If you are comparing Novita AI and Together AI, do not stop at the first chat completion call. Both can fit an OpenAI-style LLM workflow, but the decision gets clearer when you look at what your app needs after the prototype: batch jobs, dedicated endpoints, model choice, cost controls, and production operations. Novita AI is worth considering when you want model APIs, batch inference, dedicated endpoints, agent tooling, and GPU resources in one workflow. Together AI is worth evaluating when its model catalog, fine-tuning path, training stack, or infrastructure setup is a closer match for your production plan.
Quick Comparison
| Category | Novita AI | Together AI | What it means |
| Primary fit | AI and agent cloud for model APIs, batch inference, dedicated endpoints, agent sandbox, and GPU resources | Open-source AI platform for running, fine-tuning, training, and serving models | Novita is a strong fit when you want one workflow for model APIs and production deployment options; Together is a strong fit when your team is already building around Together’s open-model infrastructure. |
| LLM API compatibility | OpenAI-compatible LLM API through https://api.novita.ai/openai | OpenAI-compatible API support | Existing OpenAI SDK users can usually start with a base URL, API key, and model-name change. |
| Model discovery | Model library and /openai/v1/models endpoint list available models and metadata | Model catalog uses provider/model IDs and supports OpenAI SDK routing | Treat model names as provider-specific IDs, not interchangeable labels. |
| Pricing model | Public per-token pricing for serverless model APIs, batch API support for asynchronous LLM work, and GPU-hour pricing for dedicated endpoints | Public serverless token pricing, plus batch, dedicated inference, fine-tuning, and GPU paths | Compare live prices model by model and deployment mode by deployment mode before production use. |
| Production workflow | Real-time model APIs, LLM Batch API for asynchronous jobs, dedicated Deployments, agent sandbox, and GPU cloud | Serverless inference, batch jobs, dedicated inference, fine-tuning, and GPU clusters | Compare real-time, batch, and dedicated deployment paths on both sides instead of reducing the decision to first-call API convenience. |
| Sensitive claims | Do not infer independent latency, quality, uptime, or cheapest-provider claims from pricing tables alone | Same caveat | Run the same prompts on the same target models before choosing. |
How the LLM API Workflows Compare
Both Novita AI and Together AI reduce the first migration step for developers who already use OpenAI SDKs. In Novita’s LLM API guide, the migration path is to set the base URL to https://api.novita.ai/openai, set the API key, and update the model name. The Novita API reference also documents chat completions, completions, model listing, and model retrieval under the OpenAI-compatible endpoint family.
Together also supports OpenAI-style SDK migration for common inference workflows. Treat that as a compatibility check rather than a copy-paste instruction: confirm the supported endpoint family, model ID, streaming behavior, tool behavior, and any unsupported OpenAI platform surfaces before changing production traffic.
For most LLM application teams, the first test is straightforward: run the same small prompt set through both providers, record token usage, compare output quality, and note any differences in streaming, tool calls, structured outputs, context limits, and error handling.
Model Catalog and Availability
Novita’s model library is useful because it answers the first questions developers usually ask: which models are available, what do they cost, how much context do they support, and what model ID should go into the request. That is the right place to begin a model shortlist, but it should not be mistaken for the whole Novita product.
For real-time applications, Novita’s OpenAI-compatible LLM API lets developers swap the base URL, choose a model, and run the same kind of chat-completion workflow they already know. For offline or delayed work, Novita’s LLM Batch API supports asynchronous .jsonl jobs with OpenAI-compatible batch endpoints for chat completions and completions. For production workloads that need isolated compute, Novita Deployments provide dedicated GPU-backed endpoints with autoscaling, scale-to-zero, LoRA adapter support, and an OpenAI-compatible chat API for text workloads.
Together also offers a strong production path across serverless inference, batch jobs, dedicated inference, fine-tuning, training, and GPU clusters. A useful comparison should look at both providers as production options: Novita is a good fit when you want model APIs, batch inference, dedicated endpoints, agent tooling, and GPU options in the same developer cloud; Together is a good fit when its model catalog, fine-tuning/training stack, or infrastructure setup matches the way your team already plans to build.
Do not assume a shared model name means the same production behavior on both providers. The provider may differ in model variant, quantization, context window, caching behavior, tool support, rate limits, or routing. Before switching providers, use each provider’s live model list and model detail page to confirm the exact model ID and supported features.
