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Fireworks AI routes serverless to US regions and adds GPU-seconds on dedicated. Telnyx hosts frontier models in-region on owned GPUs.
If your inference bill grows faster than your usage, the provider is the constraint, and no amount of prompt tuning fixes it. Here is where teams go when they outgrow Fireworks AI.
Fireworks AI built its name on FireAttention and FireOptimizer, proprietary engines that squeeze more throughput from a model. The infrastructure under those engines is rented. Fireworks runs across eight major clouds behind a Virtual Cloud abstraction, which means every token you serve carries a cloud-provider margin Fireworks cannot remove, and serverless ships from US regions. Non-US regions exist, but only as dedicated, on-demand, or BYOC deployments that bill by GPU-second or GPU-hour instead of the serverless per-token rate.
What actually decides cost, latency, and data residency at production scale is who owns the infrastructure under the API, whether inference stays in your users' region without a premium tier, and whether voice, transcription, and speech run in the same place as the call. We ranked these five providers on exactly that.
Here are the five best Fireworks AI alternatives for inference, starting with the one we built.
Fireworks AI alternatives
| Provider | Best for | Deployment model |
|---|
Related articles
| Pricing model |
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| Telnyx | In-region inference on owned infrastructure, with a path into voice | Serverless on Telnyx-owned GPUs, in-region across US, EU, APAC | Per-token pricing, 1M free tokens per month, no GPU rental or compute surcharges |
| Together AI | Research-driven serverless across a 200+ model catalog | Serverless, dedicated endpoints, self-service GPU clusters | Per-token, plus GPU-hour for clusters and batch up to roughly half the cost |
| Baseten | Production multi-model orchestration | Cloud, self-hosted, or hybrid via Truss containers | Pay-as-you-go on GPU and tokens, Pro and Enterprise via sales |
| Modal | Code-first GPU deployment and elastic scale | Python SDK deploy, region selection with a us-east-1 control plane, scale to zero | Per-GPU-second across the NVIDIA fleet, plan tiers, 1.5 to 1.75x regional multiplier |
| DeepInfra | Budget-priced serverless across a broad model catalog | Serverless plus dedicated A100 through B300 GPUs | Per-token on serverless, per-GPU-hour on dedicated, $20 minimum to start |
Telnyx Inference is serverless, pay-per-token access to frontier open-weight models on GPUs Telnyx owns and operates. Four curated models cover real-time and voice, reasoning and agents, cost-efficient intelligence, and balanced workloads, and the API is OpenAI-compatible.
Most providers rent GPUs from a cloud vendor, adding a margin to every token. Because Telnyx runs an owned GPU network instead, throughput stays high and price follows the infrastructure. The short catalog is deliberate.
Inference stays in your users' region because the GPUs are physically there across the US, Europe, and APAC, which makes data residency a property of the architecture rather than a premium tier. Transcription, speech, and voice run on that same infrastructure.
Migration is usually a base URL change and new credentials, done in an afternoon. Pricing is per-token only with no GPU rental or compute surcharges, and 1M free tokens every month keeps cost predictable.
If you are leaving Fireworks AI because the model catalog feels narrow or because FireOptimizer's lock-in worries you, Together AI takes the opposite stance. Its serverless catalog runs past 200 open-source models, including DeepSeek R1, Llama 3.3 70B, Qwen3-Coder, and gpt-oss, with day-zero support and self-service GPU clusters on H100, H200, and B200 hardware for teams that want to train and serve on their own fleet.
The catch is the same one Fireworks has on regions. Together's serverless runs from US data centers, with no advertised in-region serverless for the EU or APAC. Together has also moved to FP4 quantization on most flagship models, which trades precision for throughput. Teams that need higher numerical precision and inference that stays in-region by default, on infrastructure the provider owns end to end, tend to land on Telnyx.
The Fireworks pain that points teams at Baseten is custom deployment. Fireworks gives you BYOC and on-demand dedicated, but the path is built around its own serving stack. Baseten is the inverse: bring any model and any framework (vLLM, SGLang, TensorRT-LLM, PyTorch) in a Truss container and deploy it on Baseten Cloud, in your own VPC, or in a hybrid pattern. Chains, its multi-model orchestration framework, lets you wire RAG, chunked transcription, or AI phone calling as pure Python without YAML glue.
The tradeoff is what Baseten does not own. Capacity sits on rented cloud GPUs (AWS strategic collab, Vultr, Nvidia investment), so the structural cost story looks similar to Fireworks once you peel the abstraction back. Baseten also does not publish head-to-head benchmarks, and its regional footprint is US-concentrated. Teams that want a managed serverless endpoint, owned GPUs, and inference that stays in your users' region by default tend to land on Telnyx.
modal deploy, region selection (control plane in us-east-1), scale to zeroTeams leaving Fireworks AI because the abstraction feels too opaque often look at Modal. Where Fireworks hides the serving stack behind FireOptimizer and a managed endpoint, Modal hands you the infrastructure in Python. You decorate functions with @app.function or @modal.web_server, define the GPU and image yourself, and modal deploy runs it on Modal's fleet. GPU memory snapshots cut LLM cold starts from roughly 70 seconds to about 12, and scale-to-zero keeps idle cost down.
The tradeoff is that Modal is not a drop-in inference API. There is no hosted, OpenAI-compatible chat-completions endpoint and no curated model catalog. To serve a model you write a vLLM wrapper, manage the container, and operate it yourself. Region selection exists on the Team and Enterprise plans, but every input and output still routes through Modal's us-east-1 control plane, so in-region residency is constrained by the control plane rather than function placement. Teams that want a managed serverless endpoint and true in-region inference tend to land on Telnyx.
If the reason to leave Fireworks AI is raw per-token price on commodity models, DeepInfra is built around exactly that. Its serverless catalog spans 50+ open models (DeepSeek, Qwen, Llama, Mistral, plus Voxtral for speech), and dedicated GPU pricing starts at $0.89 per A100 hour and tops out at $4.20 per B300 hour. Recent $107M Series B funding (May 2026) is the credibility signal.
The pattern that repeats every time you compare Fireworks alternatives shows up here too. DeepInfra does not publish regional inference deployments, does not document GDPR posture, and offers no free tier (Tier 1 starts at $20). Infrastructure ownership is not stated, so the structural cost question is unresolved. Teams that want frontier models on owned GPUs, with inference that stays in-region by default and 1M free tokens every month, tend to land on Telnyx.
We scored every provider on the four things that decide inference cost and latency at production scale. Model count was not one of them.
Telnyx is the only provider here that clears all four, which is why it sits at the top of the list.
Inference pricing converges. What does not converge is who owns the stack underneath it. The providers that rent their GPUs carry a margin they cannot remove, and the ones that route everything through US regions add distance they cannot optimize away.
Telnyx hosts frontier models on its own GPUs, in your users' region by default, on the same infrastructure that runs its voice AI agents and messaging. That is why the savings are structural and the latency is predictable, and why a team can start on inference and add voice agents later without a second vendor.
Start with 1M free tokens every month and see what inference looks like on infrastructure built for it.