Fine-tuning changes the hosting decision. A model that worked in a notebook may need a very different production setup once it serves Indian-language queries, processes sensitive financial records, or supports a customer-facing voice workflow. The right platform must balance latency, GPU availability, deployment control, privacy, observability, and unit economics—not just advertise the latest accelerator.
This guide compares the main options available to builders in 2026, including managed inference APIs, serverless GPU platforms, raw GPU marketplaces, and hyperscaler services. It also explains how to choose between an adapter and a full model, estimate capacity, and avoid common production mistakes.
Start with the model and traffic profile
Before comparing vendors, document four facts:
- Model size and runtime: A quantised 7B model can often run on an L4 or A10-class GPU. Larger 30B–70B models may require multiple GPUs, tensor parallelism, or aggressive quantisation.
- Fine-tuning method: LoRA and QLoRA adapters are small and portable. Full fine-tunes produce larger checkpoints and may limit serverless options.
- Traffic shape: Bursty traffic favours scale-to-zero or token-based pricing. Predictable, continuous traffic usually makes a reserved or dedicated GPU cheaper.
- Latency target: Measure time to first token and total response time separately. A chatbot, batch extraction job, and voice agent need different service-level objectives.
If you are still deciding how to prepare the checkpoint, review these best practices for fine-tuning LLMs on custom data. Hosting cannot compensate for poor evaluation data, unstable prompts, or an adapter that overfits its training set.
What to evaluate in a hosting platform
Runtime and architecture support
Check whether the service supports your exact base model, tokenizer, quantisation format, context length, and inference engine. vLLM is a strong default for open-weight language models because it supports continuous batching and paged attention, but specialised architectures or custom CUDA code may require a container-based deployment.
Ask whether you can bring a SafeTensors checkpoint, merge a LoRA adapter at build time, or load adapters dynamically. Dynamic adapter serving is valuable when one base model powers multiple products or customers; it avoids keeping several near-identical models resident in GPU memory.
Performance and scaling
Request benchmarks using your prompts, output lengths, concurrency, and context window. A headline tokens-per-second figure is not enough. Track:
- p50 and p95 time to first token;
- inter-token latency;
- requests or tokens per second at target concurrency;
- cold-start duration;
- GPU utilisation and memory headroom;
- failure and retry rates.
For low-volume applications, scale-to-zero reduces waste but introduces cold starts. For interactive products, keep a warm minimum or use a platform with fast weight loading and container reuse.
Privacy, residency, and governance
Indian teams handling KYC, health, education, or financial data should map the full data path: prompts, outputs, logs, traces, backups, model artefacts, and support access. A Mumbai region alone does not prove compliance with the Digital Personal Data Protection Act or your customer contracts.
Prefer providers that offer private networking, encryption, configurable retention, audit logs, customer-managed keys where necessary, and a clear policy on whether prompts are used for training. Redact personal data before inference and keep model weights in a controlled registry.
Best platforms to host custom fine-tuned models
Together AI: managed serving for supported open models
Together AI is a practical choice when your checkpoint uses a well-supported architecture and you want a managed route from fine-tuning to inference. Its token-based model can work well for variable or growing traffic because you avoid paying for an idle GPU.
Choose it when you value a simple API, rapid deployment, and strong performance on mainstream open models. Confirm current support for your base model, adapter format, quantisation, maximum context, and custom system requirements before committing. It is less suitable when you need unusual preprocessing, bespoke CUDA kernels, or strict control over the serving image.
Fireworks AI: efficient inference and adapter-heavy deployments
Fireworks is attractive for products that need fast generation and several task-specific variants. Its support for serving LoRA-style adapters can reduce duplicated GPU memory when multiple fine-tunes share a base model.
This pattern is especially useful for multilingual support, domain-specific classification prompts, or separate enterprise tenants. Validate adapter loading behaviour, concurrency limits, rate limits, and pricing under your expected mix of base-model and adapter requests. Benchmark with production-like prompts rather than relying only on public speed claims.
Hugging Face Inference Endpoints: portability and control
Hugging Face Inference Endpoints offer a straightforward path from a private model repository to a managed endpoint. They are a strong fit when your model depends on the Transformers ecosystem or needs a custom inference handler.
