Key Insight
The right provider depends on how your RAG workload behaves. If you're still mapping the wider market, start with our comparison of the best LLM inference APIs in 2026, then review OneInfer's Model APIs and Dedicated Deployments.
Disclosure: We build OneInfer, and we rank it first. We explain why and identify where another provider may be the better choice so you can judge the reasoning against your own workload.
Why RAG flips the usual speed-versus-cost math
RAG requests do not look like ordinary chat requests. They are input-heavy, repetitive, and latency-sensitive, which changes the metrics and pricing details that matter.
- 1Input tokens dominate the bill. Every query sends your instructions plus retrieved chunks—often 3,000 to 10,000 input tokens—to generate only a few hundred output tokens. Input and cached-input prices usually matter more than the headline output rate.
- 2Time to first token defines fast. Users judge an answer by when it starts streaming. Long prompts stretch the prefill stage, so prefill throughput matters more than a provider's generation tokens-per-second headline.
- 3Prompts repeat themselves. System instructions and popular documents recur across requests. Prompt caching can substantially discount matching prefixes, making prompt structure a major RAG cost lever.
The useful question is not simply who is fastest or cheapest. It is which endpoint handles input-heavy, repetitive, latency-sensitive traffic best for your model, region, context length, and traffic pattern.
GPU endpoints for RAG: quick comparison
| Provider | Best for in a RAG stack | Speed profile | Main cost lever |
|---|---|---|---|
| OneInfer | Routing and optimization handled for you | Sub-500ms target, custom kernels | Per-request routing and kernel throughput |
| Groq | Fast time to first token | Custom LPU silicon | Prompt caching |
| Fireworks AI | All-round balance on open models | Disaggregated prefill and decode | Cached-input and batch discounts |
| Together AI | A broad RAG toolkit on one bill | Competitive, model-dependent | Embeddings, reranking, and batch |
| DeepInfra | Aggressive serverless pricing | Model and region dependent | Low per-token rates |
| Baseten | Dedicated endpoints for custom models | Workload-tunable | Per-GPU pricing at steady traffic |
| OpenRouter | Comparing routes without provider lock-in | Depends on the selected route | Price- or latency-aware routing |
Capabilities, model availability, limits, and prices change frequently. Benchmark the exact model, region, prompt length, and concurrency level you plan to use before committing.
1. OneInfer
Best for teams that want the speed-cost tradeoff managed automatically instead of hand-tuned. Most RAG systems send every query to the same model, even though a simple policy lookup does not need the model used for multi-document synthesis.
OneInfer routes requests to the model, provider, and runtime suited to the workload. Lean models can handle straightforward retrieval-and-answer requests, while stronger models handle complex synthesis. When latency or availability changes, routing can shift traffic. For dominant workloads, Kernel Forge creates custom Triton and CUDA kernels for hot paths, including repeated long-prompt prefill work.
In OneInfer's internal benchmarks, kernel optimization has produced 2.3x to 15x throughput gains and 20% to 60% lower infrastructure costs. Treat these figures as directional: results depend on the model, hardware, batch shape, context length, and workload. One API also covers text and vision, which helps teams building RAG over PDFs, scans, tables, and screenshots.
Keep in mind: OneInfer is newer than most providers in this comparison. Teams trade a smaller community archive for more direct access to the engineering team.
2. Groq
Best for RAG products where the answer must start streaming quickly. Groq serves supported models on custom LPU hardware and is known for low time to first token. That can fit the prefill-heavy shape of RAG traffic. Prompt caching can further help repetitive prompts, but availability and terms depend on the selected model.
Keep in mind: The model catalog and retrieval tooling are more limited than broad multi-provider platforms, so confirm that your generator is supported and plan to operate embeddings or retrieval elsewhere if needed.
3. Fireworks AI
Best for a strong speed-cost balance on open models. Fireworks AI separates prefill and decode scaling and offers caching and batch options that suit RAG. Its embeddings and reranking support can also keep more of the retrieval pipeline on one platform.
Keep in mind: Pricing and caching discounts vary by model. Model your costs on the exact generator and traffic shape you will serve instead of a platform-wide headline rate.
4. Together AI
Best for running a broad RAG pipeline through one provider. Together AI combines embeddings, reranking, a wide selection of open generation models, fine-tuning, and batch processing. For teams that value breadth and one vendor relationship, it is a complete option.
