+ models through a single OpenAI-compatible API. Switch between Vedika Pro Ultra and Amazon Titan Embed v2 by changing the model parameter. No code changes needed.
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Last updated: 2026-05-21. Pricing and specifications may change. Check pricing page for latest rates.