Pricing Comparison and Caveats
Pricing changes quickly, so use the examples below as a current snapshot checked on June 5, 2026, not as a permanent price sheet.
| Example overlapping model area | Novita AI public pricing snapshot | Together AI public pricing snapshot | Caveat |
| OpenAI GPT OSS 120B | $0.05/Mt input and $0.25/Mt output | $0.15/1M input and $0.60/1M output | Compare exact model IDs and limits before treating the price rows as equivalent. |
| OpenAI GPT OSS 20B | $0.04/Mt input and $0.15/Mt output | $0.05/1M input and $0.20/1M output | Lower listed token price does not prove better output quality or latency. |
| Llama 3.3 70B Instruct | $0.135/Mt input and $0.40/Mt output | $1.04/1M input and $1.04/1M output | Context, model ID, and serving stack should be verified in live docs. |
| Qwen3 235B A22B Instruct 2507 | $0.09/Mt input and $0.58/Mt output | $0.20/1M input and $0.60/1M output for the listed FP8 Throughput row | Similar model-family names may still represent different deployment choices. |
| Deepseek V4 Pro | Novita pricing page shows $1.6/Mt input, $0.135/Mt cache read, and $3.2/Mt output; Novita model/homepage surfaces may show nearby but different values | Together pricing page lists DeepSeek V4 Pro at $2.10 input, $0.20 cached input, and $4.40 output | This is a good example of why live pricing checks matter. |
The pricing takeaway is fit-based, not absolute. Novita’s listed serverless prices are lower on several overlapping example rows at the time checked, which makes Novita attractive for cost-sensitive evaluation and production workloads. But do not stop at serverless token prices. Novita also has batch inference and dedicated Deployments, while Together has its own batch, dedicated inference, fine-tuning, and GPU options. If your workload is latency-sensitive, high-throughput, asynchronous, or better served by isolated compute, compare the deployment mode you will actually use.
Developer Workflow Examples
Use these snippets as workflow patterns. Confirm the current model ID, endpoint behavior, and account limits before using either provider in production.
Novita AI with the OpenAI Python SDK
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.novita.ai/openai",
api_key=os.environ["NOVITA_API_KEY"],
)
response = client.chat.completions.create(
model="openai/gpt-oss-20b",
messages=[
{"role": "system", "content": "You are a concise technical assistant."},
{"role": "user", "content": "Summarize the tradeoffs of serverless LLM inference."},
],
max_tokens=512,
)
print(response.choices[0].message.content)Check Novita model availability before a migration
curl --request GET \
--url https://api.novita.ai/openai/v1/models \
--header "Authorization: Bearer ${NOVITA_API_KEY}" \
--header "Content-Type: application/json"Run the same prompt on both providers
For an apples-to-apples test, keep the prompt, temperature, max output, and evaluation criteria stable. Then record:
- Model ID used on each provider.
- Input tokens, output tokens, and final cost.
- Context window and max output limit.
- Streaming behavior.
- Tool-call or structured-output behavior if your application depends on it.
- Latency under your real request shape.
- Failure modes and retry behavior.
When to Choose Novita AI
Choose Novita AI when you want to move from model testing to production without changing providers just because the workload gets more serious. Novita supports the common stages of an LLM workflow: real-time OpenAI-compatible API calls, asynchronous batch inference, dedicated endpoints, agent tooling, and GPU resources.
Novita is especially practical when:
- You want to compare several LLMs before committing to one provider or model.
- Unit economics matter and you need to inspect per-model input, output, and cache pricing.
- You have asynchronous LLM workloads that fit Novita’s LLM Batch API instead of real-time calls.
- You need dedicated endpoints for steadier traffic, isolated GPU resources, custom models, or LoRA adapters.
- Your application also needs image, audio, video, vision, agent sandbox, or GPU resources under the same platform direction.
- You want a provider that lets you start with API calls and still keep batch, dedicated, agent, and GPU paths open.
Price is still only one part of the decision. Validate output quality, latency, limits, batch behavior, deployment behavior, and feature compatibility for your workload before switching live traffic.