You typically get more control than with a narrow model API, including deployment configuration and private repositories, while avoiding the operational burden of managing Kubernetes and GPU drivers. Review available cloud regions, networking, autoscaling, idle-timeout settings, and the exact image or engine used for your model.
Modal and Baseten: Python-first production workflows
Modal and Baseten suit teams that want to define preprocessing, inference, post-processing, and scaling in code rather than assemble a large cloud stack. They are useful for models with custom business logic, retrieval steps, document parsing, or evaluation hooks around the generation call.
Modal is well suited to bursty workloads and serverless GPU execution, while Baseten focuses on packaging and operating model services through a structured deployment workflow. In both cases, test image build time, weight download speed, secrets handling, observability, and rollback behaviour—not just the first successful deployment.
RunPod and Vast.ai: flexible GPU infrastructure
RunPod is a good middle ground for builders who want managed GPU instances or serverless workers with Docker-level control. Vast.ai can be economical for experiments and batch workloads, but marketplace capacity and hardware reliability vary by host.
These options make sense when you already understand vLLM, TGI, Docker, health checks, queues, and GPU scheduling. They can produce excellent economics, but you own more of the reliability surface: model downloads, autoscaling, upgrades, security patches, alerting, and failover.
AWS SageMaker, Google Cloud, and Azure: enterprise governance
Hyperscalers remain the safest choice when procurement, private networking, identity controls, and regional governance matter more than the lowest raw inference price. AWS offers Mumbai and Hyderabad regions; Google Cloud and Azure also provide India-based infrastructure, though GPU type and availability must be checked at deployment time.
Use managed endpoints, custom containers, or Kubernetes depending on how much control your team needs. These services integrate well with existing data platforms, but the total bill includes endpoint uptime, storage, networking, logging, and managed-service overhead. For a regulated fintech or healthcare deployment, that premium may be justified.
Cost comparison and capacity planning
| Hosting model | Pricing pattern | Best fit | Main trade-off |
|---|---|---|---|
| Managed token API | Per input/output token | Variable traffic and fast launch | Limited runtime control |
| Serverless GPU | Per execution time | Bursty custom logic | Cold starts and platform limits |
| Dedicated endpoint | Per GPU hour | Steady production traffic | Idle capacity |
| Raw GPU or marketplace | Per GPU hour | Maximum control and batch jobs | You operate reliability |
| Hyperscaler managed service | Endpoint, GPU, and cloud charges | Enterprise governance | Higher platform complexity |
Build a simple monthly model using peak concurrency, average output tokens, requests per day, GPU memory, and target uptime. Compare the cost of warm capacity with the cost of cold starts and queueing. Include egress, observability, storage, support, and engineering time; the cheapest GPU rate is rarely the cheapest production system.
Practical optimisation checklist
- Quantise with AWQ, GPTQ, bitsandbytes, or an equivalent format after validating quality.
- Use continuous batching and prefix caching where the workload supports them.
- Cap context and output lengths with product-level limits.
- Cache deterministic or repetitive requests, but do not cache sensitive responses carelessly.
- Keep model weights in the same region or network as the serving endpoint.
- Separate offline batch inference from interactive traffic so one queue cannot starve the other.
- Run load tests with Indian scripts, mixed-language prompts, long documents, and realistic concurrency.
- Maintain a fallback model or provider for outages and GPU scarcity.
Teams building voice products should pay particular attention to streaming and first-token latency; the infrastructure lessons also apply to voice agents in customer service and the trade-offs covered in this voice agent versus IVR guide. For sensitive onboarding flows, hosting decisions should be reviewed alongside fintech customer onboarding with voice agents.
A sensible selection process
1. Package the model in a reproducible container or private registry.
2. Test two managed providers and one self-managed GPU option.
3. Use the same prompts, context lengths, concurrency, and quality checks across all three.
4. Measure p95 latency, availability, cost per successful request, and operational effort.
5. Run a privacy and data-residency review before production.
6. Start with the least complex option that meets your requirements, then retain an exportable checkpoint and deployment recipe.
For most Indian startups, a managed token endpoint is the fastest starting point for a standard open model. Move to serverless GPU hosting when custom code matters, dedicated instances when traffic is steady, and a hyperscaler when governance and private networking are non-negotiable. The best platform is the one that meets your latency and compliance targets while keeping the model portable enough to change providers later.