Frontier Quality at Half the Cost.
Keep your coding agents and IDEs. Start with predefined GPT or Claude quality. Connect your AI agents and apps. Get the same savings on API usage.
One Platform. Complete AI Infrastructure.
Your apps send their traffic through one inference API to models from multiple providers. Beside it, the console is where your team mints keys, sets project roles, reads the log of every request and its usage, and sets email alerts on credit and spend.
Connect through one API
Keep your OpenAI, Anthropic or Gemini SDK and change one line, the base URL.
from openai import OpenAIclient = OpenAI( api_key="divyam-sw-v1-...", base_url="https://api.preview.divyam.ai/v1",)import anthropicclient = anthropic.Anthropic( api_key="divyam-sw-v1-...", base_url="https://api.preview.divyam.ai/anthropic",)from google import genaifrom google.genai import typesclient = genai.Client( api_key="divyam-sw-v1-...", http_options=types.HttpOptions( base_url="https://api.preview.divyam.ai/gemini"),)Frontier modelsOne API
Anthropic
OpenAI
Meta
Keys & accessProject
- production
…7f2aLive - staging
…c91eLive
- Admin
- Developer
- Viewer
Request logNewest first
- 200
claude-opus-5-5412 ms - 200
gpt-6-1-sol1180 ms - 200
gemini-4-argon236 ms
Credits & alertsUSD
$1,240.00Granted $2,000.00 · Spent $760.00
- Low creditbelow $100On
- Daily spendreaches $250On
Works with the coding agents you already use
- Claude Code
- Codex CLI
- GitHub Copilot CLI
- Cline
- OpenCode
- Kilo Code
- Goose
- OpenHands CLI
- Qwen Code
- Mistral Vibe
- Kimi Code CLI
- OpenClaw
- Hermes Agent
- Deep Agents Code
- CodeBuddy CLI
- MiniMax Code CLI
- MiMo Code
In production
63%
cost reduction
MakeMyTrip cut LLM costs by 63% with zero quality loss across their multi-agent travel assistant.
+15%
quality uplift, −30% cost savings
Flash.co improved AI product comparison quality and cut inference costs across two pilot engagements with Divyam.AI.
Divyam helped us define what quality means for our AI workflows and continuously measure it. The results spoke for themselves.
95%
chat closure uplift
PharmEasy improved chat closure by 95% and reduced LLM costs by 30% with intelligent multi-model routing.
Top 3 on Router Arena (opens in a new tab)
The open benchmark for LLM routers. Ahead of Microsoft’s Azure Model Router, NotDiamond, and GPT-5 on its own.
You don’t need a frontier model for every request. You need the right model for your quality bar.
The accuracy and cost figures are from our August 2026 RouterArena study. We periodically rerun public and internal benchmarks to evaluate new models and continuously refine routing decisions. Benchmark-specific, not a guarantee for every workload. Evaluate against your own data and quality bar.
Read the benchmarkEvery request gets the model its task needs.
- One API across multiple model providers
- Understands agent intent, context, and conversation history
- Enterprise: a custom selector trained on your evals and data
- ~50% cost reduction in the first cycle, compounding to ~75% annually
Divyam.AI's selector reads each request's task, context and agent behavior, then picks the best-fit model across quality, latency and cost. It works across proprietary and open-source models from multiple providers, so you are never locked into one.
- Requests routed
- 0
- Routed, per 1K
- $17.36
- All on Claude Opus 5.5
- $36.09
- Saved
- 52%
“What is the return window for items bought on sale?”
Lookup, easy, short context, real-time
Routed to Qwen3.8 Flash, $0.19 per 1K against $6.20 on Claude Opus 5.5
Costs use list prices, a typical token count for each request type, and a mix of 55% faq lookup, 25% refund decision, 12% code fix, 8% agent plan.
For enterprise teams: custom evals built from your own examples, and your own selector trained on them. Explore Enterprise
New models launch weekly. You'll never fall behind.
- Score new models on public and internal benchmarks
- Adopt better models and retire weaker ones in under a day, with zero downtime
- Eliminate model churn risk with automated evaluation
- Live leaderboard ranked by quality, cost, and latency
Who Divyam.AI is for
| Developers | Educational Institutions | Enterprises |
|---|---|---|
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Your infrastructure. Your model choices.