When to Choose Together AI
Choose Together AI when its model catalog, fine-tuning path, training infrastructure, or deployment setup is the better match for your team. Together’s docs and product pages emphasize running open-source models, fine-tuning models, launching GPU clusters, batch jobs, and dedicated model inference.
Together is especially practical when:
- You need serverless inference today but expect to use Together’s fine-tuning or training workflow later.
- You have offline workloads such as evaluations, classification, synthetic data generation, or summarization and prefer Together’s batch workflow.
- You want dedicated inference for predictable traffic, latency-sensitive applications, or high-throughput production workloads and Together’s deployment model fits your requirements.
- Your team already has infrastructure requirements that line up with Together’s GPU cluster or dedicated inference products.
The caution is simple: do not choose Together just because the workload involves batch jobs or dedicated inference. Novita supports those paths too. Choose Together when its specific model, fine-tuning, training, batch, or dedicated setup wins for your workload after testing.
Migration Checklist for Developers
Before moving from Together AI to Novita AI, from Novita AI to Together AI, or from OpenAI to either provider, complete these checks:
- Confirm the current model ID from the provider’s live model catalog or model listing endpoint.
- Confirm the base URL and endpoint family.
- Verify chat completions, completions, streaming, tools, structured outputs, and embeddings only if your app uses them.
- Compare context window, max output, and any modality limits.
- Re-run representative prompts and score the output by task type.
- Compare total cost with live input, output, cache, batch, and dedicated endpoint pricing where relevant.
- Test latency under realistic payload size and concurrency.
- Review account limits, rate limits, error shapes, retry behavior, and fallback plans.
- Keep a rollback path if production output quality or reliability changes.
Final Recommendation
Start with the workflow you actually need to run. If you need OpenAI-compatible model APIs, batch inference, dedicated endpoints, agent tooling, or GPU resources under one Novita account, Novita AI belongs in the first test set. If you also need Together’s fine-tuning path, training stack, model catalog, batch workflow, dedicated inference, or GPU cluster setup, test Together beside it.
The safest workflow is to test both providers with the same prompts, the same success criteria, and the deployment mode you plan to use. Choose based on the actual model, workload, price sheet, batch behavior, endpoint behavior, and operating constraints, not on a generic “best,” “fastest,” or “cheapest” claim.
FAQ
Is Novita AI OpenAI-compatible?
Yes. Novita’s LLM API documentation describes compatibility with the OpenAI API standard and shows examples using the official OpenAI SDK with base_url="https://api.novita.ai/openai".
Is Together AI OpenAI-compatible?
Yes. Together supports OpenAI-style compatibility for common inference workflows. Before production migration, verify the supported endpoint family, model ID, streaming behavior, tool support, structured-output behavior, and any unsupported OpenAI platform surfaces.
Is Novita AI cheaper than Together AI?
Novita’s public pricing page showed lower listed token prices on several overlapping example model rows checked on June 5, 2026. That does not prove Novita is always cheaper for every workload because model ID, context window, cache behavior, batch discounts, dedicated endpoints, latency, and output quality all affect real cost.
Which platform has more models?
Both platforms position themselves around broad model access. Novita’s homepage says developers can run 200+ models through a single API, while Together’s product surfaces also describe access to 200+ models. For production decisions, use each provider’s live model catalog rather than comparing only headline model counts.
Should I migrate from Together AI to Novita AI?
Consider testing Novita AI if you want OpenAI-compatible model APIs with room to keep batch inference, dedicated endpoints, agent tooling, and GPU resources in the same workflow. Do not migrate only because a pricing row looks lower. First verify the exact model ID, context window, quality, latency, streaming behavior, batch behavior, endpoint behavior, tool support, and total cost for your workload.
Should I migrate from Novita AI to Together AI?
Consider Together AI if its model catalog, fine-tuning workflow, training stack, batch workflow, dedicated inference, or GPU cluster options fit your workload better after testing. Do not move away from Novita just because the application needs batch inference or dedicated endpoints; Novita supports both. Switch only when Together performs better for the exact model, deployment mode, cost profile, and reliability target you care about.
Can I use the same OpenAI SDK code for both?
For basic chat completions, the migration pattern is similar: change the base URL, set the provider API key, and use a provider-supported model ID. For production applications, separately verify streaming, tools, structured outputs, embeddings, model listing, and any unsupported OpenAI platform features.