Keep in mind: A specialist may serve a particular model faster or more cheaply. Together's central advantage is breadth across the workflow.
5. DeepInfra
Best for cost-conscious workloads with some latency flexibility. DeepInfra offers a wide catalog of open models with aggressive per-token pricing. It is a useful price baseline for internal tools, asynchronous pipelines, and RAG products that can tolerate some variation in response time.
Keep in mind: The lowest list price is not always the lowest cost per successful answer. Test latency, reliability, and answer quality using your own prompts and retrieved context.
6. Baseten
Best for dedicated endpoints serving custom embedders, rerankers, or fine-tuned generators. Baseten packages custom models as autoscaling dedicated endpoints with an operations layer. When traffic becomes predictable, dedicated per-GPU capacity can beat serverless per-token pricing.
Keep in mind: Model packaging and optimization require engineering work, and dedicated capacity makes economic sense only after utilization is stable enough to forecast.
7. OpenRouter
Best for comparing providers on live traffic before committing. OpenRouter is a gateway rather than a GPU host. Its API routes requests across multiple providers and can prioritize routes by price or latency, which makes it useful for testing alternatives without repeatedly rewriting an integration.
Keep in mind: A gateway adds another layer to the request path. Routing on price alone may also trade away the latency or reliability users notice, so define route priorities around service-level goals.
Five RAG optimization levers that can matter more than the provider
- 1Cache the stable prefix. Put system instructions and stable context before variable user content so providers can match repeated leading tokens.
- 2Cap the context budget. Retrieve broadly, rerank, and send only the most useful chunks. Extra context raises input cost and can dilute answer quality.
- 3Route by query complexity. Smaller models can answer straightforward lookups at a fraction of the cost of frontier models; reserve stronger models for complex synthesis.
- 4Stream and measure time to first token. Optimize the latency users perceive, not only total generation time or tokens per second.
- 5Batch offline work. Embedding backfills and evaluation runs rarely need real-time service tiers, so use batch processing where available.
How to choose a GPU endpoint for your RAG system
Start with serverless endpoints while traffic is intermittent and uncertain. Benchmark at realistic prompt lengths and concurrency, track time to first token and end-to-end latency, and calculate cached and uncached input costs separately. Consider dedicated capacity once traffic is steady enough to keep GPUs productively utilized.
Choose Groq when immediate streaming on a supported model is the priority; Fireworks AI for a strong balance on open models; Together AI for a broad retrieval and generation toolkit; DeepInfra for aggressive list pricing; Baseten for dedicated custom-model serving; OpenRouter for route comparison; and OneInfer when you want multi-provider routing, multimodal support, kernel optimization, and dedicated deployment options behind one API.
Build a faster, more cost-efficient RAG stack
Use one API for text and vision inference, then match each request to the model and runtime suited to its latency, quality, and cost requirements.
Frequently Asked Questions
+What is the biggest cost driver in a RAG system?
Input tokens are often the biggest inference cost because each request resends instructions and retrieved context to generate a shorter answer. Reducing irrelevant chunks and caching repeated prefixes can have more impact than comparing output-token prices alone.
+Which GPU endpoint is fastest for RAG?
Groq is known for very low raw time to first token on supported models. For the complete RAG request, long-prompt prefill speed, caching, region, queueing, routing, and model choice also affect the latency users experience. Benchmark providers with your actual context length and concurrency.
+How much can prompt caching save on RAG workloads?
Savings depend on the provider, model, cache-hit rate, and the share of repeated input. RAG workloads can benefit significantly because instructions and popular context recur, especially when the stable prompt prefix comes before variable content.
+Should I use serverless endpoints or dedicated GPUs for RAG?
Start with serverless endpoints when traffic is spiky or uncertain because you avoid capacity planning. Evaluate dedicated GPUs when latency-critical traffic becomes steady and predictable enough that per-GPU pricing and reserved capacity improve unit economics or reliability.
+Can RAG work over PDFs, scans, and images?
Yes. Multimodal RAG combines document retrieval with vision-capable models so a system can interpret page layouts, tables, stamps, scans, and screenshots. A multimodal inference API can reduce the number of separate vendor integrations needed.