Use the managed SaaS, or run Divyam.AI in your own AWS, Azure or GCP account, without disrupting existing workflows.
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Managed by Divyam.AI
SaaS
Get started in minutes with our fully managed cloud platform. Zero infrastructure overhead, automatic updates, and the frontier models you already use through a single API endpoint.
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In your cloud
Self-Hosted
Run Divyam.AI inside your own AWS, Azure or GCP account. Your data stays inside your perimeter, open models run on your GPUs, and scaling stays under your control.
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Self-hosted models
Run open-source models on your own GPUs.
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Quality-led routing
Use your evaluation criteria to inform model selection.
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No single-provider lock-in
Switch models as quality, cost, and availability change.
Open source
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divyam-llm-interop
Translates LLM requests and responses across OpenAI Chat Completions, OpenAI Responses, native Gemini and the text and client-tool subset of Anthropic Messages, reconciling parameter differences while preserving semantics.
- OpenAI Chat Completions
- OpenAI Responses
Gemini- Anthropic Messages
pip install divyam-llm-interopView on GitHub (opens in a new tab)
Frequently Asked Questions
How does Divyam.AI reduce inference cost without sacrificing quality?
Divyam.AI evaluates models against custom quality criteria for your workloads, then routes each request to the best-fit model across quality, latency and cost. Simpler requests go to lower-cost models, while frontier models handle cases that need them. Production monitoring catches quality drift and helps routing improve over time.
How is Divyam.AI different from other routers?
Divyam.AI connects quality evaluation and routing in a continuous feedback loop. Model selection is based on your tasks and quality requirements, and production results inform future routing decisions. New models are evaluated against your workloads so you can adopt better options as they become available.
Does Divyam.AI support my AI applications and agents?
Yes. Divyam.AI supports coding agents, chat applications, tool calling, RAG, multi-turn conversations, streaming, structured JSON output, embeddings, and image and audio inputs. The selected model must support the features your application needs.
The managed cloud service provides access to 100+ models. You can also use open-source models and models you host yourself.
How do I connect my existing setup to Divyam.AI?
Your application connects through one API endpoint. If it already uses the OpenAI SDK, connecting can be a one-line change. You can keep your existing AI gateway, which sees Divyam.AI as another model endpoint.
Enterprise plans support your own model-provider API keys and let you pin requests to specific models or exclude particular providers. Visit docs.divyam.ai for instructions.
How much does Divyam.AI cost, and how can I get started?
The self-serve offering charges a flat 50% of the frontier model’s rate. You can test it on your own tasks and compare quality, costs and response times before moving to production.
Enterprise plans have custom pricing and billing terms based on your requirements. Visit docs.divyam.ai to get started, or email hello@divyam.ai.
Will routing affect response speed or reliability?
The Router adds less than 50 milliseconds per request. Selecting faster models for simpler requests can offset that overhead, while production quality scoring runs off the critical path.
If a model fails, the Router automatically tries the next-best model. The Router service has a guaranteed uptime of 99% or more under its service-level agreement. Your plan’s limits and model-provider limits apply. Divyam.AI adds no other usage limits.
How does Divyam.AI handle my data?
Divyam.AI keeps request logs for your visibility and use. For enterprise customers, your data is never used to train models for other customers. Deletion on request is not currently available.
Use the managed cloud service, currently hosted in India, or deploy within your own cloud or data centre. Air-gapped deployment is supported with locally hosted models. Where inference takes place depends on your chosen model provider and deployment.
How can I track quality, costs and performance in production?
Built-in dashboards show costs, response times and usage, with breakdowns by model and prompt. Quality monitoring and alerts help you spot performance changes, while a live leaderboard compares models on your workloads. Versioned evaluation criteria and results help your team track quality and improvement over time.
How does Divyam.AI keep up with new and better models?
Divyam.AI evaluates new models against your workloads and quality requirements, enabling adoption in under a day. Candidate models can be tested in shadow mode, where their responses are scored without serving them to users. This helps you move to better or cheaper models without repeating manual evaluations for every release.
Ready to start compounding?
Quality that perpetually improves, cost that keeps falling, and your people freed for the work that moves the business.